code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(SCREAMING_SNAKE_CASE__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(SCREAMING_SNAKE_CASE__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 20 |
import os
import numpy
import onnx
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
lowercase : int = a.name
lowercase : Any = b.name
lowercase : Optional[Any] = """"""
lowercase : Dict = """"""
lowercase : int = a == b
lowercase : int = name_a
lowercase : List[str] = name_b
return res
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_graph_replace_input_with(node_proto.attribute[1].g , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
for n in graph_proto.node:
_node_replace_input_with(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
lowercase : Any = list(model.graph.initializer )
lowercase : Dict = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase : Union[str, Any] = inits[i].name
lowercase : Dict = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[str]:
lowercase : Union[str, Any] = os.path.dirname(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.basename(SCREAMING_SNAKE_CASE__ )
lowercase : str = onnx.load(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase : List[str] = list(model.graph.initializer )
lowercase : Tuple = set()
lowercase : int = {}
lowercase : Optional[Any] = []
lowercase : Dict = 0
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(SCREAMING_SNAKE_CASE__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(SCREAMING_SNAKE_CASE__ )
dup_set.add(SCREAMING_SNAKE_CASE__ )
lowercase : int = inits[j].data_type
lowercase : Optional[int] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , SCREAMING_SNAKE_CASE__ )
total_reduced_size += mem_size
lowercase : Tuple = inits[i].name
lowercase : int = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(SCREAMING_SNAKE_CASE__ )
else:
lowercase : List[str] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1_024 / 1_024 / 1_024 , """GB""" )
lowercase : str = sorted(SCREAMING_SNAKE_CASE__ )
_remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = """optimized_""" + model_file_name
lowercase : Dict = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
onnx.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return new_model
| 20 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
},
}
UpperCAmelCase_ = {
'camembert-base': 5_1_2,
}
UpperCAmelCase_ = '▁'
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : str = VOCAB_FILES_NAMES
lowerCAmelCase_ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : Optional[int] = ["""input_ids""", """attention_mask"""]
lowerCAmelCase_ : Tuple = CamembertTokenizer
def __init__( self : Any , _UpperCAmelCase : str=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : int="<s>" , _UpperCAmelCase : Optional[int]="</s>" , _UpperCAmelCase : Optional[int]="</s>" , _UpperCAmelCase : Any="<s>" , _UpperCAmelCase : List[str]="<unk>" , _UpperCAmelCase : Dict="<pad>" , _UpperCAmelCase : str="<mask>" , _UpperCAmelCase : Any=["<s>NOTUSED", "</s>NOTUSED"] , **_UpperCAmelCase : Optional[int] , ):
"""simple docstring"""
UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , **_UpperCAmelCase , )
UpperCAmelCase__ = vocab_file
UpperCAmelCase__ = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ = [self.cls_token_id]
UpperCAmelCase__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
UpperCAmelCase__ = [self.sep_token_id]
UpperCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ = os.path.join(
_UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 61 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : set ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
UpperCAmelCase__ = 0
count += depth_first_search(SCREAMING_SNAKE_CASE__ , row + 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , row - 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , col + 1 , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , col - 1 , SCREAMING_SNAKE_CASE__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : int = {
'configuration_clip': [
'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPConfig',
'CLIPOnnxConfig',
'CLIPTextConfig',
'CLIPVisionConfig',
],
'processing_clip': ['CLIPProcessor'],
'tokenization_clip': ['CLIPTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ['CLIPTokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['CLIPFeatureExtractor']
A : Dict = ['CLIPImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPModel',
'CLIPPreTrainedModel',
'CLIPTextModel',
'CLIPTextModelWithProjection',
'CLIPVisionModel',
'CLIPVisionModelWithProjection',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFCLIPModel',
'TFCLIPPreTrainedModel',
'TFCLIPTextModel',
'TFCLIPVisionModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = [
'FlaxCLIPModel',
'FlaxCLIPPreTrainedModel',
'FlaxCLIPTextModel',
'FlaxCLIPTextPreTrainedModel',
'FlaxCLIPVisionModel',
'FlaxCLIPVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
A : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __A( a ):
snake_case_ = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''text''': Value('''string''' )} )
snake_case_ = Features({} )
snake_case_ = "text"
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text"} | 6 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: int = logging.get_logger(__name__)
_UpperCamelCase: Optional[Any] = {'vocab_file': 'sentencepiece.bpe.model'}
_UpperCamelCase: List[Any] = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
_UpperCamelCase: Union[str, Any] = {
'camembert-base': 5_1_2,
}
_UpperCamelCase: Any = '▁'
class a__ ( SCREAMING_SNAKE_CASE__ ):
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = ['input_ids', 'attention_mask']
def __init__( self : Optional[int], lowerCAmelCase : List[Any], lowerCAmelCase : int="<s>", lowerCAmelCase : Optional[int]="</s>", lowerCAmelCase : Union[str, Any]="</s>", lowerCAmelCase : Union[str, Any]="<s>", lowerCAmelCase : List[str]="<unk>", lowerCAmelCase : Union[str, Any]="<pad>", lowerCAmelCase : Dict="<mask>", lowerCAmelCase : Any=["<s>NOTUSED", "</s>NOTUSED"], lowerCAmelCase : Optional[Dict[str, Any]] = None, **lowerCAmelCase : int, ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
lowercase : Union[str, Any] = AddedToken(lowerCAmelCase, lstrip=lowerCAmelCase, rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase, lowerCAmelCase ) else mask_token
lowercase : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, unk_token=lowerCAmelCase, sep_token=lowerCAmelCase, cls_token=lowerCAmelCase, pad_token=lowerCAmelCase, mask_token=lowerCAmelCase, additional_special_tokens=lowerCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCAmelCase, )
lowercase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase ) )
lowercase : Union[str, Any] = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
lowercase : int = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
lowercase : Optional[int] = len(self.fairseq_tokens_to_ids )
lowercase : List[str] = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
lowercase : Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowercase ( self : Tuple, lowerCAmelCase : List[int], lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase : List[str] = [self.cls_token_id]
lowercase : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase ( self : List[Any], lowerCAmelCase : List[int], lowerCAmelCase : Optional[List[int]] = None, lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase, token_ids_a=lowerCAmelCase, already_has_special_tokens=lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase )) + [1]
return [1] + ([0] * len(lowerCAmelCase )) + [1, 1] + ([0] * len(lowerCAmelCase )) + [1]
def lowercase ( self : int, lowerCAmelCase : List[int], lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
lowercase : str = [self.sep_token_id]
lowercase : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowercase ( self : Optional[int] ) -> List[str]:
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def lowercase ( self : Optional[int] ) -> str:
lowercase : List[str] = {self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase ( self : List[str], lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(lowerCAmelCase, out_type=lowerCAmelCase )
def lowercase ( self : Any, lowerCAmelCase : Optional[Any] ) -> Optional[int]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(lowerCAmelCase ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase )
def lowercase ( self : Tuple, lowerCAmelCase : Dict ) -> List[str]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowercase ( self : Optional[int], lowerCAmelCase : Tuple ) -> List[str]:
lowercase : Tuple = []
lowercase : Optional[int] = ''
lowercase : List[str] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase ) + token
lowercase : Any = True
lowercase : Union[str, Any] = []
else:
current_sub_tokens.append(lowerCAmelCase )
lowercase : str = False
out_string += self.sp_model.decode(lowerCAmelCase )
return out_string.strip()
def __getstate__( self : int ) -> Optional[int]:
lowercase : List[Any] = self.__dict__.copy()
lowercase : Tuple = None
return state
def __setstate__( self : str, lowerCAmelCase : List[Any] ) -> Any:
lowercase : Optional[int] = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
lowercase : str = {}
lowercase : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self : List[Any], lowerCAmelCase : str, lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase : Dict = os.path.join(
lowerCAmelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase, 'wb' ) as fi:
lowercase : int = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (out_vocab_file,)
| 53 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase : List[Any] = np.inf
def set_batch_size(_UpperCAmelCase ) -> None:
nonlocal batch_size
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase : Any = min(_UpperCAmelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase : Dict = min(_UpperCAmelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ) and feature.dtype == "binary":
lowercase : int = min(_UpperCAmelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_UpperCAmelCase , _UpperCAmelCase )
return None if batch_size is np.inf else batch_size
class a__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Union[str, Any], lowerCAmelCase : NestedDataStructureLike[PathLike], lowerCAmelCase : Optional[NamedSplit] = None, lowerCAmelCase : Optional[Features] = None, lowerCAmelCase : str = None, lowerCAmelCase : bool = False, lowerCAmelCase : bool = False, lowerCAmelCase : Optional[int] = None, **lowerCAmelCase : int, ) -> List[Any]:
super().__init__(
lowerCAmelCase, split=lowerCAmelCase, features=lowerCAmelCase, cache_dir=lowerCAmelCase, keep_in_memory=lowerCAmelCase, streaming=lowerCAmelCase, num_proc=lowerCAmelCase, **lowerCAmelCase, )
lowercase : str = path_or_paths if isinstance(lowerCAmelCase, lowerCAmelCase ) else {self.split: path_or_paths}
lowercase : Tuple = _PACKAGED_DATASETS_MODULES['parquet'][1]
lowercase : Optional[int] = Parquet(
cache_dir=lowerCAmelCase, data_files=lowerCAmelCase, features=lowerCAmelCase, hash=lowerCAmelCase, **lowerCAmelCase, )
def lowercase ( self : Optional[int] ) -> Union[str, Any]:
# Build iterable dataset
if self.streaming:
lowercase : Union[str, Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowercase : Tuple = None
lowercase : Union[str, Any] = None
lowercase : List[Any] = None
lowercase : int = None
self.builder.download_and_prepare(
download_config=lowerCAmelCase, download_mode=lowerCAmelCase, verification_mode=lowerCAmelCase, base_path=lowerCAmelCase, num_proc=self.num_proc, )
lowercase : Any = self.builder.as_dataset(
split=self.split, verification_mode=lowerCAmelCase, in_memory=self.keep_in_memory )
return dataset
class a__ :
def __init__( self : Dict, lowerCAmelCase : Dataset, lowerCAmelCase : Union[PathLike, BinaryIO], lowerCAmelCase : Optional[int] = None, **lowerCAmelCase : Optional[Any], ) -> Optional[Any]:
lowercase : List[Any] = dataset
lowercase : int = path_or_buf
lowercase : Optional[Any] = batch_size or get_writer_batch_size(dataset.features )
lowercase : Optional[Any] = parquet_writer_kwargs
def lowercase ( self : Union[str, Any] ) -> int:
lowercase : Union[str, Any] = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf, (str, bytes, os.PathLike) ):
with open(self.path_or_buf, 'wb+' ) as buffer:
lowercase : int = self._write(file_obj=lowerCAmelCase, batch_size=lowerCAmelCase, **self.parquet_writer_kwargs )
else:
lowercase : List[Any] = self._write(file_obj=self.path_or_buf, batch_size=lowerCAmelCase, **self.parquet_writer_kwargs )
return written
def lowercase ( self : int, lowerCAmelCase : BinaryIO, lowerCAmelCase : int, **lowerCAmelCase : Union[str, Any] ) -> int:
lowercase : Optional[Any] = 0
lowercase : int = parquet_writer_kwargs.pop('path_or_buf', lowerCAmelCase )
lowercase : List[str] = self.dataset.features.arrow_schema
lowercase : int = pq.ParquetWriter(lowerCAmelCase, schema=lowerCAmelCase, **lowerCAmelCase )
for offset in logging.tqdm(
range(0, len(self.dataset ), lowerCAmelCase ), unit='ba', disable=not logging.is_progress_bar_enabled(), desc='Creating parquet from Arrow format', ):
lowercase : Tuple = query_table(
table=self.dataset._data, key=slice(lowerCAmelCase, offset + batch_size ), indices=self.dataset._indices if self.dataset._indices is not None else None, )
writer.write_table(lowerCAmelCase )
written += batch.nbytes
writer.close()
return written
| 53 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _a ( lowerCamelCase: str ) -> str:
'''simple docstring'''
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip('''/''' )
__A = args.target_model_path
print(F"""Load fine-pruned model from {model_name_or_path}""" )
__A = torch.load(os.path.join(lowerCamelCase , '''pytorch_model.bin''' ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F"""Copied layer {name}""" )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F"""Copied layer {name}""" )
elif "bias" in name:
__A = tensor
print(F"""Copied layer {name}""" )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=lowerCamelCase , threshold=lowerCamelCase )
__A = tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F"""{prefix_}mask_scores"""]
__A = TopKBinarizer.apply(lowerCamelCase , lowerCamelCase )
__A = tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F"""{prefix_}mask_scores"""]
__A = ThresholdBinarizer.apply(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__A = tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F"""{prefix_}mask_scores"""]
__A , __A = -0.1, 1.1
__A = torch.sigmoid(lowerCamelCase )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F"""Pruned layer {name}""" )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(lowerCamelCase ) , F"""bertarized_{os.path.basename(lowerCamelCase )}""" )
if not os.path.isdir(lowerCamelCase ):
shutil.copytree(lowerCamelCase , lowerCamelCase )
print(F"""\nCreated folder {target_model_path}""" )
torch.save(lowerCamelCase , os.path.join(lowerCamelCase , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
snake_case__ : Optional[int] = parser.parse_args()
main(args)
| 117 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
snake_case__ : Optional[int] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
snake_case__ : Optional[int] = parser.parse_args()
if args.model_type == "bert":
snake_case__ : Dict = BertForMaskedLM.from_pretrained(args.model_name)
snake_case__ : Union[str, Any] = 'bert'
else:
raise ValueError('args.model_type should be "bert".')
snake_case__ : Optional[int] = model.state_dict()
snake_case__ : List[Any] = {}
for w in ["word_embeddings", "position_embeddings"]:
snake_case__ : Tuple = state_dict[f'{prefix}.embeddings.{w}.weight']
for w in ["weight", "bias"]:
snake_case__ : Optional[Any] = state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
snake_case__ : int = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
snake_case__ : Union[str, Any] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
]
snake_case__ : Dict = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
]
snake_case__ : Optional[int] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
]
snake_case__ : Optional[Any] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
]
snake_case__ : List[str] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
]
std_idx += 1
snake_case__ : Optional[int] = state_dict['cls.predictions.decoder.weight']
snake_case__ : str = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
snake_case__ : int = state_dict[f'cls.predictions.transform.dense.{w}']
snake_case__ : Optional[int] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
print(f'N layers selected for distillation: {std_idx}')
print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 117 | 1 |
'''simple docstring'''
from math import factorial
def _a( UpperCamelCase__ : int = 1_0_0 ):
'''simple docstring'''
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 354 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class __SCREAMING_SNAKE_CASE :
snake_case_ = PegasusConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : int , __lowercase : Optional[Any] , __lowercase : int=13 , __lowercase : List[str]=7 , __lowercase : Dict=True , __lowercase : Tuple=False , __lowercase : Optional[Any]=99 , __lowercase : str=32 , __lowercase : List[str]=2 , __lowercase : str=4 , __lowercase : Optional[int]=37 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=40 , __lowercase : str=2 , __lowercase : List[Any]=1 , __lowercase : Optional[Any]=0 , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =parent
SCREAMING_SNAKE_CASE__ : List[Any] =batch_size
SCREAMING_SNAKE_CASE__ : Optional[int] =seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] =is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_labels
SCREAMING_SNAKE_CASE__ : str =vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : List[str] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] =intermediate_size
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
SCREAMING_SNAKE_CASE__ : Any =pad_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] =bos_token_id
def __magic_name__ ( self : Any ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE__ : Any =tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE__ : Dict =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] =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 , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =prepare_pegasus_inputs_dict(__lowercase , __lowercase , __lowercase )
return config, inputs_dict
def __magic_name__ ( self : Optional[int] , __lowercase : List[str] , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =TFPegasusModel(config=__lowercase ).get_decoder()
SCREAMING_SNAKE_CASE__ : List[str] =inputs_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : Tuple =input_ids[:1, :]
SCREAMING_SNAKE_CASE__ : Tuple =inputs_dict['''attention_mask'''][:1, :]
SCREAMING_SNAKE_CASE__ : Tuple =inputs_dict['''head_mask''']
SCREAMING_SNAKE_CASE__ : List[str] =1
# first forward pass
SCREAMING_SNAKE_CASE__ : Any =model(__lowercase , attention_mask=__lowercase , head_mask=__lowercase , use_cache=__lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : str =ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE__ : int =model(__lowercase , attention_mask=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Any =model(__lowercase , attention_mask=__lowercase , past_key_values=__lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE__ : Any =output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE__ : List[str] =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__lowercase , __lowercase , rtol=1e-3 )
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[Any]=None, UpperCamelCase__ : Optional[Any]=None, UpperCamelCase__ : Union[str, Any]=None, UpperCamelCase__ : Any=None, UpperCamelCase__ : Optional[Any]=None, ):
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : str =tf.cast(tf.math.not_equal(UpperCamelCase__, config.pad_token_id ), tf.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : Any =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ), tf.inta ),
], axis=-1, )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : int =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
snake_case_ = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
snake_case_ = (
{
"""conversational""": TFPegasusForConditionalGeneration,
"""feature-extraction""": TFPegasusModel,
"""summarization""": TFPegasusForConditionalGeneration,
"""text2text-generation""": TFPegasusForConditionalGeneration,
"""translation""": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
snake_case_ = True
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE__ : List[Any] =TFPegasusModelTester(self )
SCREAMING_SNAKE_CASE__ : Dict =ConfigTester(self , config_class=__lowercase )
def __magic_name__ ( self : int ) -> Any:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__lowercase )
@require_sentencepiece
@require_tokenizers
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = [
""" 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!\" """,
]
snake_case_ = [
"""California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
""" reduce the risk of wildfires.""",
"""N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
snake_case_ = """google/pegasus-xsum"""
@cached_property
def __magic_name__ ( self : Optional[int] ) -> Tuple:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __magic_name__ ( self : List[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __magic_name__ ( self : List[str] , **__lowercase : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.translate_src_text(**__lowercase )
assert self.expected_text == generated_words
def __magic_name__ ( self : Optional[Any] , **__lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.tokenizer(self.src_text , **__lowercase , padding=__lowercase , return_tensors='''tf''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__lowercase )
return generated_words
@slow
def __magic_name__ ( self : Optional[Any] ) -> Optional[int]:
self._assert_generated_batch_equal_expected() | 222 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class snake_case :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict=13 , lowerCAmelCase : str=7 , lowerCAmelCase : Dict=True , lowerCAmelCase : List[str]=True , lowerCAmelCase : str=True , lowerCAmelCase : int=True , lowerCAmelCase : Tuple=99 , lowerCAmelCase : Dict=64 , lowerCAmelCase : Tuple=5 , lowerCAmelCase : Dict=4 , lowerCAmelCase : int=37 , lowerCAmelCase : List[Any]="gelu" , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : List[Any]=512 , lowerCAmelCase : Union[str, Any]=16 , lowerCAmelCase : Optional[Any]=2 , lowerCAmelCase : str=0.02 , lowerCAmelCase : Dict=3 , lowerCAmelCase : int=4 , lowerCAmelCase : List[Any]=None , ) -> Dict:
"""simple docstring"""
_snake_case : Optional[int] = parent
_snake_case : Dict = batch_size
_snake_case : Any = seq_length
_snake_case : Union[str, Any] = is_training
_snake_case : Union[str, Any] = use_input_mask
_snake_case : str = use_token_type_ids
_snake_case : Dict = use_labels
_snake_case : Any = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Dict = num_hidden_layers
_snake_case : Union[str, Any] = num_attention_heads
_snake_case : Optional[Any] = intermediate_size
_snake_case : Union[str, Any] = hidden_act
_snake_case : int = hidden_dropout_prob
_snake_case : Optional[int] = attention_probs_dropout_prob
_snake_case : int = max_position_embeddings
_snake_case : List[str] = type_vocab_size
_snake_case : Optional[int] = type_sequence_label_size
_snake_case : List[Any] = initializer_range
_snake_case : int = num_labels
_snake_case : Tuple = num_choices
_snake_case : List[Any] = scope
_snake_case : str = vocab_size - 1
def UpperCamelCase_ ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
_snake_case : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_snake_case : Dict = None
if self.use_input_mask:
_snake_case : List[Any] = random_attention_mask([self.batch_size, self.seq_length])
_snake_case : List[Any] = None
if self.use_labels:
_snake_case : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_snake_case : List[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCamelCase_ ( self : Any) -> int:
"""simple docstring"""
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def UpperCamelCase_ ( self : Tuple) -> Dict:
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : Union[str, Any] = self.prepare_config_and_inputs()
_snake_case : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def UpperCamelCase_ ( self : Union[str, Any] , lowerCAmelCase : Tuple , lowerCAmelCase : str , lowerCAmelCase : int) -> Optional[int]:
"""simple docstring"""
_snake_case : Union[str, Any] = GPTNeoXModel(config=lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : Optional[Any] = model(lowerCAmelCase , attention_mask=lowerCAmelCase)
_snake_case : Dict = model(lowerCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase_ ( self : Optional[Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
_snake_case : Tuple = True
_snake_case : Union[str, Any] = GPTNeoXModel(lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : Union[str, Any] = model(lowerCAmelCase , attention_mask=lowerCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase_ ( self : str , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict , lowerCAmelCase : str , lowerCAmelCase : str) -> int:
"""simple docstring"""
_snake_case : Optional[Any] = GPTNeoXForCausalLM(config=lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : int = model(lowerCAmelCase , attention_mask=lowerCAmelCase , labels=lowerCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : Optional[Any] , lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : List[str]) -> str:
"""simple docstring"""
_snake_case : Optional[Any] = self.num_labels
_snake_case : str = GPTNeoXForQuestionAnswering(lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : Tuple = model(lowerCAmelCase , attention_mask=lowerCAmelCase)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def UpperCamelCase_ ( self : str , lowerCAmelCase : Tuple , lowerCAmelCase : Dict , lowerCAmelCase : Dict , lowerCAmelCase : Any) -> Any:
"""simple docstring"""
_snake_case : Any = self.num_labels
_snake_case : Tuple = GPTNeoXForSequenceClassification(lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_snake_case : Union[str, Any] = model(lowerCAmelCase , attention_mask=lowerCAmelCase , labels=lowerCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Any , lowerCAmelCase : Optional[Any] , lowerCAmelCase : str) -> Any:
"""simple docstring"""
_snake_case : Any = self.num_labels
_snake_case : str = GPTNeoXForTokenClassification(lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
_snake_case : Dict = model(lowerCAmelCase , attention_mask=lowerCAmelCase , labels=lowerCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase_ ( self : List[Any] , lowerCAmelCase : Dict , lowerCAmelCase : str , lowerCAmelCase : Union[str, Any]) -> Dict:
"""simple docstring"""
_snake_case : Optional[Any] = True
_snake_case : List[Any] = GPTNeoXForCausalLM(config=lowerCAmelCase)
model.to(lowerCAmelCase)
model.eval()
# first forward pass
_snake_case : Optional[int] = model(lowerCAmelCase , attention_mask=lowerCAmelCase , use_cache=lowerCAmelCase)
_snake_case : List[str] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_snake_case : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size)
_snake_case : Optional[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
_snake_case : Any = torch.cat([input_ids, next_tokens] , dim=-1)
_snake_case : int = torch.cat([input_mask, next_mask] , dim=-1)
_snake_case : Dict = model(lowerCAmelCase , attention_mask=lowerCAmelCase , output_hidden_states=lowerCAmelCase)
_snake_case : List[str] = output_from_no_past["""hidden_states"""][0]
_snake_case : List[Any] = model(
lowerCAmelCase , attention_mask=lowerCAmelCase , past_key_values=lowerCAmelCase , output_hidden_states=lowerCAmelCase , )["""hidden_states"""][0]
# select random slice
_snake_case : Any = ids_tensor((1,) , output_from_past.shape[-1]).item()
_snake_case : Tuple = output_from_no_past[:, -3:, random_slice_idx].detach()
_snake_case : str = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-3))
def UpperCamelCase_ ( self : int) -> List[Any]:
"""simple docstring"""
_snake_case : int = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case , _snake_case : List[str] = config_and_inputs
_snake_case : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class snake_case ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
snake_case_ : str = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ : Union[str, Any] = (GPTNeoXForCausalLM,) if is_torch_available() else ()
snake_case_ : Dict = (
{
"""feature-extraction""": GPTNeoXModel,
"""question-answering""": GPTNeoXForQuestionAnswering,
"""text-classification""": GPTNeoXForSequenceClassification,
"""text-generation""": GPTNeoXForCausalLM,
"""token-classification""": GPTNeoXForTokenClassification,
"""zero-shot""": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ : int = False
snake_case_ : List[Any] = False
snake_case_ : Any = False
snake_case_ : str = False
def UpperCamelCase_ ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_snake_case : Union[str, Any] = GPTNeoXModelTester(self)
_snake_case : List[Any] = ConfigTester(self , config_class=lowerCAmelCase , hidden_size=64 , num_attention_heads=8)
def UpperCamelCase_ ( self : List[str]) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int) -> str:
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
def UpperCamelCase_ ( self : int) -> List[str]:
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
def UpperCamelCase_ ( self : Optional[Any]) -> str:
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : int = self.model_tester.prepare_config_and_inputs_for_decoder()
_snake_case : Dict = None
self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
def UpperCamelCase_ ( self : Optional[int]) -> Optional[int]:
"""simple docstring"""
_snake_case , _snake_case , _snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
def UpperCamelCase_ ( self : Tuple) -> Dict:
"""simple docstring"""
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase)
def UpperCamelCase_ ( self : Dict) -> str:
"""simple docstring"""
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase)
def UpperCamelCase_ ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
_snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase)
def UpperCamelCase_ ( self : Optional[Any]) -> int:
"""simple docstring"""
_snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase)
@unittest.skip(reason="""Feed forward chunking is not implemented""")
def UpperCamelCase_ ( self : Any) -> List[str]:
"""simple docstring"""
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)])
def UpperCamelCase_ ( self : int , lowerCAmelCase : List[Any]) -> Any:
"""simple docstring"""
_snake_case , _snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case : Tuple = ids_tensor([1, 10] , config.vocab_size)
_snake_case : Optional[int] = 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
_snake_case : Tuple = GPTNeoXModel(lowerCAmelCase)
original_model.to(lowerCAmelCase)
original_model.eval()
_snake_case : Union[str, Any] = original_model(lowerCAmelCase).last_hidden_state
_snake_case : Union[str, Any] = original_model(lowerCAmelCase).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
_snake_case : str = {"""type""": scaling_type, """factor""": 10.0}
_snake_case : Optional[Any] = GPTNeoXModel(lowerCAmelCase)
scaled_model.to(lowerCAmelCase)
scaled_model.eval()
_snake_case : Any = scaled_model(lowerCAmelCase).last_hidden_state
_snake_case : List[Any] = scaled_model(lowerCAmelCase).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(lowerCAmelCase , lowerCAmelCase , atol=1E-5))
else:
self.assertFalse(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5))
@require_torch
class snake_case ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self : Optional[int]) -> Any:
"""simple docstring"""
_snake_case : int = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""")
for checkpointing in [True, False]:
_snake_case : Optional[Any] = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""")
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCAmelCase)
_snake_case : Union[str, Any] = tokenizer("""My favorite food is""" , return_tensors="""pt""").to(lowerCAmelCase)
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
_snake_case : Union[str, Any] = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
_snake_case : Optional[Any] = model.generate(**lowerCAmelCase , do_sample=lowerCAmelCase , max_new_tokens=20)
_snake_case : List[Any] = tokenizer.batch_decode(lowerCAmelCase)[0]
self.assertEqual(lowerCAmelCase , lowerCAmelCase)
| 317 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a__ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self : List[Any] , *lowerCAmelCase : List[Any] , **lowerCAmelCase : Dict) -> None:
"""simple docstring"""
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , lowerCAmelCase , )
super().__init__(*lowerCAmelCase , **lowerCAmelCase)
| 317 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
torch.manual_seed(0 )
a_ : Any = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple:
a_ : List[str] = self.dummy_uncond_unet
a_ : List[Any] = ScoreSdeVeScheduler()
a_ : str = ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
sde_ve.to(SCREAMING_SNAKE_CASE__ )
sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
a_ : List[str] = torch.manual_seed(0 )
a_ : List[str] = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=SCREAMING_SNAKE_CASE__ ).images
a_ : Optional[int] = torch.manual_seed(0 )
a_ : Union[str, Any] = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ )[
0
]
a_ : Optional[int] = image[0, -3:, -3:, -1]
a_ : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
a_ : int = 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 SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
a_ : Optional[Any] = 'google/ncsnpp-church-256'
a_ : List[str] = UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : Tuple = ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
sde_ve.to(SCREAMING_SNAKE_CASE__ )
sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
a_ : Any = torch.manual_seed(0 )
a_ : List[Any] = sde_ve(num_inference_steps=1_0 , output_type='numpy' , generator=SCREAMING_SNAKE_CASE__ ).images
a_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
a_ : Any = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 370 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ ):
snake_case__ : Dict = 1
@register_to_config
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple=2_0_0_0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=2_0 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1E-3 ) -> Optional[int]:
a_ : Tuple = None
a_ : int = None
a_ : Tuple = None
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, torch.device] = None ) -> List[str]:
a_ : Tuple = torch.linspace(1 , self.config.sampling_eps , SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple=None ) -> Tuple:
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
a_ : Tuple = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
a_ : int = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
a_ : Dict = std.flatten()
while len(std.shape ) < len(score.shape ):
a_ : str = std.unsqueeze(-1 )
a_ : List[str] = -score / std
# compute
a_ : List[str] = -1.0 / len(self.timesteps )
a_ : Optional[int] = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
a_ : Optional[Any] = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
a_ : List[str] = beta_t.unsqueeze(-1 )
a_ : Optional[Any] = -0.5 * beta_t * x
a_ : Tuple = torch.sqrt(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = drift - diffusion**2 * score
a_ : List[str] = x + drift * dt
# add noise
a_ : Optional[Any] = randn_tensor(x.shape , layout=x.layout , generator=SCREAMING_SNAKE_CASE__ , device=x.device , dtype=x.dtype )
a_ : Optional[Any] = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self : int ) -> Tuple:
return self.config.num_train_timesteps
| 120 | 0 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : List[str] ={
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
),
}
class _lowercase ( SCREAMING_SNAKE_CASE__ ):
a = """efficientformer"""
def __init__( self: Any , UpperCamelCase__: Dict = [3, 2, 6, 4] , UpperCamelCase__: List[Any] = [48, 96, 224, 448] , UpperCamelCase__: List[Any] = [True, True, True, True] , UpperCamelCase__: Dict = 448 , UpperCamelCase__: Union[str, Any] = 32 , UpperCamelCase__: List[str] = 4 , UpperCamelCase__: int = 7 , UpperCamelCase__: str = 5 , UpperCamelCase__: Any = 8 , UpperCamelCase__: Tuple = 4 , UpperCamelCase__: str = 0.0 , UpperCamelCase__: str = 16 , UpperCamelCase__: int = 3 , UpperCamelCase__: Union[str, Any] = 3 , UpperCamelCase__: List[Any] = 3 , UpperCamelCase__: Tuple = 2 , UpperCamelCase__: Tuple = 1 , UpperCamelCase__: int = 0.0 , UpperCamelCase__: List[Any] = 1 , UpperCamelCase__: Optional[Any] = True , UpperCamelCase__: Optional[Any] = True , UpperCamelCase__: str = 1e-5 , UpperCamelCase__: List[str] = "gelu" , UpperCamelCase__: Dict = 0.02 , UpperCamelCase__: List[Any] = 1e-12 , UpperCamelCase__: int = 224 , UpperCamelCase__: str = 1e-05 , **UpperCamelCase__: Optional[int] , ):
super().__init__(**_UpperCAmelCase )
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : Optional[int] = hidden_dropout_prob
lowerCamelCase__ : List[str] = hidden_sizes
lowerCamelCase__ : Union[str, Any] = num_hidden_layers
lowerCamelCase__ : Any = num_attention_heads
lowerCamelCase__ : Any = initializer_range
lowerCamelCase__ : Optional[int] = layer_norm_eps
lowerCamelCase__ : Union[str, Any] = patch_size
lowerCamelCase__ : Tuple = num_channels
lowerCamelCase__ : List[str] = depths
lowerCamelCase__ : Union[str, Any] = mlp_expansion_ratio
lowerCamelCase__ : Any = downsamples
lowerCamelCase__ : Optional[int] = dim
lowerCamelCase__ : Tuple = key_dim
lowerCamelCase__ : Dict = attention_ratio
lowerCamelCase__ : str = resolution
lowerCamelCase__ : Union[str, Any] = pool_size
lowerCamelCase__ : str = downsample_patch_size
lowerCamelCase__ : List[Any] = downsample_stride
lowerCamelCase__ : Optional[int] = downsample_pad
lowerCamelCase__ : Dict = drop_path_rate
lowerCamelCase__ : Tuple = num_metaad_blocks
lowerCamelCase__ : Union[str, Any] = distillation
lowerCamelCase__ : Optional[int] = use_layer_scale
lowerCamelCase__ : Union[str, Any] = layer_scale_init_value
lowerCamelCase__ : str = image_size
lowerCamelCase__ : int = batch_norm_eps
| 41 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
_lowerCAmelCase = logging.get_logger(__name__)
# General docstring
_lowerCAmelCase = '''RegNetConfig'''
# Base docstring
_lowerCAmelCase = '''facebook/regnet-y-040'''
_lowerCAmelCase = [1, 1088, 7, 7]
# Image classification docstring
_lowerCAmelCase = '''facebook/regnet-y-040'''
_lowerCAmelCase = '''tabby, tabby cat'''
_lowerCAmelCase = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase = 3 , _UpperCAmelCase = 1 , _UpperCAmelCase = 1 , _UpperCAmelCase = "relu" , **_UpperCAmelCase , ) -> Optional[int]:
super().__init__(**_UpperCAmelCase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__UpperCamelCase : List[Any] = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__UpperCamelCase : Tuple = tf.keras.layers.ConvaD(
filters=_UpperCAmelCase , kernel_size=_UpperCAmelCase , strides=_UpperCAmelCase , padding="VALID" , groups=_UpperCAmelCase , use_bias=_UpperCAmelCase , name="convolution" , )
__UpperCamelCase : int = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="normalization" )
__UpperCamelCase : List[str] = ACTaFN[activation] if activation is not None else tf.identity
def a_ (self , _UpperCAmelCase ) -> Dict:
__UpperCamelCase : str = self.convolution(self.padding(_UpperCAmelCase ) )
__UpperCamelCase : Dict = self.normalization(_UpperCAmelCase )
__UpperCamelCase : Dict = self.activation(_UpperCAmelCase )
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , **_UpperCAmelCase ) -> Optional[Any]:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : Any = config.num_channels
__UpperCamelCase : str = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="embedder" , )
def a_ (self , _UpperCAmelCase ) -> Tuple:
__UpperCamelCase : Dict = shape_list(_UpperCAmelCase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__UpperCamelCase : Any = tf.transpose(_UpperCAmelCase , perm=(0, 2, 3, 1) )
__UpperCamelCase : List[Any] = self.embedder(_UpperCAmelCase )
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase = 2 , **_UpperCAmelCase ) -> Any:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : Any = tf.keras.layers.ConvaD(
filters=_UpperCAmelCase , kernel_size=1 , strides=_UpperCAmelCase , use_bias=_UpperCAmelCase , name="convolution" )
__UpperCamelCase : Tuple = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="normalization" )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = False ) -> tf.Tensor:
return self.normalization(self.convolution(_UpperCAmelCase ) , training=_UpperCAmelCase )
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ) -> Any:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : List[str] = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase , name="pooler" )
__UpperCamelCase : Optional[Any] = [
tf.keras.layers.ConvaD(filters=_UpperCAmelCase , kernel_size=1 , activation="relu" , name="attention.0" ),
tf.keras.layers.ConvaD(filters=_UpperCAmelCase , kernel_size=1 , activation="sigmoid" , name="attention.2" ),
]
def a_ (self , _UpperCAmelCase ) -> Tuple:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__UpperCamelCase : List[str] = self.pooler(_UpperCAmelCase )
for layer_module in self.attention:
__UpperCamelCase : str = layer_module(_UpperCAmelCase )
__UpperCamelCase : List[Any] = hidden_state * pooled
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 , **_UpperCAmelCase ) -> int:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : List[Any] = in_channels != out_channels or stride != 1
__UpperCamelCase : List[str] = max(1 , out_channels // config.groups_width )
__UpperCamelCase : List[Any] = (
TFRegNetShortCut(_UpperCAmelCase , stride=_UpperCAmelCase , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__UpperCamelCase : Optional[Any] = [
TFRegNetConvLayer(_UpperCAmelCase , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
_UpperCAmelCase , stride=_UpperCAmelCase , groups=_UpperCAmelCase , activation=config.hidden_act , name="layer.1" ),
TFRegNetConvLayer(_UpperCAmelCase , kernel_size=1 , activation=_UpperCAmelCase , name="layer.2" ),
]
__UpperCamelCase : Dict = ACTaFN[config.hidden_act]
def a_ (self , _UpperCAmelCase ) -> Union[str, Any]:
__UpperCamelCase : List[Any] = hidden_state
for layer_module in self.layers:
__UpperCamelCase : Dict = layer_module(_UpperCAmelCase )
__UpperCamelCase : List[Any] = self.shortcut(_UpperCAmelCase )
hidden_state += residual
__UpperCamelCase : Tuple = self.activation(_UpperCAmelCase )
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 , **_UpperCAmelCase ) -> Any:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : str = in_channels != out_channels or stride != 1
__UpperCamelCase : Optional[int] = max(1 , out_channels // config.groups_width )
__UpperCamelCase : Union[str, Any] = (
TFRegNetShortCut(_UpperCAmelCase , stride=_UpperCAmelCase , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
__UpperCamelCase : Union[str, Any] = [
TFRegNetConvLayer(_UpperCAmelCase , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
_UpperCAmelCase , stride=_UpperCAmelCase , groups=_UpperCAmelCase , activation=config.hidden_act , name="layer.1" ),
TFRegNetSELayer(_UpperCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) , name="layer.2" ),
TFRegNetConvLayer(_UpperCAmelCase , kernel_size=1 , activation=_UpperCAmelCase , name="layer.3" ),
]
__UpperCamelCase : Union[str, Any] = ACTaFN[config.hidden_act]
def a_ (self , _UpperCAmelCase ) -> int:
__UpperCamelCase : str = hidden_state
for layer_module in self.layers:
__UpperCamelCase : Any = layer_module(_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = self.shortcut(_UpperCAmelCase )
hidden_state += residual
__UpperCamelCase : Union[str, Any] = self.activation(_UpperCAmelCase )
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 2 , _UpperCAmelCase = 2 , **_UpperCAmelCase ) -> int:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : List[str] = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__UpperCamelCase : Tuple = [
# downsampling is done in the first layer with stride of 2
layer(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , stride=_UpperCAmelCase , name="layers.0" ),
*[layer(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , name=f"layers.{i+1}" ) for i in range(depth - 1 )],
]
def a_ (self , _UpperCAmelCase ) -> Any:
for layer_module in self.layers:
__UpperCamelCase : Dict = layer_module(_UpperCAmelCase )
return hidden_state
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , **_UpperCAmelCase ) -> str:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : Dict = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_UpperCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="stages.0" , ) )
__UpperCamelCase : Union[str, Any] = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_UpperCAmelCase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , depth=_UpperCAmelCase , name=f"stages.{i+1}" ) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = False , _UpperCAmelCase = True ) -> TFBaseModelOutputWithNoAttention:
__UpperCamelCase : List[Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__UpperCamelCase : Any = hidden_states + (hidden_state,)
__UpperCamelCase : Any = stage_module(_UpperCAmelCase )
if output_hidden_states:
__UpperCamelCase : List[Any] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_UpperCAmelCase , hidden_states=_UpperCAmelCase )
@keras_serializable
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
A = RegNetConfig
def __init__(self , _UpperCAmelCase , **_UpperCAmelCase ) -> List[Any]:
super().__init__(**_UpperCAmelCase )
__UpperCamelCase : Optional[int] = config
__UpperCamelCase : List[Any] = TFRegNetEmbeddings(_UpperCAmelCase , name="embedder" )
__UpperCamelCase : Union[str, Any] = TFRegNetEncoder(_UpperCAmelCase , name="encoder" )
__UpperCamelCase : Optional[Any] = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase , name="pooler" )
@unpack_inputs
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
__UpperCamelCase : Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase : Dict = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase : Union[str, Any] = self.embedder(_UpperCAmelCase , training=_UpperCAmelCase )
__UpperCamelCase : str = self.encoder(
_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , return_dict=_UpperCAmelCase , training=_UpperCAmelCase )
__UpperCamelCase : List[str] = encoder_outputs[0]
__UpperCamelCase : Tuple = self.pooler(_UpperCAmelCase )
# Change to NCHW output format have uniformity in the modules
__UpperCamelCase : List[str] = tf.transpose(_UpperCAmelCase , perm=(0, 3, 1, 2) )
__UpperCamelCase : List[Any] = tf.transpose(_UpperCAmelCase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__UpperCamelCase : List[str] = tuple([tf.transpose(_UpperCAmelCase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_UpperCAmelCase , pooler_output=_UpperCAmelCase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A = RegNetConfig
A = "regnet"
A = "pixel_values"
@property
def a_ (self ) -> List[Any]:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
_lowerCAmelCase = R'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
_lowerCAmelCase = R'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , SCREAMING_SNAKE_CASE__ , )
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase ) -> Tuple:
super().__init__(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = TFRegNetMainLayer(_UpperCAmelCase , name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
__UpperCamelCase : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase : Tuple = self.regnet(
pixel_values=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , return_dict=_UpperCAmelCase , training=_UpperCAmelCase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , SCREAMING_SNAKE_CASE__ , )
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase ) -> int:
super().__init__(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = config.num_labels
__UpperCamelCase : Any = TFRegNetMainLayer(_UpperCAmelCase , name="regnet" )
# classification head
__UpperCamelCase : List[str] = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def a_ (self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
__UpperCamelCase : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase : str = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase : Dict = self.regnet(
_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , return_dict=_UpperCAmelCase , training=_UpperCAmelCase )
__UpperCamelCase : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
__UpperCamelCase : List[str] = self.classifier[0](_UpperCAmelCase )
__UpperCamelCase : Optional[int] = self.classifier[1](_UpperCAmelCase )
__UpperCamelCase : str = None if labels is None else self.hf_compute_loss(labels=_UpperCAmelCase , logits=_UpperCAmelCase )
if not return_dict:
__UpperCamelCase : Union[str, Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_UpperCAmelCase , logits=_UpperCAmelCase , hidden_states=outputs.hidden_states )
| 298 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'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:
__UpperCAmelCase = [
'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
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 369 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class __a ( __UpperCamelCase ,__UpperCamelCase ):
__snake_case : Union[str, Any] = """pixel_values"""
__snake_case : Optional[Any] = False
__snake_case : Dict = TimmBackboneConfig
def __init__( self : List[str] , UpperCAmelCase : int , **UpperCAmelCase : List[str] ):
requires_backends(self , """timm""" )
super().__init__(UpperCAmelCase )
lowerCAmelCase_ : List[Any] = config
if config.backbone is None:
raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""" )
if config.backbone not in timm.list_models():
raise ValueError(F'backbone {config.backbone} is not supported by timm.' )
if hasattr(UpperCAmelCase , """out_features""" ) and config.out_features is not None:
raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )
lowerCAmelCase_ : List[str] = getattr(UpperCAmelCase , """use_pretrained_backbone""" , UpperCAmelCase )
if pretrained is None:
raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""" )
# We just take the final layer by default. This matches the default for the transformers models.
lowerCAmelCase_ : str = config.out_indices if getattr(UpperCAmelCase , """out_indices""" , UpperCAmelCase ) is not None else (-1,)
lowerCAmelCase_ : Optional[int] = timm.create_model(
config.backbone , pretrained=UpperCAmelCase , features_only=config.features_only , in_chans=config.num_channels , out_indices=UpperCAmelCase , **UpperCAmelCase , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
lowerCAmelCase_ : Union[str, Any] = self._backbone.return_layers
lowerCAmelCase_ : Dict = {layer["""module"""]: str(UpperCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(UpperCAmelCase )
@classmethod
def A ( cls : Dict , UpperCAmelCase : Union[str, Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : Dict ):
requires_backends(cls , ["""vision""", """timm"""] )
from ...models.timm_backbone import TimmBackboneConfig
lowerCAmelCase_ : Optional[Any] = kwargs.pop("""config""" , TimmBackboneConfig() )
lowerCAmelCase_ : Union[str, Any] = kwargs.pop("""use_timm_backbone""" , UpperCAmelCase )
if not use_timm:
raise ValueError("""use_timm_backbone must be True for timm backbones""" )
lowerCAmelCase_ : Union[str, Any] = kwargs.pop("""num_channels""" , config.num_channels )
lowerCAmelCase_ : Tuple = kwargs.pop("""features_only""" , config.features_only )
lowerCAmelCase_ : List[str] = kwargs.pop("""use_pretrained_backbone""" , config.use_pretrained_backbone )
lowerCAmelCase_ : Optional[Any] = kwargs.pop("""out_indices""" , config.out_indices )
lowerCAmelCase_ : Optional[Any] = TimmBackboneConfig(
backbone=UpperCAmelCase , num_channels=UpperCAmelCase , features_only=UpperCAmelCase , use_pretrained_backbone=UpperCAmelCase , out_indices=UpperCAmelCase , )
return super()._from_config(UpperCAmelCase , **UpperCAmelCase )
def A ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] ):
pass
def A ( self : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any]=None , UpperCAmelCase : List[Any]=None , UpperCAmelCase : int=None , **UpperCAmelCase : Any ):
lowerCAmelCase_ : int = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase_ : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase_ : Any = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("""Cannot output attentions for timm backbones at the moment""" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
lowerCAmelCase_ : Optional[Any] = self._all_layers
lowerCAmelCase_ : List[Any] = self._backbone(UpperCAmelCase , **UpperCAmelCase )
lowerCAmelCase_ : str = self._return_layers
lowerCAmelCase_ : Any = tuple(hidden_states[i] for i in self.out_indices )
else:
lowerCAmelCase_ : Tuple = self._backbone(UpperCAmelCase , **UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = None
lowerCAmelCase_ : List[str] = tuple(UpperCAmelCase )
lowerCAmelCase_ : int = tuple(UpperCAmelCase ) if hidden_states is not None else None
if not return_dict:
lowerCAmelCase_ : Optional[Any] = (feature_maps,)
if output_hidden_states:
lowerCAmelCase_ : Tuple = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=UpperCAmelCase , hidden_states=UpperCAmelCase , attentions=UpperCAmelCase )
| 28 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 340 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_ = '''src/transformers'''
a_ = '''docs/source/en/tasks'''
def _a ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple ) -> Tuple:
"""simple docstring"""
with open(UpperCamelCase_ , "r" , encoding="utf-8" , newline="\n" ) as f:
lowerCAmelCase__ = f.readlines()
# Find the start prompt.
lowerCAmelCase__ = 0
while not lines[start_index].startswith(UpperCamelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase__ = start_index
while not lines[end_index].startswith(UpperCamelCase_ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
a_ = direct_transformers_import(TRANSFORMERS_PATH)
a_ = {
'''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
a_ = {
'''summarization.md''': ('''nllb''',),
'''translation.md''': ('''nllb''',),
}
def _a ( UpperCamelCase_ : List[str] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase__ = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCamelCase_ , set() )
lowerCAmelCase__ = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()] ) + "\n"
def _a ( UpperCamelCase_ : Tuple , UpperCamelCase_ : List[str]=False ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = _find_text_in_file(
filename=os.path.join(UpperCamelCase_ , UpperCamelCase_ ) , start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->" , end_prompt="<!--End of the generated tip-->" , )
lowerCAmelCase__ = get_model_list_for_task(UpperCamelCase_ )
if current_list != new_list:
if overwrite:
with open(os.path.join(UpperCamelCase_ , UpperCamelCase_ ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
" to fix this." )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
a_ = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 340 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def a_ ( __snake_case : Optional[int]=32 , __snake_case : Optional[Any]=10 , __snake_case : str=100 , __snake_case : Any=1026 , __snake_case : List[str]=True , __snake_case : int="data/tokenized_stories_train_wikitext103.jbl" , __snake_case : Any="igf_context_pairs.jbl" , ) -> List[str]:
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
lowerCamelCase_ =generate_datasets(
_lowerCAmelCase , _lowerCAmelCase , number=_lowerCAmelCase , min_len=1026 , trim=_lowerCAmelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
lowerCamelCase_ =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# load pretrained model
lowerCamelCase_ =load_gpta('''gpt2''' ).to(_lowerCAmelCase )
print('''computing perplexity on objective set''' )
lowerCamelCase_ =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).item()
print('''perplexity on objective set:''' , _lowerCAmelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def a_ ( __snake_case : str , __snake_case : Any=15 , __snake_case : Optional[Any]=128 , __snake_case : int=100 , __snake_case : Any="igf_model.pt" , ) -> Union[str, Any]:
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
lowerCamelCase_ =GPTaLMHeadModel.from_pretrained('''gpt2''' )
# Initialize secondary learner to use embedding weights of model
lowerCamelCase_ =SecondaryLearner(_lowerCAmelCase )
# Train secondary learner
lowerCamelCase_ =train_secondary_learner(
_lowerCAmelCase , _lowerCAmelCase , max_epochs=_lowerCAmelCase , batch_size=_lowerCAmelCase , eval_freq=100 , igf_model_path=_lowerCAmelCase , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def a_ ( __snake_case : int , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Tuple=32 , __snake_case : Optional[Any]=1000 , __snake_case : Union[str, Any]=16 , __snake_case : Union[str, Any]=1.0 , __snake_case : List[Any]=recopy_gpta , __snake_case : Tuple=None , __snake_case : Tuple=10 , __snake_case : Any="gpt2_finetuned.pt" , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =RandomSampler(_lowerCAmelCase )
lowerCamelCase_ =DataLoader(_lowerCAmelCase , sampler=_lowerCAmelCase )
lowerCamelCase_ =max_steps // (len(_lowerCAmelCase )) + 1
lowerCamelCase_ =0
lowerCamelCase_ =torch.zeros((1, context_len) , dtype=torch.long , device=_lowerCAmelCase )
lowerCamelCase_ =recopy_model(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(_lowerCAmelCase )
secondary_learner.eval()
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =[]
lowerCamelCase_ =[]
# Compute the performance of the transformer model at the beginning
lowerCamelCase_ =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
test_perps.append(_lowerCAmelCase )
print('''Test perplexity, step''' , _lowerCAmelCase , ''':''' , _lowerCAmelCase )
for epoch in range(int(_lowerCAmelCase ) ):
for step, example in enumerate(_lowerCAmelCase ):
torch.cuda.empty_cache()
lowerCamelCase_ =random.randint(0 , example.size(2 ) - context_len - 1 )
lowerCamelCase_ =example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
lowerCamelCase_ =model(_lowerCAmelCase , labels=_lowerCAmelCase )
lowerCamelCase_ =True
if secondary_learner is not None:
lowerCamelCase_ =secondary_learner.forward(
torch.tensor(_lowerCAmelCase , dtype=torch.long , device=_lowerCAmelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_lowerCAmelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
lowerCamelCase_ =-1
if predicted_q < threshold:
lowerCamelCase_ =False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
lowerCamelCase_ =outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
lowerCamelCase_ =0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
lowerCamelCase_ =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
test_perps.append(_lowerCAmelCase )
print('''Test perplexity, step''' , _lowerCAmelCase , ''':''' , _lowerCAmelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , _lowerCAmelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''' )
# Required parameters
parser.add_argument(
'''--data_dir''' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''The input data dir. Should contain data files for WikiText.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--data_file''' , type=_lowerCAmelCase , default=_lowerCAmelCase , help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
) , )
parser.add_argument(
'''--igf_data_file''' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='''A jbl file containing the context and information gain pairs to train secondary learner.''' , )
parser.add_argument(
'''--output_dir''' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''The output directory where the final fine-tuned model is stored.''' , )
parser.add_argument(
'''--tokenizer_name''' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument('''--seed''' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='''A seed for reproducible training.''' )
parser.add_argument(
'''--context_len''' , default=32 , type=_lowerCAmelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--size_objective_set''' , default=100 , type=_lowerCAmelCase , help='''number of articles that are long enough to be used as our objective set''' , )
parser.add_argument(
'''--eval_freq''' , default=100 , type=_lowerCAmelCase , help='''secondary model evaluation is triggered at eval_freq''' )
parser.add_argument('''--max_steps''' , default=1000 , type=_lowerCAmelCase , help='''To calculate training epochs''' )
parser.add_argument(
'''--secondary_learner_batch_size''' , default=128 , type=_lowerCAmelCase , help='''batch size of training data for secondary learner''' , )
parser.add_argument(
'''--batch_size''' , default=16 , type=_lowerCAmelCase , help='''batch size of training data of language model(gpt2) ''' )
parser.add_argument(
'''--eval_interval''' , default=10 , type=_lowerCAmelCase , help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
) , )
parser.add_argument(
'''--number''' , default=100 , type=_lowerCAmelCase , help='''The number of examples split to be used as objective_set/test_data''' )
parser.add_argument(
'''--min_len''' , default=1026 , type=_lowerCAmelCase , help='''The minimum length of the article to be used as objective set''' )
parser.add_argument(
'''--secondary_learner_max_epochs''' , default=15 , type=_lowerCAmelCase , help='''number of epochs to train secondary learner''' )
parser.add_argument('''--trim''' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='''truncate the example if it exceeds context length''' )
parser.add_argument(
'''--threshold''' , default=1.0 , type=_lowerCAmelCase , help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
) , )
parser.add_argument('''--finetuned_model_name''' , default='''gpt2_finetuned.pt''' , type=_lowerCAmelCase , help='''finetuned_model_name''' )
parser.add_argument(
'''--recopy_model''' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='''Reset the model to the original pretrained GPT-2 weights after each iteration''' , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1026 , trim=_lowerCAmelCase , data_file='''data/tokenized_stories_train_wikitext103.jbl''' , igf_data_file='''igf_context_pairs.jbl''' , )
# Load train data for secondary learner
lowerCamelCase_ =joblib.load('''data/IGF_values.jbl''' )
# Train secondary learner
lowerCamelCase_ =training_secondary_learner(
_lowerCAmelCase , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path='''igf_model.pt''' , )
# load pretrained gpt2 model
lowerCamelCase_ =GPTaLMHeadModel.from_pretrained('''gpt2''' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
lowerCamelCase_ =generate_datasets(
context_len=32 , file='''data/tokenized_stories_train_wikitext103.jbl''' , number=100 , min_len=1026 , trim=_lowerCAmelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , context_len=32 , max_steps=1000 , batch_size=16 , threshold=1.0 , recopy_model=_lowerCAmelCase , secondary_learner=_lowerCAmelCase , eval_interval=10 , finetuned_model_name='''gpt2_finetuned.pt''' , )
if __name__ == "__main__":
main()
| 370 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : int =['image_processor', 'tokenizer']
lowercase : int ='LayoutLMv2ImageProcessor'
lowercase : Any =('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
def __init__( self, lowerCAmelCase=None, lowerCAmelCase=None, **lowerCAmelCase ):
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''', lowerCAmelCase, )
lowerCamelCase_ =kwargs.pop('''feature_extractor''' )
lowerCamelCase_ =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowerCAmelCase, lowerCAmelCase )
def __call__( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = False, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = 0, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = True, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'''You cannot provide bounding boxes '''
'''if you initialized the image processor with apply_ocr set to True.''' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )
# first, apply the image processor
lowerCamelCase_ =self.image_processor(images=lowerCAmelCase, return_tensors=lowerCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =[text] # add batch dimension (as the image processor always adds a batch dimension)
lowerCamelCase_ =features['''words''']
lowerCamelCase_ =self.tokenizer(
text=text if text is not None else features['''words'''], text_pair=text_pair if text_pair is not None else None, boxes=boxes if boxes is not None else features['''boxes'''], word_labels=lowerCAmelCase, add_special_tokens=lowerCAmelCase, padding=lowerCAmelCase, truncation=lowerCAmelCase, max_length=lowerCAmelCase, stride=lowerCAmelCase, pad_to_multiple_of=lowerCAmelCase, return_token_type_ids=lowerCAmelCase, return_attention_mask=lowerCAmelCase, return_overflowing_tokens=lowerCAmelCase, return_special_tokens_mask=lowerCAmelCase, return_offsets_mapping=lowerCAmelCase, return_length=lowerCAmelCase, verbose=lowerCAmelCase, return_tensors=lowerCAmelCase, **lowerCAmelCase, )
# add pixel values
lowerCamelCase_ =features.pop('''pixel_values''' )
if return_overflowing_tokens is True:
lowerCamelCase_ =self.get_overflowing_images(lowerCAmelCase, encoded_inputs['''overflow_to_sample_mapping'''] )
lowerCamelCase_ =images
return encoded_inputs
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError(
'''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
f''' {len(lowerCAmelCase )} and {len(lowerCAmelCase )}''' )
return images_with_overflow
def lowercase__ ( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase, **lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowercase__ ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''', lowerCAmelCase, )
return self.image_processor_class
@property
def lowercase__ ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''', lowerCAmelCase, )
return self.image_processor
| 6 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Union[str, Any]=None ) -> List[str]:
if subparsers is not None:
_a : Optional[int] =subparsers.add_parser("""test""" )
else:
_a : Union[str, Any] =argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""" ,default=_UpperCAmelCase ,help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) ,)
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Any ) -> Dict:
_a : Any =os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
_a : List[str] =script_name
else:
_a : Dict =F"--config_file={args.config_file} {script_name}"
_a : int =["""accelerate-launch"""] + test_args.split()
_a : int =execute_subprocess_async(_UpperCAmelCase ,env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
_a : Optional[int] =test_command_parser()
_a : Tuple =parser.parse_args()
test_command(_UpperCAmelCase )
if __name__ == "__main__":
main()
| 276 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = logging.getLogger(__name__)
@dataclass
class A__ :
__UpperCamelCase : str
__UpperCamelCase : List[str]
__UpperCamelCase : Optional[List[str]]
@dataclass
class A__ :
__UpperCamelCase : List[int]
__UpperCamelCase : List[int]
__UpperCamelCase : Optional[List[int]] = None
__UpperCamelCase : Optional[List[int]] = None
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str = "train"
__UpperCamelCase : Tuple = "dev"
__UpperCamelCase : str = "test"
class A__ :
@staticmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE :Dict , SCREAMING_SNAKE_CASE :Union[Split, str] ) -> List[InputExample]:
'''simple docstring'''
raise NotImplementedError
@staticmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE :str ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
@staticmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE :List[InputExample] , SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :PreTrainedTokenizer , SCREAMING_SNAKE_CASE :str=False , SCREAMING_SNAKE_CASE :Optional[Any]="[CLS]" , SCREAMING_SNAKE_CASE :Optional[int]=1 , SCREAMING_SNAKE_CASE :Any="[SEP]" , SCREAMING_SNAKE_CASE :List[Any]=False , SCREAMING_SNAKE_CASE :Union[str, Any]=False , SCREAMING_SNAKE_CASE :List[str]=0 , SCREAMING_SNAKE_CASE :str=0 , SCREAMING_SNAKE_CASE :Dict=-1_0_0 , SCREAMING_SNAKE_CASE :Optional[int]=0 , SCREAMING_SNAKE_CASE :Tuple=True , ) -> List[InputFeatures]:
'''simple docstring'''
_a : str ={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE )}
_a : Tuple =[]
for ex_index, example in enumerate(SCREAMING_SNAKE_CASE ):
if ex_index % 1_0_0_0_0 == 0:
logger.info("""Writing example %d of %d""" , SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) )
_a : Optional[Any] =[]
_a : List[Any] =[]
for word, label in zip(example.words , example.labels ):
_a : Optional[int] =tokenizer.tokenize(SCREAMING_SNAKE_CASE )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(SCREAMING_SNAKE_CASE ) > 0:
tokens.extend(SCREAMING_SNAKE_CASE )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(SCREAMING_SNAKE_CASE ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
_a : Optional[int] =tokenizer.num_special_tokens_to_add()
if len(SCREAMING_SNAKE_CASE ) > max_seq_length - special_tokens_count:
_a : List[Any] =tokens[: (max_seq_length - special_tokens_count)]
_a : Tuple =label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
_a : Dict =[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
_a : Any =[cls_token] + tokens
_a : Dict =[pad_token_label_id] + label_ids
_a : Union[str, Any] =[cls_token_segment_id] + segment_ids
_a : List[str] =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
_a : Optional[int] =[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE )
# Zero-pad up to the sequence length.
_a : Union[str, Any] =max_seq_length - len(SCREAMING_SNAKE_CASE )
if pad_on_left:
_a : Optional[Any] =([pad_token] * padding_length) + input_ids
_a : Optional[int] =([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
_a : Union[str, Any] =([pad_token_segment_id] * padding_length) + segment_ids
_a : Dict =([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(SCREAMING_SNAKE_CASE ) == max_seq_length
assert len(SCREAMING_SNAKE_CASE ) == max_seq_length
assert len(SCREAMING_SNAKE_CASE ) == max_seq_length
assert len(SCREAMING_SNAKE_CASE ) == max_seq_length
if ex_index < 5:
logger.info("""*** Example ***""" )
logger.info("""guid: %s""" , example.guid )
logger.info("""tokens: %s""" , """ """.join([str(SCREAMING_SNAKE_CASE ) for x in tokens] ) )
logger.info("""input_ids: %s""" , """ """.join([str(SCREAMING_SNAKE_CASE ) for x in input_ids] ) )
logger.info("""input_mask: %s""" , """ """.join([str(SCREAMING_SNAKE_CASE ) for x in input_mask] ) )
logger.info("""segment_ids: %s""" , """ """.join([str(SCREAMING_SNAKE_CASE ) for x in segment_ids] ) )
logger.info("""label_ids: %s""" , """ """.join([str(SCREAMING_SNAKE_CASE ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
_a : Tuple =None
features.append(
InputFeatures(
input_ids=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , label_ids=SCREAMING_SNAKE_CASE ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : List[InputFeatures]
__UpperCamelCase : int = nn.CrossEntropyLoss().ignore_index
def __init__( self :Dict , SCREAMING_SNAKE_CASE :TokenClassificationTask , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :PreTrainedTokenizer , SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :Optional[int] = None , SCREAMING_SNAKE_CASE :int=False , SCREAMING_SNAKE_CASE :Split = Split.train , ) -> List[str]:
'''simple docstring'''
# Load data features from cache or dataset file
_a : Optional[Any] =os.path.join(
SCREAMING_SNAKE_CASE , """cached_{}_{}_{}""".format(mode.value , tokenizer.__class__.__name__ , str(SCREAMING_SNAKE_CASE ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_a : List[str] =cached_features_file + """.lock"""
with FileLock(SCREAMING_SNAKE_CASE ):
if os.path.exists(SCREAMING_SNAKE_CASE ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
_a : Any =torch.load(SCREAMING_SNAKE_CASE )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
_a : Any =token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# TODO clean up all this to leverage built-in features of tokenizers
_a : List[str] =token_classification_task.convert_examples_to_features(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=SCREAMING_SNAKE_CASE , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f"Saving features into cached file {cached_features_file}" )
torch.save(self.features , SCREAMING_SNAKE_CASE )
def __len__( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return len(self.features )
def __getitem__( self :Dict , SCREAMING_SNAKE_CASE :int ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
if is_tf_available():
import tensorflow as tf
class A__ :
__UpperCamelCase : List[InputFeatures]
__UpperCamelCase : int = -100
def __init__( self :str , SCREAMING_SNAKE_CASE :TokenClassificationTask , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :PreTrainedTokenizer , SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :Optional[int] = None , SCREAMING_SNAKE_CASE :str=False , SCREAMING_SNAKE_CASE :Split = Split.train , ) -> Any:
'''simple docstring'''
_a : Tuple =token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# TODO clean up all this to leverage built-in features of tokenizers
_a : List[Any] =token_classification_task.convert_examples_to_features(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=SCREAMING_SNAKE_CASE , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
_a : Union[str, Any] =tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) , (
{"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
_a : Union[str, Any] =tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) , (
{
"""input_ids""": tf.TensorShape([None] ),
"""attention_mask""": tf.TensorShape([None] ),
"""token_type_ids""": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def __UpperCAmelCase ( self :Tuple ) -> Any:
'''simple docstring'''
_a : List[Any] =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self :str ) -> Optional[int]:
'''simple docstring'''
return len(self.features )
def __getitem__( self :int , SCREAMING_SNAKE_CASE :str ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
| 276 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self : Union[str, Any] , snake_case : Optional[int] , snake_case : List[str]=1_3 , snake_case : Union[str, Any]=3_0 , snake_case : int=2 , snake_case : List[str]=3 , snake_case : int=True , snake_case : Optional[Any]=True , snake_case : Optional[Any]=3_2 , snake_case : Optional[Any]=5 , snake_case : List[Any]=4 , snake_case : List[str]=3_7 , snake_case : Optional[Any]="gelu" , snake_case : Optional[int]=0.1 , snake_case : Optional[Any]=0.1 , snake_case : Union[str, Any]=1_0 , snake_case : Dict=0.02 , snake_case : Dict=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = parent
UpperCamelCase_ : str = batch_size
UpperCamelCase_ : Tuple = image_size
UpperCamelCase_ : List[Any] = patch_size
UpperCamelCase_ : Tuple = num_channels
UpperCamelCase_ : str = is_training
UpperCamelCase_ : Optional[int] = use_labels
UpperCamelCase_ : Optional[Any] = hidden_size
UpperCamelCase_ : List[Any] = num_hidden_layers
UpperCamelCase_ : Tuple = num_attention_heads
UpperCamelCase_ : List[Any] = intermediate_size
UpperCamelCase_ : Optional[Any] = hidden_act
UpperCamelCase_ : Union[str, Any] = hidden_dropout_prob
UpperCamelCase_ : Dict = attention_probs_dropout_prob
UpperCamelCase_ : List[str] = type_sequence_label_size
UpperCamelCase_ : Optional[Any] = initializer_range
UpperCamelCase_ : List[str] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCamelCase_ : int = (image_size // patch_size) ** 2
UpperCamelCase_ : Union[str, Any] = num_patches + 1
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase_ : Optional[int] = None
if self.use_labels:
UpperCamelCase_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ : Tuple = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : List[Any] , snake_case : Dict , snake_case : Any ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : List[str] = ViTMSNModel(config=snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : Tuple = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : Tuple , snake_case : Optional[Any] , snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : str = self.type_sequence_label_size
UpperCamelCase_ : Union[str, Any] = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : List[str] = model(snake_case , labels=snake_case )
print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' )
print('Labels: {labels}' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase_ : Any = 1
UpperCamelCase_ : Union[str, Any] = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase_ : Union[str, Any] = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : int = self.prepare_config_and_inputs()
UpperCamelCase_ : List[Any] = config_and_inputs
UpperCamelCase_ : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( snake_case_ , snake_case_ , unittest.TestCase ):
lowercase = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowercase = (
{'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = ViTMSNModelTester(self )
UpperCamelCase_ : Tuple = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMSN does not use inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ : int = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase_ : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ : Optional[int] = model_class(snake_case )
UpperCamelCase_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase_ : Union[str, Any] = [*signature.parameters.keys()]
UpperCamelCase_ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
"""simple docstring"""
UpperCamelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ : str = ViTMSNModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __lowercase ( ):
UpperCamelCase_ : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Dict:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
"""simple docstring"""
torch.manual_seed(2 )
UpperCamelCase_ : Optional[int] = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(snake_case )
UpperCamelCase_ : Dict = self.default_image_processor
UpperCamelCase_ : Union[str, Any] = prepare_img()
UpperCamelCase_ : int = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
UpperCamelCase_ : Dict = model(**snake_case )
# verify the logits
UpperCamelCase_ : Optional[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , snake_case )
UpperCamelCase_ : Optional[int] = torch.tensor([-0.0803, -0.4454, -0.2375] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1e-4 ) )
| 368 | def __lowercase ( lowerCamelCase : str ):
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
UpperCamelCase_ : Optional[int] = sorted(string.lower() )
return len(lowerCamelCase ) == len(set(lowerCamelCase ) )
if __name__ == "__main__":
a_ = input('Enter a string ').strip()
a_ = is_isogram(input_str)
print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")
| 50 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ,A__ ,A__ ,A__ = None ,A__ = None ,A__ = False ,**A__ ,):
super().__init__(features=A__ ,cache_dir=A__ ,keep_in_memory=A__ ,**A__)
lowercase = Sql(
cache_dir=A__ ,features=A__ ,sql=A__ ,con=A__ ,**A__ ,)
def A__ ( self):
lowercase = None
lowercase = None
lowercase = None
lowercase = None
self.builder.download_and_prepare(
download_config=A__ ,download_mode=A__ ,verification_mode=A__ ,base_path=A__ ,)
# Build dataset for splits
lowercase = self.builder.as_dataset(
split='''train''' ,verification_mode=A__ ,in_memory=self.keep_in_memory)
return dataset
class lowercase :
def __init__( self ,A__ ,A__ ,A__ ,A__ = None ,A__ = None ,**A__ ,):
if num_proc is not None and num_proc <= 0:
raise ValueError(f'num_proc {num_proc} must be an integer > 0.')
lowercase = dataset
lowercase = name
lowercase = con
lowercase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowercase = num_proc
lowercase = to_sql_kwargs
def A__ ( self):
lowercase = self.to_sql_kwargs.pop('''sql''' ,A__)
lowercase = self.to_sql_kwargs.pop('''con''' ,A__)
lowercase = self.to_sql_kwargs.pop('''index''' ,A__)
lowercase = self._write(index=A__ ,**self.to_sql_kwargs)
return written
def A__ ( self ,A__):
lowercase , lowercase , lowercase = args
lowercase = {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
lowercase = query_table(
table=self.dataset.data ,key=slice(A__ ,offset + self.batch_size) ,indices=self.dataset._indices ,)
lowercase = batch.to_pandas()
lowercase = df.to_sql(self.name ,self.con ,index=A__ ,**A__)
return num_rows or len(A__)
def A__ ( self ,A__ ,**A__):
lowercase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 ,len(self.dataset) ,self.batch_size) ,unit='''ba''' ,disable=not logging.is_progress_bar_enabled() ,desc='''Creating SQL from Arrow format''' ,):
written += self._batch_sql((offset, index, to_sql_kwargs))
else:
lowercase , lowercase = len(self.dataset), self.batch_size
with multiprocessing.Pool(self.num_proc) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql ,[(offset, index, to_sql_kwargs) for offset in range(0 ,A__ ,A__)] ,) ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,unit='''ba''' ,disable=not logging.is_progress_bar_enabled() ,desc='''Creating SQL from Arrow format''' ,):
written += num_rows
return written
| 101 |
from functools import lru_cache
@lru_cache
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 101 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class A_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : List[str] = 1
@register_to_config
def __init__( self : List[Any] ,SCREAMING_SNAKE_CASE__ : int = 1_0_0_0 ,SCREAMING_SNAKE_CASE__ : Optional[Union[np.ndarray, List[float]]] = None):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(SCREAMING_SNAKE_CASE__)
# standard deviation of the initial noise distribution
__lowerCamelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__lowerCamelCase : int = 4
# running values
__lowerCamelCase : Tuple = []
def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Union[str, torch.device] = None):
__lowerCamelCase : List[str] = num_inference_steps
__lowerCamelCase : int = torch.linspace(1 ,0 ,num_inference_steps + 1)[:-1]
__lowerCamelCase : Dict = torch.cat([steps, torch.tensor([0.0])])
if self.config.trained_betas is not None:
__lowerCamelCase : Any = torch.tensor(self.config.trained_betas ,dtype=torch.floataa)
else:
__lowerCamelCase : Dict = torch.sin(steps * math.pi / 2) ** 2
__lowerCamelCase : Optional[int] = (1.0 - self.betas**2) ** 0.5
__lowerCamelCase : Union[str, Any] = (torch.atana(self.betas ,self.alphas) / math.pi * 2)[:-1]
__lowerCamelCase : Tuple = timesteps.to(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = []
def lowerCAmelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : torch.FloatTensor ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : torch.FloatTensor ,SCREAMING_SNAKE_CASE__ : bool = True ,):
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')
__lowerCamelCase : Tuple = (self.timesteps == timestep).nonzero().item()
__lowerCamelCase : int = timestep_index + 1
__lowerCamelCase : str = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(SCREAMING_SNAKE_CASE__)
if len(self.ets) == 1:
__lowerCamelCase : List[Any] = self.ets[-1]
elif len(self.ets) == 2:
__lowerCamelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets) == 3:
__lowerCamelCase : Dict = (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
else:
__lowerCamelCase : Optional[Any] = (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])
__lowerCamelCase : Tuple = self._get_prev_sample(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : torch.FloatTensor ,*SCREAMING_SNAKE_CASE__ : Any ,**SCREAMING_SNAKE_CASE__ : List[str]):
return sample
def lowerCAmelCase ( self : str ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[Any]):
__lowerCamelCase : Any = self.alphas[timestep_index]
__lowerCamelCase : Dict = self.betas[timestep_index]
__lowerCamelCase : Optional[int] = self.alphas[prev_timestep_index]
__lowerCamelCase : str = self.betas[prev_timestep_index]
__lowerCamelCase : Any = (sample - sigma * ets) / max(SCREAMING_SNAKE_CASE__ ,1E-8)
__lowerCamelCase : Tuple = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any]):
return self.config.num_train_timesteps
| 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a ={
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a =[
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
a =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 113 | 1 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowerCAmelCase = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
_lowerCAmelCase = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
_lowerCAmelCase = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
_lowerCAmelCase = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
_lowerCAmelCase = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
'''simple docstring'''
def a_ (self ) -> Tuple:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/openai/human-eval" , codebase_urls=["https://github.com/openai/human-eval"] , reference_urls=["https://github.com/openai/human-eval"] , license=_LICENSE , )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=[1, 1_0, 1_0_0] , _UpperCAmelCase=4 , _UpperCAmelCase=3.0 ) -> Optional[Any]:
if os.getenv("HF_ALLOW_CODE_EVAL" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("This metric is currently not supported on Windows." )
with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:
__UpperCamelCase : int = []
__UpperCamelCase : Union[str, Any] = Counter()
__UpperCamelCase : Optional[int] = 0
__UpperCamelCase : str = defaultdict(_UpperCAmelCase )
for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ):
for candidate in candidates:
__UpperCamelCase : Union[str, Any] = candidate + "\n" + test_case
__UpperCamelCase : Dict = (test_program, timeout, task_id, completion_id[task_id])
__UpperCamelCase : int = executor.submit(_UpperCAmelCase , *_UpperCAmelCase )
futures.append(_UpperCAmelCase )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(_UpperCAmelCase ):
__UpperCamelCase : Any = future.result()
results[result["task_id"]].append((result["completion_id"], result) )
__UpperCamelCase , __UpperCamelCase : str = [], []
for result in results.values():
result.sort()
__UpperCamelCase : int = [r[1]["passed"] for r in result]
total.append(len(_UpperCAmelCase ) )
correct.append(sum(_UpperCAmelCase ) )
__UpperCamelCase : Optional[int] = np.array(_UpperCAmelCase )
__UpperCamelCase : int = np.array(_UpperCAmelCase )
__UpperCamelCase : Union[str, Any] = k
__UpperCamelCase : str = {f"pass@{k}": estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
def estimator(snake_case__ , snake_case__ , snake_case__ ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(snake_case__ , snake_case__ ):
__UpperCamelCase : Tuple = itertools.repeat(snake_case__ , len(snake_case__ ) )
else:
assert len(snake_case__ ) == len(snake_case__ )
__UpperCamelCase : Optional[int] = iter(snake_case__ )
return np.array([estimator(int(snake_case__ ) , int(snake_case__ ) , snake_case__ ) for n, c in zip(snake_case__ , snake_case__ )] )
| 298 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A = ["image_processor", "tokenizer"]
A = "OwlViTImageProcessor"
A = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase ) -> str:
__UpperCamelCase : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , _UpperCAmelCase , )
__UpperCamelCase : str = kwargs.pop("feature_extractor" )
__UpperCamelCase : Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
def __call__(self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="max_length" , _UpperCAmelCase="np" , **_UpperCAmelCase ) -> str:
if text is None and query_images is None and images is None:
raise ValueError(
"You have to specify at least one text or query image or image. All three cannot be none." )
if text is not None:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) or (isinstance(_UpperCAmelCase , _UpperCAmelCase ) and not isinstance(text[0] , _UpperCAmelCase )):
__UpperCamelCase : Tuple = [self.tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )]
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(text[0] , _UpperCAmelCase ):
__UpperCamelCase : List[str] = []
# Maximum number of queries across batch
__UpperCamelCase : List[str] = max([len(_UpperCAmelCase ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(_UpperCAmelCase ) != max_num_queries:
__UpperCamelCase : Any = t + [" "] * (max_num_queries - len(_UpperCAmelCase ))
__UpperCamelCase : int = self.tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
encodings.append(_UpperCAmelCase )
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )
if return_tensors == "np":
__UpperCamelCase : List[str] = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
__UpperCamelCase : int = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
__UpperCamelCase : Tuple = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
__UpperCamelCase : Optional[Any] = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
__UpperCamelCase : Any = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 )
__UpperCamelCase : List[Any] = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
__UpperCamelCase : Any = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 )
__UpperCamelCase : Optional[Any] = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 )
else:
raise ValueError("Target return tensor type could not be returned" )
__UpperCamelCase : Optional[Any] = BatchEncoding()
__UpperCamelCase : Union[str, Any] = input_ids
__UpperCamelCase : List[str] = attention_mask
if query_images is not None:
__UpperCamelCase : str = BatchEncoding()
__UpperCamelCase : Any = self.image_processor(
_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase ).pixel_values
__UpperCamelCase : List[Any] = query_pixel_values
if images is not None:
__UpperCamelCase : Dict = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
if text is not None and images is not None:
__UpperCamelCase : Optional[Any] = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
__UpperCamelCase : Union[str, Any] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**_UpperCAmelCase ) , tensor_type=_UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[int]:
return self.image_processor.post_process(*_UpperCAmelCase , **_UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> List[str]:
return self.image_processor.post_process_object_detection(*_UpperCAmelCase , **_UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[int]:
return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase , **_UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> int:
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def a_ (self ) -> Tuple:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , _UpperCAmelCase , )
return self.image_processor_class
@property
def a_ (self ) -> Union[str, Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , _UpperCAmelCase , )
return self.image_processor
| 298 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class UpperCamelCase_ (__A ):
__magic_name__ = ['''vqvae''']
def __init__( self : int , lowerCAmelCase_ : AutoencoderKL , lowerCAmelCase_ : UNetaDConditionModel , lowerCAmelCase_ : Mel , lowerCAmelCase_ : Union[DDIMScheduler, DDPMScheduler] , ) -> Tuple:
super().__init__()
self.register_modules(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , mel=lowerCAmelCase_ , vqvae=lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
return 50 if isinstance(self.scheduler , lowerCAmelCase_ ) else 1_000
@torch.no_grad()
def __call__( self : Any , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : str = None , lowerCAmelCase_ : np.ndarray = None , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = None , lowerCAmelCase_ : torch.Generator = None , lowerCAmelCase_ : float = 0 , lowerCAmelCase_ : float = 0 , lowerCAmelCase_ : torch.Generator = None , lowerCAmelCase_ : float = 0 , lowerCAmelCase_ : torch.Tensor = None , lowerCAmelCase_ : torch.Tensor = None , lowerCAmelCase_ : List[Any]=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
UpperCAmelCase_ : str = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase_ : int = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase_ : Any = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowerCAmelCase_ , device=self.device , )
UpperCAmelCase_ : Tuple = noise
UpperCAmelCase_ : Optional[Any] = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : int = self.mel.audio_slice_to_image(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = np.frombuffer(input_image.tobytes() , dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase_ : Dict = (input_image / 255) * 2 - 1
UpperCAmelCase_ : List[str] = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase_ : Union[str, Any] = self.vqvae.encode(torch.unsqueeze(lowerCAmelCase_ , 0 ) ).latent_dist.sample(
generator=lowerCAmelCase_ )[0]
UpperCAmelCase_ : List[str] = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase_ : str = self.scheduler.add_noise(lowerCAmelCase_ , lowerCAmelCase_ , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase_ : List[str] = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase_ : List[Any] = int(mask_start_secs * pixels_per_second )
UpperCAmelCase_ : Any = int(mask_end_secs * pixels_per_second )
UpperCAmelCase_ : int = self.scheduler.add_noise(lowerCAmelCase_ , lowerCAmelCase_ , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowerCAmelCase_ ):
UpperCAmelCase_ : List[str] = self.unet(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )["sample"]
else:
UpperCAmelCase_ : Optional[int] = self.unet(lowerCAmelCase_ , lowerCAmelCase_ )["sample"]
if isinstance(self.scheduler , lowerCAmelCase_ ):
UpperCAmelCase_ : str = self.scheduler.step(
model_output=lowerCAmelCase_ , timestep=lowerCAmelCase_ , sample=lowerCAmelCase_ , eta=lowerCAmelCase_ , generator=lowerCAmelCase_ , )["prev_sample"]
else:
UpperCAmelCase_ : List[str] = self.scheduler.step(
model_output=lowerCAmelCase_ , timestep=lowerCAmelCase_ , sample=lowerCAmelCase_ , generator=lowerCAmelCase_ , )["prev_sample"]
if mask is not None:
if mask_start > 0:
UpperCAmelCase_ : Any = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase_ : Union[str, Any] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase_ : List[Any] = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase_ : Optional[Any] = self.vqvae.decode(lowerCAmelCase_ )["sample"]
UpperCAmelCase_ : Optional[int] = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ : List[Any] = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase_ : List[Any] = (images * 255).round().astype("uint8" )
UpperCAmelCase_ : int = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowerCAmelCase_ , mode="RGB" ).convert("L" ) for _ in images) )
UpperCAmelCase_ : Any = [self.mel.image_to_audio(lowerCAmelCase_ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase_ )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowerCAmelCase_ ) )
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : List[Image.Image] , lowerCAmelCase_ : int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , lowerCAmelCase_ )
self.scheduler.set_timesteps(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = np.array(
[np.frombuffer(image.tobytes() , dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase_ : Dict = (sample / 255) * 2 - 1
UpperCAmelCase_ : Any = torch.Tensor(lowerCAmelCase_ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase_ : Dict = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase_ : int = self.scheduler.alphas_cumprod[t]
UpperCAmelCase_ : Optional[Any] = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase_ : str = 1 - alpha_prod_t
UpperCAmelCase_ : Dict = self.unet(lowerCAmelCase_ , lowerCAmelCase_ )["sample"]
UpperCAmelCase_ : str = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase_ : int = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase_ : Tuple = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : torch.Tensor , lowerCAmelCase_ : torch.Tensor , lowerCAmelCase_ : float ) -> torch.Tensor:
UpperCAmelCase_ : Tuple = acos(torch.dot(torch.flatten(lowerCAmelCase_ ) , torch.flatten(lowerCAmelCase_ ) ) / torch.norm(lowerCAmelCase_ ) / torch.norm(lowerCAmelCase_ ) )
return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase_ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase_ )
| 253 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase_ = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 253 | 1 |
"""simple docstring"""
import os
def _snake_case ( ) -> Dict:
with open(os.path.dirname(lowerCamelCase__ ) + "/p022_names.txt" ) as file:
lowerCamelCase_ : str =str(file.readlines()[0] )
lowerCamelCase_ : Union[str, Any] =names.replace("\"" , "" ).split("," )
names.sort()
lowerCamelCase_ : str =0
lowerCamelCase_ : Optional[int] =0
for i, name in enumerate(lowerCamelCase__ ):
for letter in name:
name_score += ord(lowerCamelCase__ ) - 64
total_score += (i + 1) * name_score
lowerCamelCase_ : List[Any] =0
return total_score
if __name__ == "__main__":
print(solution())
| 144 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
A__ : Optional[Any] = logging.getLogger(__name__)
def _snake_case ( ) -> int:
lowerCamelCase_ : Tuple =argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=lowerCamelCase__ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=lowerCamelCase__ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=lowerCamelCase__ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=lowerCamelCase__ , default="data/dump" , help="The dump file prefix." )
lowerCamelCase_ : Tuple =parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
lowerCamelCase_ : Tuple =BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : Optional[Any] =tokenizer.special_tokens_map["cls_token"] # `[CLS]`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCamelCase_ : str =RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : List[Any] =tokenizer.special_tokens_map["cls_token"] # `<s>`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCamelCase_ : Tuple =GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : Dict =tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
lowerCamelCase_ : Optional[int] =fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(lowerCamelCase__ )} examples to process.""" )
lowerCamelCase_ : str =[]
lowerCamelCase_ : Union[str, Any] =0
lowerCamelCase_ : List[str] =10_000
lowerCamelCase_ : int =time.time()
for text in data:
lowerCamelCase_ : List[str] =F"""{bos} {text.strip()} {sep}"""
lowerCamelCase_ : str =tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
rslt.append(lowerCamelCase__ )
iter += 1
if iter % interval == 0:
lowerCamelCase_ : List[Any] =time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
lowerCamelCase_ : Tuple =time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(lowerCamelCase__ )} examples processed.""" )
lowerCamelCase_ : Optional[Any] =F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
lowerCamelCase_ : Optional[int] =tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCamelCase_ : int =[np.uintaa(lowerCamelCase__ ) for d in rslt]
else:
lowerCamelCase_ : Tuple =[np.intaa(lowerCamelCase__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(lowerCamelCase__ , "wb" ) as handle:
pickle.dump(rslt_ , lowerCamelCase__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 144 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ : Optional[Any] = TypeVar('T')
class a ( Generic[T] ):
"""simple docstring"""
UpperCAmelCase = 4_2 # Cache store of keys
UpperCAmelCase = 4_2 # References of the keys in cache
UpperCAmelCase = 1_0 # Maximum capacity of cache
def __init__( self: Tuple , UpperCamelCase: int ):
"""simple docstring"""
A__ = deque()
A__ = set()
if not n:
A__ = sys.maxsize
elif n < 0:
raise ValueError("""n should be an integer greater than 0.""" )
else:
A__ = n
def UpperCamelCase ( self: int , UpperCamelCase: T ):
"""simple docstring"""
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
A__ = self.dq_store.pop()
self.key_reference.remove(UpperCamelCase )
else:
self.dq_store.remove(UpperCamelCase )
self.dq_store.appendleft(UpperCamelCase )
self.key_reference.add(UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
for k in self.dq_store:
print(UpperCamelCase )
def __repr__( self: Optional[Any] ):
"""simple docstring"""
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE_ : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 364 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class a :
"""simple docstring"""
def __init__( self: Any , UpperCamelCase: List[str] , UpperCamelCase: Optional[Any]=13 , UpperCamelCase: str=10 , UpperCamelCase: Dict=3 , UpperCamelCase: Any=2 , UpperCamelCase: str=2 , UpperCamelCase: Any=2 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Any=True , UpperCamelCase: Dict=32 , UpperCamelCase: Optional[int]=5 , UpperCamelCase: Tuple=4 , UpperCamelCase: Optional[int]=37 , UpperCamelCase: Dict="gelu" , UpperCamelCase: Optional[int]=0.1 , UpperCamelCase: Dict=0.1 , UpperCamelCase: Union[str, Any]=10 , UpperCamelCase: List[Any]=0.02 , UpperCamelCase: str=0.9 , UpperCamelCase: Any=None , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = patch_size
A__ = tubelet_size
A__ = num_frames
A__ = is_training
A__ = use_labels
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = type_sequence_label_size
A__ = initializer_range
A__ = mask_ratio
A__ = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
A__ = (image_size // patch_size) ** 2
A__ = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
A__ = int(mask_ratio * self.seq_length )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = floats_tensor(
[self.batch_size, self.num_frames, 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 UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_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 , is_decoder=UpperCamelCase , initializer_range=self.initializer_range , )
def UpperCamelCase ( self: Any , UpperCamelCase: Dict , UpperCamelCase: Tuple , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = VideoMAEModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self: List[str] , UpperCamelCase: Optional[Any] , UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any] ):
"""simple docstring"""
A__ = VideoMAEForPreTraining(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
A__ = torch.ones((self.num_masks,) )
A__ = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
A__ = mask.expand(self.batch_size , -1 ).bool()
A__ = model(UpperCamelCase , UpperCamelCase )
# model only returns predictions for masked patches
A__ = mask.sum().item()
A__ = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def UpperCamelCase ( self: Union[str, 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 ( _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
UpperCAmelCase = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = VideoMAEModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase , hidden_size=37 )
def UpperCamelCase ( self: str , UpperCamelCase: Optional[int] , UpperCamelCase: Dict , UpperCamelCase: Union[str, Any]=False ):
"""simple docstring"""
A__ = copy.deepcopy(UpperCamelCase )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
A__ = torch.ones((self.model_tester.num_masks,) )
A__ = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
A__ = mask.expand(self.model_tester.batch_size , -1 ).bool()
A__ = bool_masked_pos.to(UpperCamelCase )
if return_labels:
if model_class in [
*get_values(UpperCamelCase ),
]:
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase )
return inputs_dict
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""VideoMAE does not use inputs_embeds""" )
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
pass
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase , nn.Linear ) )
def UpperCamelCase ( self: List[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(UpperCamelCase )
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] , UpperCamelCase )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCamelCase )
@slow
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = VideoMAEModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
if not self.has_attentions:
pass
else:
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = self.model_tester.seq_length - self.model_tester.num_masks
A__ = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
A__ = True
A__ = False
A__ = True
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
A__ = len(UpperCamelCase )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
self.assertEqual(out_len + 1 , len(UpperCamelCase ) )
A__ = outputs.attentions
self.assertEqual(len(UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase: Tuple , UpperCamelCase: Optional[Any] , UpperCamelCase: List[str] ):
A__ = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCamelCase , UpperCamelCase ) )
A__ = outputs.hidden_states
A__ = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(UpperCamelCase ) , UpperCamelCase )
A__ = self.model_tester.seq_length - self.model_tester.num_masks
A__ = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
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(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(UpperCamelCase , UpperCamelCase , UpperCamelCase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
pass
def _snake_case ( ):
A__ = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
A__ = np.load(UpperCAmelCase_ )
return list(UpperCAmelCase_ )
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics""" ).to(
UpperCamelCase )
A__ = self.default_image_processor
A__ = prepare_video()
A__ = image_processor(UpperCamelCase , return_tensors="""pt""" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(**UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase )
A__ = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" ).to(UpperCamelCase )
A__ = self.default_image_processor
A__ = prepare_video()
A__ = image_processor(UpperCamelCase , return_tensors="""pt""" ).to(UpperCamelCase )
# add boolean mask, indicating which patches to mask
A__ = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
A__ = torch.load(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(**UpperCamelCase )
# verify the logits
A__ = torch.Size([1, 14_08, 15_36] )
A__ = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=UpperCamelCase )
self.assertEqual(outputs.logits.shape , UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , UpperCamelCase , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
A__ = torch.tensor([0.5_142] , device=UpperCamelCase )
self.assertTrue(torch.allclose(outputs.loss , UpperCamelCase , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
A__ = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" , norm_pix_loss=UpperCamelCase ).to(
UpperCamelCase )
with torch.no_grad():
A__ = model(**UpperCamelCase )
A__ = torch.tensor(torch.tensor([0.6_469] ) , device=UpperCamelCase )
self.assertTrue(torch.allclose(outputs.loss , UpperCamelCase , atol=1e-4 ) )
| 69 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __a ( __lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class A_ (lowercase__ ):
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = parser.add_parser("download" )
download_parser.add_argument(
"--cache-dir" , type=lowercase_ , default=lowercase_ , help="Path to location to store the models" )
download_parser.add_argument(
"--force" , action="store_true" , help="Force the model to be download even if already in cache-dir" )
download_parser.add_argument(
"--trust-remote-code" , action="store_true" , help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine" , )
download_parser.add_argument("model" , type=lowercase_ , help="Name of the model to download" )
download_parser.set_defaults(func=lowercase_ )
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = model
UpperCAmelCase_ : Union[str, Any] = cache
UpperCAmelCase_ : Union[str, Any] = force
UpperCAmelCase_ : Union[str, Any] = trust_remote_code
def UpperCamelCase__ ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 | 1 |
"""simple docstring"""
import math
class A__ :
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = 0.0
__lowerCAmelCase : Optional[int] = 0.0
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def __lowerCAmelCase ():
# Training Examples ( m, n )
__lowerCAmelCase : Union[str, Any] = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
__lowerCAmelCase : List[Any] = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
__lowerCAmelCase : Dict = SelfOrganizingMap()
__lowerCAmelCase : List[str] = 3
__lowerCAmelCase : List[Any] = 0.5
for _ in range(_UpperCamelCase ):
for j in range(len(_UpperCamelCase ) ):
# training sample
__lowerCAmelCase : Optional[Any] = training_samples[j]
# Compute the winning vector
__lowerCAmelCase : str = self_organizing_map.get_winner(_UpperCamelCase , _UpperCamelCase )
# Update the winning vector
__lowerCAmelCase : Optional[int] = self_organizing_map.update(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# classify test sample
__lowerCAmelCase : Optional[Any] = [0, 0, 0, 1]
__lowerCAmelCase : List[str] = self_organizing_map.get_winner(_UpperCamelCase , _UpperCamelCase )
# results
print(F"Clusters that the test sample belongs to : {winner}" )
print(F"Weights that have been trained : {weights}" )
# running the main() function
if __name__ == "__main__":
main() | 182 |
"""simple docstring"""
import argparse
import datetime
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : Optional[Any] = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
__lowerCAmelCase : Optional[Any] = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(_UpperCamelCase ) < 11:
raise ValueError('Must be 10 characters long' )
# Get month
__lowerCAmelCase : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('Month must be between 1 - 12' )
__lowerCAmelCase : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get day
__lowerCAmelCase : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('Date must be between 1 - 31' )
# Get second separator
__lowerCAmelCase : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get year
__lowerCAmelCase : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'Year out of range. There has to be some sort of limit...right?' )
# Get datetime obj for validation
__lowerCAmelCase : Tuple = datetime.date(int(_UpperCamelCase ) , int(_UpperCamelCase ) , int(_UpperCamelCase ) )
# Start math
if m <= 2:
__lowerCAmelCase : int = y - 1
__lowerCAmelCase : Tuple = m + 12
# maths var
__lowerCAmelCase : int = int(str(_UpperCamelCase )[:2] )
__lowerCAmelCase : int = int(str(_UpperCamelCase )[2:] )
__lowerCAmelCase : int = int(2.6 * m - 5.39 )
__lowerCAmelCase : int = int(c / 4 )
__lowerCAmelCase : int = int(k / 4 )
__lowerCAmelCase : int = int(d + k )
__lowerCAmelCase : int = int(t + u + v + x )
__lowerCAmelCase : int = int(z - (2 * c) )
__lowerCAmelCase : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('The date was evaluated incorrectly. Contact developer.' )
# Response
__lowerCAmelCase : str = F"Your date {date_input}, is a {days[str(_UpperCamelCase )]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ = argparse.ArgumentParser(
description=(
"""Find out what day of the week nearly any date is or was. Enter """
"""date as a string in the mm-dd-yyyy or mm/dd/yyyy format"""
)
)
parser.add_argument(
"""date_input""", type=str, help="""Date as a string (mm-dd-yyyy or mm/dd/yyyy)"""
)
lowerCamelCase__ = parser.parse_args()
zeller(args.date_input) | 182 | 1 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] ="EncodecFeatureExtractor"
SCREAMING_SNAKE_CASE_ : List[Any] =("T5Tokenizer", "T5TokenizerFast")
def __init__( self : Optional[int] , __A : Optional[Any] , __A : List[Any] ):
super().__init__(__A , __A )
__UpperCamelCase = self.feature_extractor
__UpperCamelCase = False
def _lowerCamelCase ( self : Dict , __A : Dict=None , __A : Dict=None , __A : Union[str, Any]=True ):
return self.tokenizer.get_decoder_prompt_ids(task=__A , language=__A , no_timestamps=__A )
def __call__( self : Union[str, Any] , *__A : List[Any] , **__A : List[Any] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A , **__A )
__UpperCamelCase = kwargs.pop('audio' , __A )
__UpperCamelCase = kwargs.pop('sampling_rate' , __A )
__UpperCamelCase = kwargs.pop('text' , __A )
if len(__A ) > 0:
__UpperCamelCase = args[0]
__UpperCamelCase = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if text is not None:
__UpperCamelCase = self.tokenizer(__A , **__A )
if audio is not None:
__UpperCamelCase = self.feature_extractor(__A , *__A , sampling_rate=__A , **__A )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
__UpperCamelCase = audio_inputs['input_values']
if "padding_mask" in audio_inputs:
__UpperCamelCase = audio_inputs['padding_mask']
return inputs
def _lowerCamelCase ( self : Optional[Any] , *__A : str , **__A : Dict ):
__UpperCamelCase = kwargs.pop('audio' , __A )
__UpperCamelCase = kwargs.pop('padding_mask' , __A )
if len(__A ) > 0:
__UpperCamelCase = args[0]
__UpperCamelCase = args[1:]
if audio_values is not None:
return self._decode_audio(__A , padding_mask=__A )
else:
return self.tokenizer.batch_decode(*__A , **__A )
def _lowerCamelCase ( self : str , *__A : List[str] , **__A : List[Any] ):
return self.tokenizer.decode(*__A , **__A )
def _lowerCamelCase ( self : Dict , __A : List[Any] , __A : Optional = None ):
__UpperCamelCase = to_numpy(__A )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = audio_values.shape
if padding_mask is None:
return list(__A )
__UpperCamelCase = to_numpy(__A )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
__UpperCamelCase = seq_len - padding_mask.shape[-1]
__UpperCamelCase = 1 - self.feature_extractor.padding_value
__UpperCamelCase = np.pad(__A , ((0, 0), (0, difference)) , 'constant' , constant_values=__A )
__UpperCamelCase = audio_values.tolist()
for i in range(__A ):
__UpperCamelCase = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
__UpperCamelCase = sliced_audio.reshape(__A , -1 )
return audio_values
| 53 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 53 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float = 1 / sqrt(2 ) ) -> IIRFilter:
_UpperCAmelCase : Dict = tau * frequency / samplerate
_UpperCAmelCase : List[str] = sin(lowerCAmelCase )
_UpperCAmelCase : Tuple = cos(lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor)
_UpperCAmelCase : List[Any] = (1 - _cos) / 2
_UpperCAmelCase : List[Any] = 1 - _cos
_UpperCAmelCase : int = 1 + alpha
_UpperCAmelCase : Tuple = -2 * _cos
_UpperCAmelCase : Optional[Any] = 1 - alpha
_UpperCAmelCase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float = 1 / sqrt(2 ) ) -> IIRFilter:
_UpperCAmelCase : Optional[int] = tau * frequency / samplerate
_UpperCAmelCase : List[Any] = sin(lowerCAmelCase )
_UpperCAmelCase : Any = cos(lowerCAmelCase )
_UpperCAmelCase : str = _sin / (2 * q_factor)
_UpperCAmelCase : List[Any] = (1 + _cos) / 2
_UpperCAmelCase : Dict = -1 - _cos
_UpperCAmelCase : int = 1 + alpha
_UpperCAmelCase : Any = -2 * _cos
_UpperCAmelCase : int = 1 - alpha
_UpperCAmelCase : Any = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float = 1 / sqrt(2 ) ) -> IIRFilter:
_UpperCAmelCase : Tuple = tau * frequency / samplerate
_UpperCAmelCase : Any = sin(lowerCAmelCase )
_UpperCAmelCase : List[Any] = cos(lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = _sin / (2 * q_factor)
_UpperCAmelCase : Union[str, Any] = _sin / 2
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : Tuple = -ba
_UpperCAmelCase : Union[str, Any] = 1 + alpha
_UpperCAmelCase : Tuple = -2 * _cos
_UpperCAmelCase : Tuple = 1 - alpha
_UpperCAmelCase : int = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float = 1 / sqrt(2 ) ) -> IIRFilter:
_UpperCAmelCase : Any = tau * frequency / samplerate
_UpperCAmelCase : Union[str, Any] = sin(lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = cos(lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor)
_UpperCAmelCase : Any = 1 - alpha
_UpperCAmelCase : int = -2 * _cos
_UpperCAmelCase : Any = 1 + alpha
_UpperCAmelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float , lowerCAmelCase: float = 1 / sqrt(2 ) , ) -> IIRFilter:
_UpperCAmelCase : Any = tau * frequency / samplerate
_UpperCAmelCase : Optional[int] = sin(lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = cos(lowerCAmelCase )
_UpperCAmelCase : Optional[int] = _sin / (2 * q_factor)
_UpperCAmelCase : List[str] = 10 ** (gain_db / 40)
_UpperCAmelCase : Dict = 1 + alpha * big_a
_UpperCAmelCase : Union[str, Any] = -2 * _cos
_UpperCAmelCase : Optional[Any] = 1 - alpha * big_a
_UpperCAmelCase : int = 1 + alpha / big_a
_UpperCAmelCase : str = -2 * _cos
_UpperCAmelCase : List[Any] = 1 - alpha / big_a
_UpperCAmelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float , lowerCAmelCase: float = 1 / sqrt(2 ) , ) -> IIRFilter:
_UpperCAmelCase : Tuple = tau * frequency / samplerate
_UpperCAmelCase : Dict = sin(lowerCAmelCase )
_UpperCAmelCase : Optional[int] = cos(lowerCAmelCase )
_UpperCAmelCase : str = _sin / (2 * q_factor)
_UpperCAmelCase : str = 10 ** (gain_db / 40)
_UpperCAmelCase : Any = (big_a + 1) - (big_a - 1) * _cos
_UpperCAmelCase : int = (big_a + 1) + (big_a - 1) * _cos
_UpperCAmelCase : List[str] = (big_a - 1) - (big_a + 1) * _cos
_UpperCAmelCase : Dict = (big_a - 1) + (big_a + 1) * _cos
_UpperCAmelCase : Union[str, Any] = 2 * sqrt(lowerCAmelCase ) * alpha
_UpperCAmelCase : int = big_a * (pmc + aaa)
_UpperCAmelCase : Optional[int] = 2 * big_a * mpc
_UpperCAmelCase : str = big_a * (pmc - aaa)
_UpperCAmelCase : List[Any] = ppmc + aaa
_UpperCAmelCase : int = -2 * pmpc
_UpperCAmelCase : int = ppmc - aaa
_UpperCAmelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float , lowerCAmelCase: float = 1 / sqrt(2 ) , ) -> IIRFilter:
_UpperCAmelCase : List[Any] = tau * frequency / samplerate
_UpperCAmelCase : int = sin(lowerCAmelCase )
_UpperCAmelCase : List[str] = cos(lowerCAmelCase )
_UpperCAmelCase : List[Any] = _sin / (2 * q_factor)
_UpperCAmelCase : Union[str, Any] = 10 ** (gain_db / 40)
_UpperCAmelCase : int = (big_a + 1) - (big_a - 1) * _cos
_UpperCAmelCase : Optional[Any] = (big_a + 1) + (big_a - 1) * _cos
_UpperCAmelCase : Dict = (big_a - 1) - (big_a + 1) * _cos
_UpperCAmelCase : List[Any] = (big_a - 1) + (big_a + 1) * _cos
_UpperCAmelCase : Tuple = 2 * sqrt(lowerCAmelCase ) * alpha
_UpperCAmelCase : Any = big_a * (ppmc + aaa)
_UpperCAmelCase : str = -2 * big_a * pmpc
_UpperCAmelCase : List[str] = big_a * (ppmc - aaa)
_UpperCAmelCase : Any = pmc + aaa
_UpperCAmelCase : Any = 2 * mpc
_UpperCAmelCase : Union[str, Any] = pmc - aaa
_UpperCAmelCase : Tuple = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 189 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase ) + 1
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
_UpperCAmelCase : List[str] = [[0 for i in range(lowerCAmelCase )] for j in range(lowerCAmelCase )]
# since string of zero length match pattern of zero length
_UpperCAmelCase : List[Any] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowerCAmelCase ):
_UpperCAmelCase : Dict = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowerCAmelCase ):
_UpperCAmelCase : Tuple = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowerCAmelCase ):
for j in range(1 , lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
_UpperCAmelCase : Optional[Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
_UpperCAmelCase : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
_UpperCAmelCase : str = dp[i - 1][j]
else:
_UpperCAmelCase : int = 0
else:
_UpperCAmelCase : List[Any] = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
SCREAMING_SNAKE_CASE_ = 'aab'
SCREAMING_SNAKE_CASE_ = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 189 | 1 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as input_file:
UpperCAmelCase : List[str] = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
UpperCAmelCase : str = input_file.read()
UpperCAmelCase : int = regexp.search(_SCREAMING_SNAKE_CASE )
return match
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as input_file:
UpperCAmelCase : Union[str, Any] = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
UpperCAmelCase : Optional[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
UpperCAmelCase : int = regexp.finditer(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[int] = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Dict = Path("""./datasets""" )
UpperCAmelCase : Optional[int] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE ) ):
raise AssertionError(F"open(...) must use utf-8 encoding in {dataset}" )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any = Path("""./datasets""" )
UpperCAmelCase : Optional[Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_SCREAMING_SNAKE_CASE ) ):
raise AssertionError(F"print statement found in {dataset}. Use datasets.logger/logging instead." )
| 109 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : List[str] = "gpt_neox"
def __init__( self , A_=50_432 , A_=6_144 , A_=44 , A_=64 , A_=24_576 , A_="gelu" , A_=0.25 , A_=10_000 , A_=0.0 , A_=0.0 , A_=0.1 , A_=2_048 , A_=0.02 , A_=1e-5 , A_=True , A_=0 , A_=2 , A_=False , A_=True , A_=None , **A_ , ) -> Tuple:
"""simple docstring"""
super().__init__(bos_token_id=A_ , eos_token_id=A_ , **A_ )
UpperCamelCase = vocab_size
UpperCamelCase = max_position_embeddings
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = rotary_pct
UpperCamelCase = rotary_emb_base
UpperCamelCase = attention_dropout
UpperCamelCase = hidden_dropout
UpperCamelCase = classifier_dropout
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = use_cache
UpperCamelCase = tie_word_embeddings
UpperCamelCase = use_parallel_residual
UpperCamelCase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'''got {self.rope_scaling}''' )
UpperCamelCase = self.rope_scaling.get('type' , A_ )
UpperCamelCase = self.rope_scaling.get('factor' , A_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
if rope_scaling_factor is None or not isinstance(A_ , A_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 222 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not len(_UpperCamelCase ) == len(_UpperCamelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
# Calculate the determinants of the matrices
__lowerCAmelCase = aa * ba - aa * ba
__lowerCAmelCase = ca * ba - ca * ba
__lowerCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__lowerCAmelCase = determinant_x / determinant
__lowerCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 259 |
"""simple docstring"""
from __future__ import annotations
import time
A : Union[str, Any] = list[tuple[int, int]]
A : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
A : int = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a ):
__lowerCAmelCase = pos_x
__lowerCAmelCase = pos_y
__lowerCAmelCase = (pos_y, pos_x)
__lowerCAmelCase = goal_x
__lowerCAmelCase = goal_y
__lowerCAmelCase = parent
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a ):
__lowerCAmelCase = Node(start[1] , start[0] , goal[1] , goal[0] , __a )
__lowerCAmelCase = Node(goal[1] , goal[0] , goal[1] , goal[0] , __a )
__lowerCAmelCase = [self.start]
__lowerCAmelCase = False
def snake_case ( self ):
while self.node_queue:
__lowerCAmelCase = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
__lowerCAmelCase = True
return self.retrace_path(__a )
__lowerCAmelCase = self.get_successors(__a )
for node in successors:
self.node_queue.append(__a )
if not self.reached:
return [self.start.pos]
return None
def snake_case ( self , __a ):
__lowerCAmelCase = []
for action in delta:
__lowerCAmelCase = parent.pos_x + action[1]
__lowerCAmelCase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__a , __a , self.target.pos_y , self.target.pos_x , __a ) )
return successors
def snake_case ( self , __a ):
__lowerCAmelCase = node
__lowerCAmelCase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__lowerCAmelCase = current_node.parent
path.reverse()
return path
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a ):
__lowerCAmelCase = BreadthFirstSearch(__a , __a )
__lowerCAmelCase = BreadthFirstSearch(__a , __a )
__lowerCAmelCase = False
def snake_case ( self ):
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
__lowerCAmelCase = self.fwd_bfs.node_queue.pop(0 )
__lowerCAmelCase = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
__lowerCAmelCase = True
return self.retrace_bidirectional_path(
__a , __a )
__lowerCAmelCase = current_bwd_node
__lowerCAmelCase = current_fwd_node
__lowerCAmelCase = {
self.fwd_bfs: self.fwd_bfs.get_successors(__a ),
self.bwd_bfs: self.bwd_bfs.get_successors(__a ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__a )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def snake_case ( self , __a , __a ):
__lowerCAmelCase = self.fwd_bfs.retrace_path(__a )
__lowerCAmelCase = self.bwd_bfs.retrace_path(__a )
bwd_path.pop()
bwd_path.reverse()
__lowerCAmelCase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
A : List[Any] = (0, 0)
A : Union[str, Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
A : Any = time.time()
A : Dict = BreadthFirstSearch(init, goal)
A : Any = bfs.search()
A : List[str] = time.time() - start_bfs_time
print("Unidirectional BFS computation time : ", bfs_time)
A : Optional[Any] = time.time()
A : Optional[int] = BidirectionalBreadthFirstSearch(init, goal)
A : Any = bd_bfs.search()
A : str = time.time() - start_bd_bfs_time
print("Bidirectional BFS computation time : ", bd_bfs_time)
| 259 | 1 |
"""simple docstring"""
import copy
import re
class a :
_snake_case : Any = 'hp'
_snake_case : Dict = {}
_snake_case : Dict = None
@classmethod
def lowerCAmelCase_ ( cls : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : str ):
_UpperCAmelCase = prefix
_UpperCAmelCase = defaults
cls.build_naming_info()
@staticmethod
def lowerCAmelCase_ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any ):
if len(__lowerCAmelCase ) == 0:
return ""
_UpperCAmelCase = None
if any(char.isdigit() for char in word ):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(__lowerCAmelCase ) + 1 ):
_UpperCAmelCase = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
_UpperCAmelCase = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__lowerCAmelCase : str ):
_UpperCAmelCase = """"""
while integer != 0:
_UpperCAmelCase = chr(ord("""A""" ) + integer % 10 ) + s
integer //= 10
return s
_UpperCAmelCase = 0
while True:
_UpperCAmelCase = word + """#""" + int_to_alphabetic(__lowerCAmelCase )
if sword in info["reverse_short_word"]:
continue
else:
_UpperCAmelCase = sword
break
_UpperCAmelCase = short_word
_UpperCAmelCase = word
return short_word
@staticmethod
def lowerCAmelCase_ ( __lowerCAmelCase : Dict , __lowerCAmelCase : int ):
_UpperCAmelCase = param_name.split("""_""" )
_UpperCAmelCase = [TrialShortNamer.shortname_for_word(__lowerCAmelCase , __lowerCAmelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
_UpperCAmelCase = ["""""", """_"""]
for separator in separators:
_UpperCAmelCase = separator.join(__lowerCAmelCase )
if shortname not in info["reverse_short_param"]:
_UpperCAmelCase = shortname
_UpperCAmelCase = param_name
return shortname
return param_name
@staticmethod
def lowerCAmelCase_ ( __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] ):
_UpperCAmelCase = TrialShortNamer.shortname_for_key(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = short_name
_UpperCAmelCase = param_name
@classmethod
def lowerCAmelCase_ ( cls : List[Any] ):
if cls.NAMING_INFO is not None:
return
_UpperCAmelCase = {
"""short_word""": {},
"""reverse_short_word""": {},
"""short_param""": {},
"""reverse_short_param""": {},
}
_UpperCAmelCase = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = info
@classmethod
def lowerCAmelCase_ ( cls : Dict , __lowerCAmelCase : Optional[Any] ):
cls.build_naming_info()
assert cls.PREFIX is not None
_UpperCAmelCase = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
_UpperCAmelCase = cls.NAMING_INFO["""short_param"""][k]
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase = 1 if v else 0
_UpperCAmelCase = """""" if isinstance(__lowerCAmelCase , (int, float) ) else """-"""
_UpperCAmelCase = f'''{key}{sep}{v}'''
name.append(__lowerCAmelCase )
return "_".join(__lowerCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : Optional[int] , __lowerCAmelCase : Dict ):
_UpperCAmelCase = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
_UpperCAmelCase = []
else:
_UpperCAmelCase = repr.split("""_""" )
_UpperCAmelCase = {}
for value in values:
if "-" in value:
_UpperCAmelCase = value.split("""-""" )
else:
_UpperCAmelCase = re.sub("""[0-9.]""" , """""" , __lowerCAmelCase )
_UpperCAmelCase = float(re.sub("""[^0-9.]""" , """""" , __lowerCAmelCase ) )
_UpperCAmelCase = cls.NAMING_INFO["""reverse_short_param"""][p_k]
_UpperCAmelCase = p_v
for k in cls.DEFAULTS:
if k not in parameters:
_UpperCAmelCase = cls.DEFAULTS[k]
return parameters
| 289 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : str = ["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
__A : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 120 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class A( unittest.TestCase ):
'''simple docstring'''
def a__ ( self : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = SamImageProcessor()
lowerCamelCase_ = SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : Any , **A_ : int ) -> int:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def a__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCamelCase_ = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
lowerCamelCase_ = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def a__ ( self : int ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = self.prepare_image_inputs()
lowerCamelCase_ = image_processor(A_ , return_tensors='np' )
lowerCamelCase_ = processor(images=A_ , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_torch
def a__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = [torch.ones((1, 3, 5, 5) )]
lowerCamelCase_ = [[1764, 2646]]
lowerCamelCase_ = [[683, 1024]]
lowerCamelCase_ = processor.post_process_masks(A_ , A_ , A_ )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
lowerCamelCase_ = processor.post_process_masks(
A_ , torch.tensor(A_ ) , torch.tensor(A_ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
lowerCamelCase_ = [np.ones((1, 3, 5, 5) )]
lowerCamelCase_ = processor.post_process_masks(A_ , np.array(A_ ) , np.array(A_ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
lowerCamelCase_ = [[1, 0], [0, 1]]
with self.assertRaises(A_ ):
lowerCamelCase_ = processor.post_process_masks(A_ , np.array(A_ ) , np.array(A_ ) )
@require_vision
@require_tf
class A( unittest.TestCase ):
'''simple docstring'''
def a__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = SamImageProcessor()
lowerCamelCase_ = SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : List[Any] , **A_ : int ) -> Dict:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def a__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : int ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCamelCase_ = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
lowerCamelCase_ = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=A_ , padding_value=1.0 )
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 ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = self.prepare_image_inputs()
lowerCamelCase_ = image_processor(A_ , return_tensors='np' )
lowerCamelCase_ = processor(images=A_ , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_tf
def a__ ( self : Tuple ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = [tf.ones((1, 3, 5, 5) )]
lowerCamelCase_ = [[1764, 2646]]
lowerCamelCase_ = [[683, 1024]]
lowerCamelCase_ = processor.post_process_masks(A_ , A_ , A_ , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
lowerCamelCase_ = processor.post_process_masks(
A_ , tf.convert_to_tensor(A_ ) , tf.convert_to_tensor(A_ ) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
lowerCamelCase_ = [np.ones((1, 3, 5, 5) )]
lowerCamelCase_ = processor.post_process_masks(
A_ , np.array(A_ ) , np.array(A_ ) , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
lowerCamelCase_ = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
lowerCamelCase_ = processor.post_process_masks(
A_ , np.array(A_ ) , np.array(A_ ) , return_tensors='tf' )
@require_vision
@require_torchvision
class A( unittest.TestCase ):
'''simple docstring'''
def a__ ( self : str ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = SamImageProcessor()
lowerCamelCase_ = SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : List[str] , **A_ : Optional[int] ) -> Tuple:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def a__ ( self : Dict ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCamelCase_ = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def a__ ( self : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
lowerCamelCase_ = [tf.convert_to_tensor(A_ )]
lowerCamelCase_ = [torch.tensor(A_ )]
lowerCamelCase_ = [[1764, 2646]]
lowerCamelCase_ = [[683, 1024]]
lowerCamelCase_ = processor.post_process_masks(
A_ , A_ , A_ , return_tensors='tf' )
lowerCamelCase_ = processor.post_process_masks(
A_ , A_ , A_ , return_tensors='pt' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def a__ ( self : Tuple ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.get_image_processor()
lowerCamelCase_ = SamProcessor(image_processor=A_ )
lowerCamelCase_ = self.prepare_image_inputs()
lowerCamelCase_ = image_processor(A_ , return_tensors='pt' )['pixel_values'].numpy()
lowerCamelCase_ = processor(images=A_ , return_tensors='pt' )['pixel_values'].numpy()
lowerCamelCase_ = image_processor(A_ , return_tensors='tf' )['pixel_values'].numpy()
lowerCamelCase_ = processor(images=A_ , return_tensors='tf' )['pixel_values'].numpy()
self.assertTrue(np.allclose(A_ , A_ ) )
self.assertTrue(np.allclose(A_ , A_ ) )
self.assertTrue(np.allclose(A_ , A_ ) )
| 208 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowerCamelCase : Optional[Any] = False
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Any = "ybelkada/fonts"
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
'Pix2StructImageProcessor. Please upgrade torch.' )
def _SCREAMING_SNAKE_CASE ( lowercase : Dict , lowercase : List[str] , lowercase : List[Any] ):
'''simple docstring'''
requires_backends(lowercase , ['torch'] )
_check_torch_version()
lowerCamelCase_ = image_tensor.unsqueeze(0 )
lowerCamelCase_ = torch.nn.functional.unfold(lowercase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
lowerCamelCase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , lowercase , lowercase , -1 )
lowerCamelCase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : int = 36 , lowercase : str = "black" , lowercase : str = "white" , lowercase : int = 5 , lowercase : int = 5 , lowercase : int = 5 , lowercase : int = 5 , lowercase : Optional[bytes] = None , lowercase : Optional[str] = None , ):
'''simple docstring'''
requires_backends(lowercase , 'vision' )
# Add new lines so that each line is no more than 80 characters.
lowerCamelCase_ = textwrap.TextWrapper(width=80 )
lowerCamelCase_ = wrapper.wrap(text=lowercase )
lowerCamelCase_ = '\n'.join(lowercase )
if font_bytes is not None and font_path is None:
lowerCamelCase_ = io.BytesIO(lowercase )
elif font_path is not None:
lowerCamelCase_ = font_path
else:
lowerCamelCase_ = hf_hub_download(lowercase , 'Arial.TTF' )
lowerCamelCase_ = ImageFont.truetype(lowercase , encoding='UTF-8' , size=lowercase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
lowerCamelCase_ = ImageDraw.Draw(Image.new('RGB' , (1, 1) , lowercase ) )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = temp_draw.textbbox((0, 0) , lowercase , lowercase )
# Create the actual image with a bit of padding around the text.
lowerCamelCase_ = text_width + left_padding + right_padding
lowerCamelCase_ = text_height + top_padding + bottom_padding
lowerCamelCase_ = Image.new('RGB' , (image_width, image_height) , lowercase )
lowerCamelCase_ = ImageDraw.Draw(lowercase )
draw.text(xy=(left_padding, top_padding) , text=lowercase , fill=lowercase , font=lowercase )
return image
def _SCREAMING_SNAKE_CASE ( lowercase : np.ndarray , lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(lowercase , 'vision' )
# Convert to PIL image if necessary
lowerCamelCase_ = to_pil_image(lowercase )
lowerCamelCase_ = render_text(lowercase , **lowercase )
lowerCamelCase_ = max(header_image.width , image.width )
lowerCamelCase_ = int(image.height * (new_width / image.width) )
lowerCamelCase_ = int(header_image.height * (new_width / header_image.width) )
lowerCamelCase_ = Image.new('RGB' , (new_width, new_height + new_header_height) , 'white' )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
lowerCamelCase_ = to_numpy_array(lowercase )
if infer_channel_dimension_format(lowercase ) == ChannelDimension.LAST:
lowerCamelCase_ = to_channel_dimension_format(lowercase , ChannelDimension.LAST )
return new_image
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''flattened_patches''']
def __init__( self : Dict , A_ : bool = True , A_ : bool = True , A_ : Dict[str, int] = None , A_ : int = 2048 , A_ : bool = False , **A_ : str , ) -> None:
"""simple docstring"""
super().__init__(**A_ )
lowerCamelCase_ = patch_size if patch_size is not None else {'height': 16, 'width': 16}
lowerCamelCase_ = do_normalize
lowerCamelCase_ = do_convert_rgb
lowerCamelCase_ = max_patches
lowerCamelCase_ = is_vqa
def a__ ( self : Union[str, Any] , A_ : np.ndarray , A_ : int , A_ : dict , **A_ : Any ) -> np.ndarray:
"""simple docstring"""
requires_backends(self.extract_flattened_patches , 'torch' )
_check_torch_version()
# convert to torch
lowerCamelCase_ = to_channel_dimension_format(A_ , ChannelDimension.FIRST )
lowerCamelCase_ = torch.from_numpy(A_ )
lowerCamelCase_ , lowerCamelCase_ = patch_size['height'], patch_size['width']
lowerCamelCase_ , lowerCamelCase_ = get_image_size(A_ )
# maximize scale s.t.
lowerCamelCase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
lowerCamelCase_ = max(min(math.floor(scale * image_height / patch_height ) , A_ ) , 1 )
lowerCamelCase_ = max(min(math.floor(scale * image_width / patch_width ) , A_ ) , 1 )
lowerCamelCase_ = max(num_feasible_rows * patch_height , 1 )
lowerCamelCase_ = max(num_feasible_cols * patch_width , 1 )
lowerCamelCase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode='bilinear' , align_corners=A_ , antialias=A_ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
lowerCamelCase_ = torch_extract_patches(A_ , A_ , A_ )
lowerCamelCase_ = patches.shape
lowerCamelCase_ = patches_shape[1]
lowerCamelCase_ = patches_shape[2]
lowerCamelCase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
lowerCamelCase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
lowerCamelCase_ = torch.arange(A_ ).reshape([rows, 1] ).repeat(1 , A_ ).reshape([rows * columns, 1] )
lowerCamelCase_ = torch.arange(A_ ).reshape([1, columns] ).repeat(A_ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
lowerCamelCase_ = row_ids.to(torch.floataa )
lowerCamelCase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
lowerCamelCase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
lowerCamelCase_ = torch.nn.functional.pad(A_ , [0, 0, 0, max_patches - (rows * columns)] ).float()
lowerCamelCase_ = to_numpy_array(A_ )
return result
def a__ ( self : Optional[Any] , A_ : np.ndarray , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : str ) -> np.ndarray:
"""simple docstring"""
if image.dtype == np.uinta:
lowerCamelCase_ = image.astype(np.floataa )
# take mean across the whole `image`
lowerCamelCase_ = np.mean(A_ )
lowerCamelCase_ = np.std(A_ )
lowerCamelCase_ = max(A_ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(A_ , mean=A_ , std=A_ , **A_ )
def a__ ( self : Optional[Any] , A_ : ImageInput , A_ : Optional[str] = None , A_ : bool = None , A_ : Optional[bool] = None , A_ : Optional[int] = None , A_ : Optional[Dict[str, int]] = None , A_ : Optional[Union[str, TensorType]] = None , A_ : ChannelDimension = ChannelDimension.FIRST , **A_ : Optional[int] , ) -> ImageInput:
"""simple docstring"""
lowerCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ = patch_size if patch_size is not None else self.patch_size
lowerCamelCase_ = max_patches if max_patches is not None else self.max_patches
lowerCamelCase_ = self.is_vqa
if kwargs.get('data_format' , A_ ) is not None:
raise ValueError('data_format is not an accepted input as the outputs are ' )
lowerCamelCase_ = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ = [convert_to_rgb(A_ ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ = [to_numpy_array(A_ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError('A header text must be provided for VQA models.' )
lowerCamelCase_ = kwargs.pop('font_bytes' , A_ )
lowerCamelCase_ = kwargs.pop('font_path' , A_ )
if isinstance(A_ , A_ ):
lowerCamelCase_ = [header_text] * len(A_ )
lowerCamelCase_ = [
render_header(A_ , header_text[i] , font_bytes=A_ , font_path=A_ )
for i, image in enumerate(A_ )
]
if do_normalize:
lowerCamelCase_ = [self.normalize(image=A_ ) for image in images]
# convert to torch tensor and permute
lowerCamelCase_ = [
self.extract_flattened_patches(image=A_ , max_patches=A_ , patch_size=A_ )
for image in images
]
# create attention mask in numpy
lowerCamelCase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
lowerCamelCase_ = BatchFeature(
data={'flattened_patches': images, 'attention_mask': attention_masks} , tensor_type=A_ )
return encoded_outputs
| 208 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
lowerCAmelCase_ : Dict = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowerCAmelCase_ : List[str] = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
lowerCAmelCase_ : Optional[Any] = '|'.join(sys.argv[1:])
lowerCAmelCase_ : Optional[Any] = re.compile(Rf"""^({joined_dirs}).*?\.py$""")
lowerCAmelCase_ : Optional[Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 63 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(A__ )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
_lowerCamelCase : List[str] = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 28 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case_(_UpperCamelCase , _UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
_snake_case = OmegaConf.load(lowercase__ )
if display:
print(yaml.dump(OmegaConf.to_container(lowercase__ ) ) )
return config
def snake_case_(_UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None ) -> Dict:
"""simple docstring"""
if conf_path is None:
_snake_case = './model_checkpoints/vqgan_only.yaml'
_snake_case = load_config(lowercase__ , display=lowercase__ )
_snake_case = VQModel(**config.model.params )
if ckpt_path is None:
_snake_case = './model_checkpoints/vqgan_only.pt'
_snake_case = torch.load(lowercase__ , map_location=lowercase__ )
if ".ckpt" in ckpt_path:
_snake_case = sd['state_dict']
model.load_state_dict(lowercase__ , strict=lowercase__ )
model.to(lowercase__ )
del sd
return model
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
_snake_case = model.encode(lowercase__ )
print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}""" )
_snake_case = model.decode(lowercase__ )
return xrec
def snake_case_(_UpperCamelCase , _UpperCamelCase=False ) -> str:
"""simple docstring"""
_snake_case = string.rsplit('''.''' , 1 )
if reload:
_snake_case = importlib.import_module(lowercase__ )
importlib.reload(lowercase__ )
return getattr(importlib.import_module(lowercase__ , package=lowercase__ ) , cls )
def snake_case_(_UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
if "target" not in config:
raise KeyError('''Expected key `target` to instantiate.''' )
return get_obj_from_str(config['''target'''] )(**config.get('''params''' , {} ) )
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase=True ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = instantiate_from_config(lowercase__ )
if sd is not None:
model.load_state_dict(lowercase__ )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if ckpt:
_snake_case = torch.load(lowercase__ , map_location='''cpu''' )
_snake_case = pl_sd['global_step']
print(F"""loaded model from global step {global_step}.""" )
else:
_snake_case = {'state_dict': None}
_snake_case = None
_snake_case = load_model_from_config(config.model , pl_sd['''state_dict'''] , gpu=lowercase__ , eval_mode=lowercase__ )['model']
return model, global_step
| 362 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def snake_case_(_UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
_snake_case = checkpoints.load_tax_checkpoint(_UpperCamelCase )
_snake_case = flatten_dict(_UpperCamelCase )
return flax_params
def snake_case_(_UpperCamelCase ) -> List[str]:
"""simple docstring"""
_snake_case = {}
_snake_case = {
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
_snake_case = {
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
_snake_case = '''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
_snake_case = new_key.replace(_UpperCamelCase , _UpperCamelCase )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
_snake_case = new_key.replace(_UpperCamelCase , _UpperCamelCase )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
_snake_case = re.sub(R'''layers_(\d+)''' , R'''layer.\1''' , _UpperCamelCase )
_snake_case = new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
_snake_case = re.sub(R'''layers_(\d+)''' , R'''layer.\1''' , _UpperCamelCase )
_snake_case = flax_dict[key]
_snake_case = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
_snake_case = torch.from_numpy(converted_dict[key].T )
else:
_snake_case = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=False ) -> List[Any]:
"""simple docstring"""
_snake_case = get_flax_param(_UpperCamelCase )
if not use_large:
_snake_case = PixaStructVisionConfig()
_snake_case = PixaStructTextConfig()
else:
_snake_case = PixaStructVisionConfig(
hidden_size=1_536 , d_ff=3_968 , num_attention_heads=24 , num_hidden_layers=18 )
_snake_case = PixaStructTextConfig(hidden_size=1_536 , d_ff=3_968 , num_heads=24 , num_layers=18 )
_snake_case = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=_UpperCamelCase )
_snake_case = PixaStructForConditionalGeneration(_UpperCamelCase )
_snake_case = rename_and_convert_flax_params(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
_snake_case = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
_snake_case = PixaStructImageProcessor()
_snake_case = PixaStructProcessor(image_processor=_UpperCamelCase , tokenizer=_UpperCamelCase )
if use_large:
_snake_case = 4_096
_snake_case = True
# mkdir if needed
os.makedirs(_UpperCamelCase , exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
processor.save_pretrained(_UpperCamelCase )
print('''Model saved in {}'''.format(_UpperCamelCase ) )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
__A = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 278 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(a - b ) for a, b in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if point:
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
for item in point:
if not isinstance(SCREAMING_SNAKE_CASE , (int, float) ):
__UpperCamelCase :Optional[int] = (
'''Expected a list of numbers as input, found '''
f"""{type(SCREAMING_SNAKE_CASE ).__name__}"""
)
raise TypeError(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :List[str] = f"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE ).__name__}"""
raise TypeError(SCREAMING_SNAKE_CASE )
else:
raise ValueError('''Missing an input''' )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(x - y ) for x, y in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A : str = logging.get_logger(__name__)
class __A( a ):
def __init__( self , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''bs4'''] )
super().__init__(**_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> int:
'''simple docstring'''
__a = []
__a = []
__a = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
__a = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
__a = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = BeautifulSoup(_snake_case , '''html.parser''' )
__a = []
__a = []
__a = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
__a = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
__a , __a = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xtags does not correspond''' )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xsubs does not correspond''' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = ''''''
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , _snake_case ) -> BatchFeature:
'''simple docstring'''
__a = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
__a = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
__a = True
if not valid_strings:
raise ValueError(
'''HTML strings must of type `str`, `List[str]` (batch of examples), '''
F"""but is of type {type(_snake_case )}.""" )
__a = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
__a = [html_strings]
# Get nodes + xpaths
__a = []
__a = []
for html_string in html_strings:
__a , __a , __a = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
__a = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
__a = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
__a = {'''nodes''': nodes, '''xpaths''': xpaths}
__a = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs | 6 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['''FNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['''FNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FNetForMaskedLM''',
'''FNetForMultipleChoice''',
'''FNetForNextSentencePrediction''',
'''FNetForPreTraining''',
'''FNetForQuestionAnswering''',
'''FNetForSequenceClassification''',
'''FNetForTokenClassification''',
'''FNetLayer''',
'''FNetModel''',
'''FNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 16 |
'''simple docstring'''
class _A :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : int = data
__UpperCAmelCase : int = previous
__UpperCAmelCase : Union[str, Any] = next_node
def __str__( self ) -> str:
'''simple docstring'''
return f'{self.data}'
def __A ( self ) -> int:
'''simple docstring'''
return self.data
def __A ( self ) -> List[str]:
'''simple docstring'''
return self.next
def __A ( self ) -> str:
'''simple docstring'''
return self.previous
class _A :
def __init__( self , __UpperCAmelCase ) -> str:
'''simple docstring'''
__UpperCAmelCase : int = head
def __iter__( self ) -> str:
'''simple docstring'''
return self
def __A ( self ) -> str:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
__UpperCAmelCase : List[str] = self.current.get_data()
__UpperCAmelCase : int = self.current.get_next()
return value
class _A :
def __init__( self ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = None # First node in list
__UpperCAmelCase : List[str] = None # Last node in list
def __str__( self ) -> int:
'''simple docstring'''
__UpperCAmelCase : Tuple = self.head
__UpperCAmelCase : Optional[int] = []
while current is not None:
nodes.append(current.get_data() )
__UpperCAmelCase : Any = current.get_next()
return " ".join(str(__UpperCAmelCase ) for node in nodes )
def __contains__( self , __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.head
while current:
if current.get_data() == value:
return True
__UpperCAmelCase : Optional[Any] = current.get_next()
return False
def __iter__( self ) -> str:
'''simple docstring'''
return LinkedListIterator(self.head )
def __A ( self ) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def __A ( self , __UpperCAmelCase ) -> None:
'''simple docstring'''
if self.head is None:
__UpperCAmelCase : str = node
__UpperCAmelCase : List[str] = node
else:
self.insert_before_node(self.head , __UpperCAmelCase )
def __A ( self , __UpperCAmelCase ) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(__UpperCAmelCase )
else:
self.insert_after_node(self.tail , __UpperCAmelCase )
def __A ( self , __UpperCAmelCase ) -> None:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = Node(__UpperCAmelCase )
if self.head is None:
self.set_head(__UpperCAmelCase )
else:
self.set_tail(__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
'''simple docstring'''
__UpperCAmelCase : Tuple = node
__UpperCAmelCase : List[Any] = node.previous
if node.get_previous() is None:
__UpperCAmelCase : str = node_to_insert
else:
__UpperCAmelCase : Optional[Any] = node_to_insert
__UpperCAmelCase : List[Any] = node_to_insert
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
'''simple docstring'''
__UpperCAmelCase : List[str] = node
__UpperCAmelCase : Union[str, Any] = node.next
if node.get_next() is None:
__UpperCAmelCase : Dict = node_to_insert
else:
__UpperCAmelCase : Any = node_to_insert
__UpperCAmelCase : List[str] = node_to_insert
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : Optional[Any] = Node(__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = self.head
while node:
if current_position == position:
self.insert_before_node(__UpperCAmelCase , __UpperCAmelCase )
return
current_position += 1
__UpperCAmelCase : int = node.next
self.insert_after_node(self.tail , __UpperCAmelCase )
def __A ( self , __UpperCAmelCase ) -> Node:
'''simple docstring'''
__UpperCAmelCase : Dict = self.head
while node:
if node.get_data() == item:
return node
__UpperCAmelCase : List[str] = node.get_next()
raise Exception("""Node not found""" )
def __A ( self , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if (node := self.get_node(__UpperCAmelCase )) is not None:
if node == self.head:
__UpperCAmelCase : Optional[int] = self.head.get_next()
if node == self.tail:
__UpperCAmelCase : Union[str, Any] = self.tail.get_previous()
self.remove_node_pointers(__UpperCAmelCase )
@staticmethod
def __A ( __UpperCAmelCase ) -> None:
'''simple docstring'''
if node.get_next():
__UpperCAmelCase : Optional[Any] = node.previous
if node.get_previous():
__UpperCAmelCase : int = node.next
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Union[str, Any] = None
def __A ( self ) -> List[Any]:
'''simple docstring'''
return self.head is None
def lowercase_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16 | 1 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCamelCase__ = logging.get_logger(__name__)
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : str = set()
__lowerCAmelCase : Any = []
def parse_line(_UpperCamelCase ):
for line in fp:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__lowerCAmelCase : Any = line.decode('UTF-8' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(' ' ):
# process a single warning and move it to `selected_warnings`.
if len(_UpperCAmelCase ) > 0:
__lowerCAmelCase : str = '\n'.join(_UpperCAmelCase )
# Only keep the warnings specified in `targets`
if any(F": {x}: " in warning for x in targets ):
selected_warnings.add(_UpperCAmelCase )
buffer.clear()
continue
else:
__lowerCAmelCase : List[str] = line.strip()
buffer.append(_UpperCAmelCase )
if from_gh:
for filename in os.listdir(_UpperCAmelCase ):
__lowerCAmelCase : Dict = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if not os.path.isdir(_UpperCAmelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(_UpperCAmelCase ) as fp:
parse_line(_UpperCAmelCase )
else:
try:
with zipfile.ZipFile(_UpperCAmelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(_UpperCAmelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(_UpperCAmelCase ) as fp:
parse_line(_UpperCAmelCase )
except Exception:
logger.warning(
F"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped." )
return selected_warnings
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Tuple = set()
__lowerCAmelCase : Optional[int] = [os.path.join(_UpperCAmelCase , _UpperCAmelCase ) for p in os.listdir(_UpperCAmelCase ) if (p.endswith('.zip' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(_UpperCAmelCase , _UpperCAmelCase ) )
return selected_warnings
if __name__ == "__main__":
def __lowerCAmelCase (_UpperCamelCase ):
return values.split(',' )
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
lowerCamelCase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
lowerCamelCase__ = extract_warnings(args.output_dir, args.targets)
lowerCamelCase__ = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4) | 86 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowerCamelCase__ : Tuple = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
lowerCamelCase__ : Dict = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
lowerCamelCase__ : str = subset[i - 1][j]
if arr[i - 1] <= j:
lowerCamelCase__ : Dict = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=9 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.002 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = encoder_seq_length
_UpperCAmelCase = decoder_seq_length
# For common tests
_UpperCAmelCase = self.decoder_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = d_ff
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = decoder_start_token_id
_UpperCAmelCase = None
_UpperCAmelCase = decoder_layers
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
return TaConfig.from_pretrained('google/umt5-base' )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) -> List[str]:
"""simple docstring"""
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_SCREAMING_SNAKE_CASE )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_SCREAMING_SNAKE_CASE )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_SCREAMING_SNAKE_CASE )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = config.num_attention_heads
_UpperCAmelCase = self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, input_dict
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = model(
input_ids=_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = model(input_ids=_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = result.last_hidden_state
_UpperCAmelCase = result.past_key_values
_UpperCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) )
self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) + 1 )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE )['last_hidden_state']
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )['last_hidden_state']
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).half().eval()
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )['last_hidden_state']
self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE ).any().item() )
@require_torch
class __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_a : List[Any] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_a : Tuple = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_a : List[str] = True
_a : List[Any] = False
_a : Tuple = False
_a : List[Any] = True
_a : str = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_a : Tuple = [0.8, 0.9]
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = UMTaModelTester(self )
@unittest.skip('Test has a segmentation fault on torch 1.8.0' )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = UMTaModel(config_and_inputs[0] ).to(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_SCREAMING_SNAKE_CASE , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_SCREAMING_SNAKE_CASE , opset_version=9 , input_names=['input_ids', 'decoder_input_ids'] , )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
_UpperCAmelCase = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs[0]
_UpperCAmelCase = UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval()
model.to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = {
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=_SCREAMING_SNAKE_CASE ),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_SCREAMING_SNAKE_CASE ),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_SCREAMING_SNAKE_CASE ),
}
for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE , head_masking.items() ):
_UpperCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.generate(
config_and_inputs[1]['input_ids'] , num_beams=1 , max_length=3 , output_attentions=_SCREAMING_SNAKE_CASE , return_dict_in_generate=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_UpperCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a ( unittest.TestCase ):
@slow
@unittest.skip(
'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = UMTaForConditionalGeneration.from_pretrained('google/umt5-small' , return_dict=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = AutoTokenizer.from_pretrained('google/umt5-small' , use_fast=_SCREAMING_SNAKE_CASE , legacy=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors='pt' , padding=_SCREAMING_SNAKE_CASE ).input_ids
# fmt: off
_UpperCAmelCase = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.generate(input_ids.to(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = [
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
_UpperCAmelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 185 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCAmelCase__ :Dict = pd.read_csv('''sample_data.csv''', header=None)
lowerCAmelCase__ :int = df.shape[:1][0]
# If you're using some other dataset input the target column
lowerCAmelCase__ :Union[str, Any] = df.iloc[:, 1:2]
lowerCAmelCase__ :Optional[int] = actual_data.values.reshape(len_data, 1)
lowerCAmelCase__ :Tuple = MinMaxScaler().fit_transform(actual_data)
lowerCAmelCase__ :str = 1_0
lowerCAmelCase__ :Optional[Any] = 5
lowerCAmelCase__ :List[str] = 2_0
lowerCAmelCase__ :Any = len_data - periods * look_back
lowerCAmelCase__ :Union[str, Any] = actual_data[:division]
lowerCAmelCase__ :Tuple = actual_data[division - look_back :]
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = [], []
lowerCAmelCase__ , lowerCAmelCase__ :str = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
lowerCAmelCase__ :Optional[Any] = np.array(train_x)
lowerCAmelCase__ :Any = np.array(test_x)
lowerCAmelCase__ :Dict = np.array([list(i.ravel()) for i in train_y])
lowerCAmelCase__ :Tuple = np.array([list(i.ravel()) for i in test_y])
lowerCAmelCase__ :Optional[int] = Sequential()
model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
lowerCAmelCase__ :List[Any] = model.fit(
x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
)
lowerCAmelCase__ :Optional[Any] = model.predict(x_test)
| 185 | 1 |
'''simple docstring'''
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'pipelines_utils',
'0.22.0',
'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
standard_warn=False,
stacklevel=3,
)
| 234 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list[int] , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : str = int(_lowerCamelCase )
# Initialize Result
__SCREAMING_SNAKE_CASE : Tuple = []
# Traverse through all denomination
for denomination in reversed(_lowerCamelCase ):
# Find denominations
while int(_lowerCamelCase ) >= int(_lowerCamelCase ):
total_value -= int(_lowerCamelCase )
answer.append(_lowerCamelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCamelCase__ : int = []
UpperCamelCase__ : List[Any] = '''0'''
if (
input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
== "y"
):
UpperCamelCase__ : Tuple = int(input('''Enter the number of denominations you want to add: ''').strip())
for i in range(0, n):
denominations.append(int(input(f"Denomination {i}: ").strip()))
UpperCamelCase__ : str = input('''Enter the change you want to make in Indian Currency: ''').strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCamelCase__ : List[Any] = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
UpperCamelCase__ : str = input('''Enter the change you want to make: ''').strip()
if int(value) == 0 or int(value) < 0:
print('''The total value cannot be zero or negative.''')
else:
print(f"Following is minimal change for {value}: ")
UpperCamelCase__ : int = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=''' ''') | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 319 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class __A :
def __init__( self , a__ ):
_lowerCAmelCase : int = []
self.adlist.append(
{"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []} )
for keyword in keywords:
self.add_keyword(a__ )
self.set_fail_transitions()
def __A ( self , a__ , a__ ):
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def __A ( self , a__ ):
_lowerCAmelCase : List[Any] = 0
for character in keyword:
_lowerCAmelCase : List[Any] = self.find_next_state(a__ , a__ )
if next_state is None:
self.adlist.append(
{
"""value""": character,
"""next_states""": [],
"""fail_state""": 0,
"""output""": [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
_lowerCAmelCase : Optional[int] = len(self.adlist ) - 1
else:
_lowerCAmelCase : Optional[Any] = next_state
self.adlist[current_state]["output"].append(a__ )
def __A ( self ):
_lowerCAmelCase : Union[str, Any] = deque()
for node in self.adlist[0]["next_states"]:
q.append(a__ )
_lowerCAmelCase : Union[str, Any] = 0
while q:
_lowerCAmelCase : List[Any] = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(a__ )
_lowerCAmelCase : List[Any] = self.adlist[r]["""fail_state"""]
while (
self.find_next_state(a__ , self.adlist[child]["""value"""] ) is None
and state != 0
):
_lowerCAmelCase : Optional[Any] = self.adlist[state]["""fail_state"""]
_lowerCAmelCase : Optional[Any] = self.find_next_state(
a__ , self.adlist[child]["""value"""] )
if self.adlist[child]["fail_state"] is None:
_lowerCAmelCase : str = 0
_lowerCAmelCase : Tuple = (
self.adlist[child]["""output"""]
+ self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
)
def __A ( self , a__ ):
_lowerCAmelCase : Dict = {} # returns a dict with keywords and list of its occurrences
_lowerCAmelCase : Tuple = 0
for i in range(len(a__ ) ):
while (
self.find_next_state(a__ , string[i] ) is None
and current_state != 0
):
_lowerCAmelCase : Dict = self.adlist[current_state]["""fail_state"""]
_lowerCAmelCase : Optional[int] = self.find_next_state(a__ , string[i] )
if next_state is None:
_lowerCAmelCase : Any = 0
else:
_lowerCAmelCase : Tuple = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
_lowerCAmelCase : Any = []
result[key].append(i - len(a__ ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : Optional[Any] = 32
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
return int(x / 2**20 )
class __lowercase :
"""simple docstring"""
def __enter__( self ) -> Optional[Any]:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowerCamelCase = torch.cuda.memory_allocated()
return self
def __exit__( self , *A ) -> int:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
lowerCamelCase = torch.cuda.memory_allocated()
lowerCamelCase = torch.cuda.max_memory_allocated()
lowerCamelCase = bamb(self.end - self.begin )
lowerCamelCase = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def __lowerCamelCase ( lowerCamelCase__ : Accelerator , lowerCamelCase__ : int = 16 , lowerCamelCase__ : str = "bert-base-cased" , lowerCamelCase__ : int = 320 , lowerCamelCase__ : int = 160 , ):
'''simple docstring'''
lowerCamelCase = AutoTokenizer.from_pretrained(lowerCamelCase__ )
lowerCamelCase = load_dataset(
"""glue""" , """mrpc""" , split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'} )
def tokenize_function(lowerCamelCase__ : str ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase__ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCamelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
lowerCamelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
def __lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase = config["""lr"""]
lowerCamelCase = int(config["""num_epochs"""] )
lowerCamelCase = int(config["""seed"""] )
lowerCamelCase = int(config["""batch_size"""] )
lowerCamelCase = args.model_name_or_path
set_seed(lowerCamelCase__ )
lowerCamelCase , lowerCamelCase = get_dataloaders(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__ , return_dict=lowerCamelCase__ )
# Instantiate optimizer
lowerCamelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase = optimizer_cls(params=model.parameters() , lr=lowerCamelCase__ )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowerCamelCase = 1
lowerCamelCase = (len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=0 , num_training_steps=lowerCamelCase__ , )
else:
lowerCamelCase = DummyScheduler(lowerCamelCase__ , total_num_steps=lowerCamelCase__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase = 0
# Now we train the model
lowerCamelCase = {}
for epoch in range(lowerCamelCase__ , lowerCamelCase__ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
lowerCamelCase = model(**lowerCamelCase__ )
lowerCamelCase = outputs.loss
lowerCamelCase = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowerCamelCase = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """peak_memory_utilization.json""" ) , """w""" ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase__ , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--peak_memory_upper_bound""" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" , )
parser.add_argument(
"""--n_train""" , type=lowerCamelCase__ , default=320 , help="""Number of training examples to use.""" , )
parser.add_argument(
"""--n_val""" , type=lowerCamelCase__ , default=160 , help="""Number of validation examples to use.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase__ , default=1 , help="""Number of train epochs.""" , )
lowerCamelCase = parser.parse_args()
lowerCamelCase = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 252 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCamelCase_ ( _lowerCamelCase ):
create_state_space_tree(UpperCamelCase__ , [] , 0 )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if index == len(UpperCamelCase__ ):
print(UpperCamelCase__ )
return
create_state_space_tree(UpperCamelCase__ , UpperCamelCase__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(UpperCamelCase__ , UpperCamelCase__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
A_ : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["A", "B", "C"])
generate_all_subsequences(seq)
| 352 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
A_ : List[Any] = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class a_ ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ : Optional[datasets.Features] = None
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , ):
import pyspark
def generate_fn():
lowerCamelCase__ : Optional[Any] = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
lowerCamelCase__ : Dict = df_with_partition_id.select('*' ).where(f'''part_id = {partition_id}''' ).drop('part_id' )
lowerCamelCase__ : Dict = partition_df.collect()
lowerCamelCase__ : int = 0
for row in rows:
yield f'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class a_ ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Tuple = df
lowerCamelCase__ : Any = partition_order or range(self.df.rdd.getNumPartitions() )
lowerCamelCase__ : List[Any] = _generate_iterable_examples(self.df, self.partition_order )
def __iter__(self ):
'''simple docstring'''
yield from self.generate_examples_fn()
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowerCamelCase_ )
return SparkExamplesIterable(self.df, partition_order=lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.split_shard_indices_by_worker(lowerCamelCase_, lowerCamelCase_ )
return SparkExamplesIterable(self.df, partition_order=lowerCamelCase_ )
@property
def a__ (self ):
'''simple docstring'''
return len(self.partition_order )
class a_ ( datasets.DatasetBuilder ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = SparkConfig
def __init__(self, lowerCamelCase_, lowerCamelCase_ = None, lowerCamelCase_ = None, **lowerCamelCase_, ):
'''simple docstring'''
import pyspark
lowerCamelCase__ : str = pyspark.sql.SparkSession.builder.getOrCreate()
lowerCamelCase__ : Optional[Any] = df
lowerCamelCase__ : Dict = working_dir
super().__init__(
cache_dir=lowerCamelCase_, config_name=str(self.df.semanticHash() ), **lowerCamelCase_, )
def a__ (self ):
'''simple docstring'''
def create_cache_and_write_probe(lowerCamelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir, exist_ok=lowerCamelCase_ )
lowerCamelCase__ : str = os.path.join(self._cache_dir, 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowerCamelCase_, 'a' )
return [probe_file]
if self._spark.conf.get('spark.master', '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
lowerCamelCase__ : Tuple = (
self._spark.sparkContext.parallelize(range(1 ), 1 ).mapPartitions(lowerCamelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def a__ (self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
import pyspark
def get_arrow_batch_size(lowerCamelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
lowerCamelCase__ : List[Any] = self.df.count()
lowerCamelCase__ : List[Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
lowerCamelCase__ : List[Any] = (
self.df.limit(lowerCamelCase_ )
.repartition(1 )
.mapInArrow(lowerCamelCase_, 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
lowerCamelCase__ : Dict = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
lowerCamelCase__ : str = min(lowerCamelCase_, int(approx_total_size / max_shard_size ) )
lowerCamelCase__ : List[Any] = self.df.repartition(lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
import pyspark
lowerCamelCase__ : List[str] = ParquetWriter if file_format == 'parquet' else ArrowWriter
lowerCamelCase__ : List[str] = os.path.join(self._working_dir, os.path.basename(lowerCamelCase_ ) ) if self._working_dir else fpath
lowerCamelCase__ : Optional[int] = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
lowerCamelCase__ : int = self.config.features
lowerCamelCase__ : Dict = self._writer_batch_size
lowerCamelCase__ : Optional[Any] = self._fs.storage_options
def write_arrow(lowerCamelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
lowerCamelCase__ : Any = pyspark.TaskContext().taskAttemptId()
lowerCamelCase__ : str = next(lowerCamelCase_, lowerCamelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]], names=['task_id', 'num_examples', 'num_bytes'], )
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : Any = writer_class(
features=lowerCamelCase_, path=working_fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), writer_batch_size=lowerCamelCase_, storage_options=lowerCamelCase_, embed_local_files=lowerCamelCase_, )
lowerCamelCase__ : List[str] = pa.Table.from_batches([first_batch] )
writer.write_table(lowerCamelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
lowerCamelCase__ , lowerCamelCase__ : Tuple = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['task_id', 'num_examples', 'num_bytes'], )
shard_id += 1
lowerCamelCase__ : Dict = writer_class(
features=writer._features, path=working_fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), writer_batch_size=lowerCamelCase_, storage_options=lowerCamelCase_, embed_local_files=lowerCamelCase_, )
lowerCamelCase__ : Tuple = pa.Table.from_batches([batch] )
writer.write_table(lowerCamelCase_ )
if writer._num_bytes > 0:
lowerCamelCase__ , lowerCamelCase__ : Tuple = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['task_id', 'num_examples', 'num_bytes'], )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowerCamelCase_ ) ):
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(lowerCamelCase_ ), os.path.basename(lowerCamelCase_ ) )
shutil.move(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : List[str] = (
self.df.mapInArrow(lowerCamelCase_, 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ), pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ), pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ), pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ), )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def a__ (self, lowerCamelCase_, lowerCamelCase_ = "arrow", lowerCamelCase_ = None, lowerCamelCase_ = None, **lowerCamelCase_, ):
'''simple docstring'''
self._validate_cache_dir()
lowerCamelCase__ : Union[str, Any] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowerCamelCase_ )
lowerCamelCase__ : str = not is_remote_filesystem(self._fs )
lowerCamelCase__ : Any = os.path.join if is_local else posixpath.join
lowerCamelCase__ : Any = '-TTTTT-SSSSS-of-NNNNN'
lowerCamelCase__ : Tuple = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
lowerCamelCase__ : Union[str, Any] = path_join(self._output_dir, lowerCamelCase_ )
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Dict = 0
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = []
lowerCamelCase__ : List[str] = []
for task_id, content in self._prepare_split_single(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : int = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowerCamelCase_ )
lowerCamelCase__ : str = total_num_examples
lowerCamelCase__ : int = total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
lowerCamelCase__ : Union[str, Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
lowerCamelCase__ : Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
rename(
lowerCamelCase_, fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), fpath.replace('TTTTT-SSSSS', f'''{global_shard_id:05d}''' ).replace('NNNNN', f'''{total_shards:05d}''' ), )
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : List[str] = 0
for i in range(len(lowerCamelCase_ ) ):
lowerCamelCase__ , lowerCamelCase__ : Any = task_id_and_num_shards[i]
for shard_id in range(lowerCamelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowerCamelCase_, len(lowerCamelCase_ ) ).map(lambda lowerCamelCase_ : _rename_shard(*lowerCamelCase_ ) ).collect()
else:
# don't use any pattern
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Dict = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), fpath.replace(lowerCamelCase_, '' ), )
def a__ (self, lowerCamelCase_, ):
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 316 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class A__ :
"""simple docstring"""
__A : List[str] = BlenderbotConfig
__A : Tuple = {}
__A : Optional[int] = '''gelu'''
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Optional[int]:
'''simple docstring'''
a__ : Union[str, Any] = parent
a__ : Dict = batch_size
a__ : Union[str, Any] = seq_length
a__ : List[str] = is_training
a__ : List[Any] = use_labels
a__ : str = vocab_size
a__ : str = hidden_size
a__ : List[Any] = num_hidden_layers
a__ : Tuple = num_attention_heads
a__ : Dict = intermediate_size
a__ : Any = hidden_dropout_prob
a__ : List[Any] = attention_probs_dropout_prob
a__ : List[str] = max_position_embeddings
a__ : List[Any] = eos_token_id
a__ : Union[str, Any] = pad_token_id
a__ : str = bos_token_id
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : str = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
a__ : str = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
a__ : List[str] = tf.concat([input_ids, eos_tensor] , axis=1)
a__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__ : Optional[Any] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
a__ : Optional[int] = prepare_blenderbot_inputs_dict(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__)
return config, inputs_dict
def __lowercase ( self , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__ : int = TFBlenderbotModel(config=lowerCAmelCase__).get_decoder()
a__ : Any = inputs_dict['input_ids']
a__ : List[str] = input_ids[:1, :]
a__ : Any = inputs_dict['attention_mask'][:1, :]
a__ : Dict = inputs_dict['head_mask']
a__ : List[str] = 1
# first forward pass
a__ : Tuple = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , head_mask=lowerCAmelCase__ , use_cache=lowerCAmelCase__)
a__ , a__ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a__ : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size)
a__ : Dict = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
a__ : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1)
a__ : Union[str, Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1)
a__ : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__)[0]
a__ : str = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
a__ : int = int(ids_tensor((1,) , output_from_past.shape[-1]))
a__ : Any = output_from_no_past[:, -3:, random_slice_idx]
a__ : int = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCAmelCase__ , lowerCAmelCase__ , rtol=1e-3)
def A_ ( A__ , A__ , A__ , A__=None , A__=None , A__=None , A__=None , A__=None , ) -> Optional[Any]:
if attention_mask is None:
a__ : int = tf.cast(tf.math.not_equal(A__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
a__ : List[str] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
a__ : Tuple = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
a__ : Union[str, Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
a__ : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A__ ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
__A : Dict = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__A : str = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__A : int = (
{
'''conversational''': TFBlenderbotForConditionalGeneration,
'''feature-extraction''': TFBlenderbotModel,
'''summarization''': TFBlenderbotForConditionalGeneration,
'''text2text-generation''': TFBlenderbotForConditionalGeneration,
'''translation''': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A : List[str] = True
__A : List[str] = False
__A : List[str] = False
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : int = TFBlenderbotModelTester(self)
a__ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__)
def __lowercase ( self) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__)
@require_tokenizers
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
__A : int = ['''My friends are cool but they eat too many carbs.''']
__A : Any = '''facebook/blenderbot-400M-distill'''
@cached_property
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name)
@cached_property
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
return model
@slow
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Dict = self.tokenizer(self.src_text , return_tensors='tf')
a__ : List[Any] = self.model.generate(
model_inputs.input_ids , )
a__ : Optional[int] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowerCAmelCase__)[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__UpperCamelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__UpperCamelCase = tuple[int, int]
class UpperCamelCase :
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, ) -> None:
snake_case_ = pos_x
snake_case_ = pos_y
snake_case_ = (pos_y, pos_x)
snake_case_ = goal_x
snake_case_ = goal_y
snake_case_ = g_cost
snake_case_ = parent
snake_case_ = self.calculate_heuristic()
snake_case_ = self.g_cost + self.h_cost
def a_ ( self) -> float:
snake_case_ = self.pos_x - self.goal_x
snake_case_ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(lowerCAmelCase__) + abs(lowerCAmelCase__)
else:
return sqrt(dy**2 + dx**2)
def __lt__( self, lowerCAmelCase__) -> bool:
return self.f_cost < other.f_cost
class UpperCamelCase :
def __init__( self, lowerCAmelCase__, lowerCAmelCase__) -> Union[str, Any]:
snake_case_ = Node(start[1], start[0], goal[1], goal[0], 0, lowerCAmelCase__)
snake_case_ = Node(goal[1], goal[0], goal[1], goal[0], 9_9999, lowerCAmelCase__)
snake_case_ = [self.start]
snake_case_ = []
snake_case_ = False
def a_ ( self) -> list[TPosition]:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
snake_case_ = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
return self.retrace_path(lowerCAmelCase__)
self.closed_nodes.append(lowerCAmelCase__)
snake_case_ = self.get_successors(lowerCAmelCase__)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowerCAmelCase__)
else:
# retrieve the best current path
snake_case_ = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase__))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowerCAmelCase__)
else:
self.open_nodes.append(lowerCAmelCase__)
return [self.start.pos]
def a_ ( self, lowerCAmelCase__) -> list[Node]:
snake_case_ = []
for action in delta:
snake_case_ = parent.pos_x + action[1]
snake_case_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(lowerCAmelCase__) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowerCAmelCase__, lowerCAmelCase__, self.target.pos_y, self.target.pos_x, parent.g_cost + 1, lowerCAmelCase__, ))
return successors
def a_ ( self, lowerCAmelCase__) -> list[TPosition]:
snake_case_ = node
snake_case_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
snake_case_ = current_node.parent
path.reverse()
return path
class UpperCamelCase :
def __init__( self, lowerCAmelCase__, lowerCAmelCase__) -> None:
snake_case_ = AStar(lowerCAmelCase__, lowerCAmelCase__)
snake_case_ = AStar(lowerCAmelCase__, lowerCAmelCase__)
snake_case_ = False
def a_ ( self) -> list[TPosition]:
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
snake_case_ = self.fwd_astar.open_nodes.pop(0)
snake_case_ = self.bwd_astar.open_nodes.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
lowerCAmelCase__, lowerCAmelCase__)
self.fwd_astar.closed_nodes.append(lowerCAmelCase__)
self.bwd_astar.closed_nodes.append(lowerCAmelCase__)
snake_case_ = current_bwd_node
snake_case_ = current_fwd_node
snake_case_ = {
self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase__),
self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase__),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(lowerCAmelCase__)
else:
# retrieve the best current path
snake_case_ = astar.open_nodes.pop(
astar.open_nodes.index(lowerCAmelCase__))
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(lowerCAmelCase__)
else:
astar.open_nodes.append(lowerCAmelCase__)
return [self.fwd_astar.start.pos]
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__) -> list[TPosition]:
snake_case_ = self.fwd_astar.retrace_path(lowerCAmelCase__)
snake_case_ = self.bwd_astar.retrace_path(lowerCAmelCase__)
bwd_path.pop()
bwd_path.reverse()
snake_case_ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__UpperCamelCase = (0, 0)
__UpperCamelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__UpperCamelCase = time.time()
__UpperCamelCase = AStar(init, goal)
__UpperCamelCase = a_star.search()
__UpperCamelCase = time.time() - start_time
print(F"""AStar execution time = {end_time:f} seconds""")
__UpperCamelCase = time.time()
__UpperCamelCase = BidirectionalAStar(init, goal)
__UpperCamelCase = time.time() - bd_start_time
print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 69 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
lowerCamelCase = random.Random()
def lowerCamelCase_ ( _a , _a=1.0 , _a=None , _a=None ):
"""simple docstring"""
if rng is None:
lowerCAmelCase__ : List[Any] = global_rng
lowerCAmelCase__ : Tuple = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class _a ( unittest.TestCase):
def __init__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : str=7 , _SCREAMING_SNAKE_CASE : Optional[Any]=400 , _SCREAMING_SNAKE_CASE : int=2000 , _SCREAMING_SNAKE_CASE : Optional[Any]=24 , _SCREAMING_SNAKE_CASE : Optional[int]=24 , _SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , _SCREAMING_SNAKE_CASE : str=1_6000 , _SCREAMING_SNAKE_CASE : Tuple=True , _SCREAMING_SNAKE_CASE : Tuple=True , )-> Optional[Any]:
lowerCAmelCase__ : Tuple = parent
lowerCAmelCase__ : int = batch_size
lowerCAmelCase__ : int = min_seq_length
lowerCAmelCase__ : Optional[int] = max_seq_length
lowerCAmelCase__ : Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCAmelCase__ : List[Any] = feature_size
lowerCAmelCase__ : int = num_mel_bins
lowerCAmelCase__ : Union[str, Any] = padding_value
lowerCAmelCase__ : str = sampling_rate
lowerCAmelCase__ : Any = return_attention_mask
lowerCAmelCase__ : Dict = do_normalize
def UpperCAmelCase__( self : Dict )-> int:
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__( self : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any]=False , _SCREAMING_SNAKE_CASE : Dict=False )-> Any:
def _flatten(_SCREAMING_SNAKE_CASE : List[Any] ):
return list(itertools.chain(*_SCREAMING_SNAKE_CASE ) )
if equal_length:
lowerCAmelCase__ : List[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCAmelCase__ : Any = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCAmelCase__ : List[str] = [np.asarray(_SCREAMING_SNAKE_CASE ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _a ( _lowercase , unittest.TestCase):
_a : Tuple = SpeechaTextFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__( self : Union[str, Any] )-> Any:
lowerCAmelCase__ : str = SpeechaTextFeatureExtractionTester(self )
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : str )-> Optional[int]:
self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE , axis=0 ) - 1 ) < 1E-3 ) )
def UpperCAmelCase__( self : Optional[int] )-> int:
# Tests that all call wrap to encode_plus and batch_encode_plus
lowerCAmelCase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Optional[int] = [np.asarray(_SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
# Test feature size
lowerCAmelCase__ : Dict = feature_extractor(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCAmelCase__ : str = feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
lowerCAmelCase__ : Optional[Any] = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# Test batched
lowerCAmelCase__ : int = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
lowerCAmelCase__ : Union[str, Any] = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
lowerCAmelCase__ : Optional[int] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCAmelCase__ : str = np.asarray(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
lowerCAmelCase__ : List[Any] = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
def UpperCAmelCase__( self : Any )-> Union[str, Any]:
lowerCAmelCase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Dict = ['''longest''', '''max_length''', '''do_not_pad''']
lowerCAmelCase__ : Any = [None, 16, None]
for max_length, padding in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ : Union[str, Any] = feature_extractor(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : int = inputs.input_features
lowerCAmelCase__ : Optional[int] = inputs.attention_mask
lowerCAmelCase__ : str = [np.sum(_SCREAMING_SNAKE_CASE ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase__( self : List[Any] )-> int:
lowerCAmelCase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Optional[Any] = ['''longest''', '''max_length''', '''do_not_pad''']
lowerCAmelCase__ : Optional[int] = [None, 16, None]
for max_length, padding in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ : str = feature_extractor(
_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='''np''' , return_attention_mask=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Union[str, Any] = inputs.input_features
lowerCAmelCase__ : Optional[int] = inputs.attention_mask
lowerCAmelCase__ : str = [np.sum(_SCREAMING_SNAKE_CASE ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase__( self : str )-> Optional[int]:
lowerCAmelCase__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Optional[Any] = feature_extractor(
_SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=4 , truncation=_SCREAMING_SNAKE_CASE , return_tensors='''np''' , return_attention_mask=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Union[str, Any] = inputs.input_features
lowerCAmelCase__ : Any = inputs.attention_mask
lowerCAmelCase__ : str = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def UpperCAmelCase__( self : Dict )-> Optional[int]:
lowerCAmelCase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Tuple = feature_extractor(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=4 , truncation=_SCREAMING_SNAKE_CASE , return_tensors='''np''' , return_attention_mask=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Optional[int] = inputs.input_features
lowerCAmelCase__ : Optional[Any] = inputs.attention_mask
lowerCAmelCase__ : Tuple = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
lowerCAmelCase__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowerCAmelCase__ : Optional[int] = feature_extractor(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=16 , truncation=_SCREAMING_SNAKE_CASE , return_tensors='''np''' , return_attention_mask=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Any = inputs.input_features
lowerCAmelCase__ : int = inputs.attention_mask
lowerCAmelCase__ : List[Any] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def UpperCAmelCase__( self : str )-> Union[str, Any]:
import torch
lowerCAmelCase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : Optional[Any] = np.random.rand(100 , 32 ).astype(np.floataa )
lowerCAmelCase__ : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCAmelCase__ : List[Any] = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCAmelCase__ : int = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__( self : List[str] , _SCREAMING_SNAKE_CASE : Tuple )-> List[str]:
from datasets import load_dataset
lowerCAmelCase__ : str = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
lowerCAmelCase__ : List[str] = ds.sort('''id''' ).select(range(_SCREAMING_SNAKE_CASE ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCAmelCase__( self : Optional[int] )-> int:
# fmt: off
lowerCAmelCase__ : Optional[Any] = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
lowerCAmelCase__ : str = self._load_datasamples(1 )
lowerCAmelCase__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase__ : int = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 370 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _a , _a=1_000 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCAmelCase__ : int = n - 1
lowerCAmelCase__ : Any = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowerCAmelCase__ : Optional[Any] = 0
while count < prec:
lowerCAmelCase__ : Optional[Any] = random.randint(2 , n - 1 )
lowerCAmelCase__ : List[Any] = bin_exp_mod(_a , _a , _a )
if b != 1:
lowerCAmelCase__ : Dict = True
for _ in range(_a ):
if b == n - 1:
lowerCAmelCase__ : Union[str, Any] = False
break
lowerCAmelCase__ : Tuple = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
lowerCamelCase = abs(int(input('''Enter bound : ''').strip()))
print('''Here\'s the list of primes:''')
print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 211 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
__UpperCamelCase : Dict = '\\n@inproceedings{lin-2004-rouge,\n title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n author = "Lin, Chin-Yew",\n booktitle = "Text Summarization Branches Out",\n month = jul,\n year = "2004",\n address = "Barcelona, Spain",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W04-1013",\n pages = "74--81",\n}\n'
__UpperCamelCase : int = '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'
__UpperCamelCase : Any = '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n `"rougeL"`: Longest common subsequence based scoring.\n `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric(\'rouge\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n >>> print(results["rouge1"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results["rouge1"].mid.fmeasure)\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowercase__ ( datasets.Metric):
def __A ( self : Union[str, Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def __A ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int=None , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : int=False ):
'''simple docstring'''
if rouge_types is None:
SCREAMING_SNAKE_CASE : Dict = ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
SCREAMING_SNAKE_CASE : Tuple = rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__ , use_stemmer=UpperCamelCase__ )
if use_aggregator:
SCREAMING_SNAKE_CASE : Optional[Any] = scoring.BootstrapAggregator()
else:
SCREAMING_SNAKE_CASE : int = []
for ref, pred in zip(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[str] = scorer.score(UpperCamelCase__ , UpperCamelCase__ )
if use_aggregator:
aggregator.add_scores(UpperCamelCase__ )
else:
scores.append(UpperCamelCase__ )
if use_aggregator:
SCREAMING_SNAKE_CASE : int = aggregator.aggregate()
else:
SCREAMING_SNAKE_CASE : str = {}
for key in scores[0]:
SCREAMING_SNAKE_CASE : Dict = [score[key] for score in scores]
return result
| 182 | import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
__UpperCamelCase : Any = getLogger(__name__)
__UpperCamelCase : int = 'cuda' if torch.cuda.is_available() else 'cpu'
def A ( _lowercase , _lowercase , _lowercase , _lowercase = 8 , _lowercase = DEFAULT_DEVICE , _lowercase=False , _lowercase="summarization" , _lowercase=None , **_lowercase , ):
SCREAMING_SNAKE_CASE : List[str] = Path(_lowercase ).open('''w''' , encoding='''utf-8''' )
SCREAMING_SNAKE_CASE : int = str(_lowercase )
SCREAMING_SNAKE_CASE : Any = AutoModelForSeqaSeqLM.from_pretrained(_lowercase ).to(_lowercase )
if fpaa:
SCREAMING_SNAKE_CASE : Dict = model.half()
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(_lowercase )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
SCREAMING_SNAKE_CASE : str = time.time()
# update config with task specific params
use_task_specific_params(_lowercase , _lowercase )
if prefix is None:
SCREAMING_SNAKE_CASE : Optional[int] = prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_lowercase , _lowercase ) ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [prefix + text for text in examples_chunk]
SCREAMING_SNAKE_CASE : Dict = tokenizer(_lowercase , return_tensors='''pt''' , truncation=_lowercase , padding='''longest''' ).to(_lowercase )
SCREAMING_SNAKE_CASE : str = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_lowercase , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
SCREAMING_SNAKE_CASE : Tuple = int(time.time() - start_time ) # seconds
SCREAMING_SNAKE_CASE : str = len(_lowercase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def A ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def A ( _lowercase=True ):
SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
parser.add_argument('''model_name''' , type=_lowercase , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' , type=_lowercase , help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' , type=_lowercase , help='''where to save summaries''' )
parser.add_argument('''--reference_path''' , type=_lowercase , required=_lowercase , help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' , type=_lowercase , required=_lowercase , default='''metrics.json''' , help='''where to save metrics''' )
parser.add_argument('''--device''' , type=_lowercase , required=_lowercase , default=_lowercase , help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' , type=_lowercase , required=_lowercase , default=_lowercase , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' , type=_lowercase , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=_lowercase , default=8 , required=_lowercase , help='''batch size''' )
parser.add_argument(
'''--n_obs''' , type=_lowercase , default=-1 , required=_lowercase , help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--dump-args''' , action='''store_true''' , help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' , nargs='''?''' , type=_lowercase , const=datetime_now() , help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_known_args()
SCREAMING_SNAKE_CASE : Optional[Any] = parse_numeric_n_bool_cl_kwargs(_lowercase )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
SCREAMING_SNAKE_CASE : Any = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_lowercase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
SCREAMING_SNAKE_CASE : List[str] = generate_summaries_or_translations(
_lowercase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_lowercase , )
if args.reference_path is None:
return {}
# Compute scores
SCREAMING_SNAKE_CASE : Dict = calculate_bleu if '''translation''' in args.task else calculate_rouge
SCREAMING_SNAKE_CASE : Union[str, Any] = [x.rstrip() for x in open(args.save_path ).readlines()]
SCREAMING_SNAKE_CASE : Optional[int] = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowercase )]
SCREAMING_SNAKE_CASE : dict = score_fn(_lowercase , _lowercase )
scores.update(_lowercase )
if args.dump_args:
scores.update(_lowercase )
if args.info:
SCREAMING_SNAKE_CASE : Tuple = args.info
if verbose:
print(_lowercase )
if args.score_path is not None:
json.dump(_lowercase , open(args.score_path , '''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 182 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__UpperCAmelCase = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def A__ ( __lowerCamelCase = "mumbai" ):
SCREAMING_SNAKE_CASE_ = BeautifulSoup(requests.get(url + location ).content, '''html.parser''' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('''div''', attrs={'''data-tn-component''': '''organicJob'''} ):
SCREAMING_SNAKE_CASE_ = job.find('''a''', attrs={'''data-tn-element''': '''jobTitle'''} ).text.strip()
SCREAMING_SNAKE_CASE_ = job.find('''span''', {'''class''': '''company'''} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(F"""Job {i:>2} is {job[0]} at {job[1]}""")
| 257 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _UpperCamelCase ( self ) -> List[str]:
SCREAMING_SNAKE_CASE_ = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained('''google/mt5-small''' )
SCREAMING_SNAKE_CASE_ = tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
SCREAMING_SNAKE_CASE_ = tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
SCREAMING_SNAKE_CASE_ = shift_tokens_right(_A , model.config.pad_token_id , model.config.decoder_start_token_id )
SCREAMING_SNAKE_CASE_ = model(_A , decoder_input_ids=_A ).logits
SCREAMING_SNAKE_CASE_ = optax.softmax_cross_entropy(_A , onehot(_A , logits.shape[-1] ) ).mean()
SCREAMING_SNAKE_CASE_ = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE_ = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 257 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , ) -> List[Any]:
UpperCamelCase__ : int = {}
if train_file is not None:
UpperCamelCase__ : str = [train_file]
if eval_file is not None:
UpperCamelCase__ : Any = [eval_file]
if test_file is not None:
UpperCamelCase__ : Dict = [test_file]
UpperCamelCase__ : str = datasets.load_dataset("csv" , data_files=__lowerCAmelCase )
UpperCamelCase__ : List[str] = list(ds[list(files.keys() )[0]].features.keys() )
UpperCamelCase__ : List[Any] = features_name.pop(__lowerCAmelCase )
UpperCamelCase__ : Dict = list(set(ds[list(files.keys() )[0]][label_name] ) )
UpperCamelCase__ : List[Any] = {label: i for i, label in enumerate(__lowerCAmelCase )}
UpperCamelCase__ : str = tokenizer.model_input_names
UpperCamelCase__ : Union[str, Any] = {}
if len(__lowerCAmelCase ) == 1:
for k in files.keys():
UpperCamelCase__ : Optional[Any] = ds[k].map(
lambda __lowerCAmelCase : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , padding="max_length" ) , batched=__lowerCAmelCase , )
elif len(__lowerCAmelCase ) == 2:
for k in files.keys():
UpperCamelCase__ : Any = ds[k].map(
lambda __lowerCAmelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , padding="max_length" , ) , batched=__lowerCAmelCase , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
UpperCamelCase__ : Tuple = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : int = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
UpperCamelCase__ : List[Any] = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : str = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
UpperCamelCase__ : Dict = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : Union[str, Any] = labelaid[ex[label_name]]
yield (d, label)
UpperCamelCase__ : Optional[Any] = (
tf.data.Dataset.from_generator(
__lowerCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
UpperCamelCase__ : Union[str, Any] = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
UpperCamelCase__ : Optional[Any] = (
tf.data.Dataset.from_generator(
__lowerCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
UpperCamelCase__ : Tuple = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
UpperCamelCase__ : int = (
tf.data.Dataset.from_generator(
__lowerCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
UpperCamelCase__ : Optional[int] = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
lowerCamelCase : int =logging.getLogger(__name__)
@dataclass
class __a :
_lowerCAmelCase : int = field(metadata={'''help''': '''Which column contains the label'''} )
_lowerCAmelCase : str = field(default=A__ , metadata={'''help''': '''The path of the training file'''} )
_lowerCAmelCase : Optional[str] = field(default=A__ , metadata={'''help''': '''The path of the development file'''} )
_lowerCAmelCase : Optional[str] = field(default=A__ , metadata={'''help''': '''The path of the test file'''} )
_lowerCAmelCase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowerCAmelCase : bool = field(
default=A__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
@dataclass
class __a :
_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 : bool = field(default=A__ , metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_lowerCAmelCase : Optional[str] = field(
default=A__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase__ : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(
f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
f'16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : str = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=__lowerCAmelCase , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
UpperCamelCase__ : List[str] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(__lowerCAmelCase ) , labelaid=__lowerCAmelCase , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="text-classification" , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
UpperCamelCase__ : List[str] = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(".bin" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , )
def compute_metrics(__lowerCAmelCase ) -> Dict:
UpperCamelCase__ : List[str] = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
UpperCamelCase__ : List[Any] = TFTrainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=__lowerCAmelCase , eval_dataset=__lowerCAmelCase , compute_metrics=__lowerCAmelCase , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCamelCase__ : Dict = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase__ : Optional[int] = trainer.evaluate()
UpperCamelCase__ : Union[str, Any] = os.path.join(training_args.output_dir , "eval_results.txt" )
with open(__lowerCAmelCase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(f' {key} = {value}' )
writer.write(f'{key} = {value}\n' )
results.update(__lowerCAmelCase )
return results
if __name__ == "__main__":
main() | 189 |
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
lowerCamelCase : Optional[Any] =logging.get_logger(__name__)
lowerCamelCase : Optional[int] ={
'''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__ ):
_lowerCAmelCase : Optional[int] = '''owlvit_text_model'''
def __init__( self : Any , SCREAMING_SNAKE_CASE : Union[str, Any]=4_94_08 , SCREAMING_SNAKE_CASE : List[str]=5_12 , SCREAMING_SNAKE_CASE : List[Any]=20_48 , SCREAMING_SNAKE_CASE : Any=12 , SCREAMING_SNAKE_CASE : Any=8 , SCREAMING_SNAKE_CASE : Dict=16 , SCREAMING_SNAKE_CASE : Union[str, Any]="quick_gelu" , SCREAMING_SNAKE_CASE : List[str]=1e-5 , SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE : Any=0.0_2 , SCREAMING_SNAKE_CASE : int=1.0 , SCREAMING_SNAKE_CASE : Any=0 , SCREAMING_SNAKE_CASE : int=4_94_06 , SCREAMING_SNAKE_CASE : List[str]=4_94_07 , **SCREAMING_SNAKE_CASE : Optional[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = vocab_size
UpperCamelCase__ : int = hidden_size
UpperCamelCase__ : List[str] = intermediate_size
UpperCamelCase__ : Tuple = num_hidden_layers
UpperCamelCase__ : str = num_attention_heads
UpperCamelCase__ : Any = max_position_embeddings
UpperCamelCase__ : List[Any] = hidden_act
UpperCamelCase__ : str = layer_norm_eps
UpperCamelCase__ : List[Any] = attention_dropout
UpperCamelCase__ : Tuple = initializer_range
UpperCamelCase__ : Optional[Any] = initializer_factor
@classmethod
def __lowercase ( cls : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Any = cls.get_config_dict(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCamelCase__ : Dict = 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(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
class __a ( A__ ):
_lowerCAmelCase : str = '''owlvit_vision_model'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : str=7_68 , SCREAMING_SNAKE_CASE : Dict=30_72 , SCREAMING_SNAKE_CASE : int=12 , SCREAMING_SNAKE_CASE : Union[str, Any]=12 , SCREAMING_SNAKE_CASE : Dict=3 , SCREAMING_SNAKE_CASE : Union[str, Any]=7_68 , SCREAMING_SNAKE_CASE : Optional[int]=32 , SCREAMING_SNAKE_CASE : Dict="quick_gelu" , SCREAMING_SNAKE_CASE : Optional[Any]=1e-5 , SCREAMING_SNAKE_CASE : List[str]=0.0 , SCREAMING_SNAKE_CASE : Dict=0.0_2 , SCREAMING_SNAKE_CASE : Optional[int]=1.0 , **SCREAMING_SNAKE_CASE : Tuple , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = hidden_size
UpperCamelCase__ : str = intermediate_size
UpperCamelCase__ : Any = num_hidden_layers
UpperCamelCase__ : str = num_attention_heads
UpperCamelCase__ : int = num_channels
UpperCamelCase__ : Union[str, Any] = image_size
UpperCamelCase__ : List[Any] = patch_size
UpperCamelCase__ : Tuple = hidden_act
UpperCamelCase__ : Optional[int] = layer_norm_eps
UpperCamelCase__ : Optional[Any] = attention_dropout
UpperCamelCase__ : Dict = initializer_range
UpperCamelCase__ : int = initializer_factor
@classmethod
def __lowercase ( cls : Dict , SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : List[Any] = cls.get_config_dict(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCamelCase__ : List[str] = 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(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
class __a ( A__ ):
_lowerCAmelCase : str = '''owlvit'''
_lowerCAmelCase : Tuple = True
def __init__( self : Any , SCREAMING_SNAKE_CASE : Optional[Any]=None , SCREAMING_SNAKE_CASE : int=None , SCREAMING_SNAKE_CASE : str=5_12 , SCREAMING_SNAKE_CASE : Any=2.6_5_9_2 , SCREAMING_SNAKE_CASE : Union[str, Any]=True , **SCREAMING_SNAKE_CASE : List[Any] , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
if text_config is None:
UpperCamelCase__ : str = {}
logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )
if vision_config is None:
UpperCamelCase__ : List[str] = {}
logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )
UpperCamelCase__ : Dict = OwlViTTextConfig(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = OwlViTVisionConfig(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = projection_dim
UpperCamelCase__ : Union[str, Any] = logit_scale_init_value
UpperCamelCase__ : int = return_dict
UpperCamelCase__ : Tuple = 1.0
@classmethod
def __lowercase ( cls : Any , SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Dict = cls.get_config_dict(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
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(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@classmethod
def __lowercase ( cls : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , **SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = {}
UpperCamelCase__ : Union[str, Any] = text_config
UpperCamelCase__ : Optional[int] = vision_config
return cls.from_dict(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCamelCase__ : Union[str, Any] = self.text_config.to_dict()
UpperCamelCase__ : List[str] = self.vision_config.to_dict()
UpperCamelCase__ : Optional[int] = self.__class__.model_type
return output
class __a ( A__ ):
@property
def __lowercase ( self : Any ):
'''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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
] )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return 1e-4
def __lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE : "ProcessorMixin" , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = super().generate_dummy_inputs(
processor.tokenizer , batch_size=SCREAMING_SNAKE_CASE , seq_length=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE )
return {**text_input_dict, **image_input_dict}
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
return 14 | 189 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
@slow
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ = FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
A_ = AutoTokenizer.from_pretrained("""xlm-roberta-base""" )
A_ = """The dog is cute and lives in the garden house"""
A_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
A_ = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
A_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
A_ = model(UpperCamelCase__ )["""last_hidden_state"""]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 351 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = str(id_ )
A_ = None
A_ = None
A_ = []
A_ = {} # {vertex:distance}
def __lt__( self , UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.key < other.key
def __repr__( self ) -> Dict:
'''simple docstring'''
return self.id
def snake_case_ ( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
self.neighbors.append(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
A_ = weight
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[int]:
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1], UpperCAmelCase__ )
graph[b - 1].add_edge(graph[a - 1], UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> list:
A_ = []
for u in graph:
A_ = math.inf
A_ = None
A_ = 0
A_ = graph[:]
while q:
A_ = min(UpperCAmelCase__ )
q.remove(UpperCAmelCase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
A_ = u
A_ = u.edges[v.id]
for i in range(1, len(UpperCAmelCase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Iterator[tuple]:
for u in graph:
A_ = math.inf
A_ = None
A_ = 0
A_ = list(UpperCAmelCase__ )
hq.heapify(UpperCAmelCase__ )
while h:
A_ = hq.heappop(UpperCAmelCase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
A_ = u
A_ = u.edges[v.id]
hq.heapify(UpperCAmelCase__ )
for i in range(1, len(UpperCAmelCase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase__ ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 101 | 0 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
__snake_case = {
"""b0""": {
"""hidden_dim""": 12_80,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 2_24,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 12_80,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 2_40,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 14_08,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 2_60,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 15_36,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 3_00,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 17_92,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 3_80,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 20_48,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 4_56,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 23_04,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 5_28,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 25_60,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 6_00,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def _A ( SCREAMING_SNAKE_CASE__ : List[str] ):
UpperCamelCase :int = EfficientNetConfig()
UpperCamelCase :Optional[Any] = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCamelCase :Dict = CONFIG_MAP[model_name]['''width_coef''']
UpperCamelCase :int = CONFIG_MAP[model_name]['''depth_coef''']
UpperCamelCase :Optional[int] = CONFIG_MAP[model_name]['''image_size''']
UpperCamelCase :Optional[int] = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCamelCase :int = CONFIG_MAP[model_name]['''dw_padding''']
UpperCamelCase :str = '''huggingface/label-files'''
UpperCamelCase :Optional[int] = '''imagenet-1k-id2label.json'''
UpperCamelCase :List[Any] = 1000
UpperCamelCase :Optional[int] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='''dataset''' ) , '''r''' ) )
UpperCamelCase :str = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
UpperCamelCase :Dict = idalabel
UpperCamelCase :int = {v: k for k, v in idalabel.items()}
return config
def _A ( ):
UpperCamelCase :Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase :Dict = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
def _A ( SCREAMING_SNAKE_CASE__ : Dict ):
UpperCamelCase :str = CONFIG_MAP[model_name]['''image_size''']
UpperCamelCase :List[Any] = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.4_85, 0.4_56, 0.4_06] , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] , do_center_crop=SCREAMING_SNAKE_CASE__ , )
return preprocessor
def _A ( SCREAMING_SNAKE_CASE__ : Any ):
UpperCamelCase :Union[str, Any] = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCamelCase :Dict = sorted(set(SCREAMING_SNAKE_CASE__ ) )
UpperCamelCase :Tuple = len(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[Any] = {b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__ , range(SCREAMING_SNAKE_CASE__ ) )}
UpperCamelCase :List[Any] = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCamelCase :str = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCamelCase :Union[str, Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCamelCase :Optional[int] = '''efficientnet.''' + item[1]
UpperCamelCase :List[str] = '''classifier.weight'''
UpperCamelCase :Dict = '''classifier.bias'''
return key_mapping
def _A ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCamelCase :List[Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCamelCase :Optional[Any] = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCamelCase :Dict = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCamelCase :str = torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
else:
UpperCamelCase :int = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def _A ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict ):
UpperCamelCase :Any = model_classes[model_name](
include_top=SCREAMING_SNAKE_CASE__ , weights='''imagenet''' , input_tensor=SCREAMING_SNAKE_CASE__ , input_shape=SCREAMING_SNAKE_CASE__ , pooling=SCREAMING_SNAKE_CASE__ , classes=1000 , classifier_activation='''softmax''' , )
UpperCamelCase :Dict = original_model.trainable_variables
UpperCamelCase :List[Any] = original_model.non_trainable_variables
UpperCamelCase :Tuple = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCamelCase :Union[str, Any] = param.numpy()
UpperCamelCase :Union[str, Any] = list(tf_params.keys() )
# Load HuggingFace model
UpperCamelCase :List[str] = get_efficientnet_config(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[str] = EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__ ).eval()
UpperCamelCase :Union[str, Any] = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCamelCase :Optional[int] = rename_keys(SCREAMING_SNAKE_CASE__ )
replace_params(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Initialize preprocessor and preprocess input image
UpperCamelCase :Tuple = convert_image_processor(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[Any] = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCamelCase :List[str] = hf_model(**SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Union[str, Any] = outputs.logits.detach().numpy()
# Original model inference
UpperCamelCase :Optional[int] = False
UpperCamelCase :Optional[Any] = CONFIG_MAP[model_name]['''image_size''']
UpperCamelCase :Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCamelCase :Optional[int] = image.img_to_array(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Union[str, Any] = np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=0 )
UpperCamelCase :Any = original_model.predict(SCREAMING_SNAKE_CASE__ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
os.mkdir(SCREAMING_SNAKE_CASE__ )
# Save converted model and image processor
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
UpperCamelCase :Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )
hf_model.push_to_hub(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
__snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 259 |
from __future__ import annotations
from typing import Any
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : int ):
if index == len(SCREAMING_SNAKE_CASE__ ):
print(SCREAMING_SNAKE_CASE__ )
return
create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
__snake_case = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["""A""", """B""", """C"""])
generate_all_subsequences(seq)
| 259 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( A__, A__, A__, A__=1_0_2_4 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = [], []
SCREAMING_SNAKE_CASE_ : Optional[Any] = list(zip(__A, __A ) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = sorted_examples[0]
def is_too_big(A__ ):
return tok(__A, return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
SCREAMING_SNAKE_CASE_ : List[Any] = new_src + ' ' + src
SCREAMING_SNAKE_CASE_ : Dict = new_tgt + ' ' + tgt
if is_too_big(__A ) or is_too_big(__A ): # cant fit, finalize example
finished_src.append(__A )
finished_tgt.append(__A )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] = src, tgt
else: # can fit, keep adding
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__A )
finished_tgt.append(__A )
return finished_src, finished_tgt
def a__ ( A__, A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : Dict = Path(__A )
save_path.mkdir(exist_ok=__A )
for split in ["train"]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
SCREAMING_SNAKE_CASE_ : int = [x.rstrip() for x in Path(__A ).open().readlines()]
SCREAMING_SNAKE_CASE_ : Dict = [x.rstrip() for x in Path(__A ).open().readlines()]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = pack_examples(__A, __A, __A, __A )
print(F'''packed {split} split from {len(__A )} examples -> {len(__A )}.''' )
Path(save_path / F'''{split}.source''' ).open('w' ).write('\n'.join(__A ) )
Path(save_path / F'''{split}.target''' ).open('w' ).write('\n'.join(__A ) )
for split in ["val", "test"]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(__A, save_path / F'''{split}.source''' )
shutil.copyfile(__A, save_path / F'''{split}.target''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Tuple = argparse.ArgumentParser()
parser.add_argument('--tok_name', type=__A, help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len', type=__A, default=1_2_8 )
parser.add_argument('--data_dir', type=__A )
parser.add_argument('--save_path', type=__A )
SCREAMING_SNAKE_CASE_ : Any = parser.parse_args()
SCREAMING_SNAKE_CASE_ : Any = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__A, Path(args.data_dir ), args.max_seq_len, args.save_path )
if __name__ == "__main__":
packer_cli()
| 357 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=1_8 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = size if size is not None else {'shortest_edge': 1_8}
SCREAMING_SNAKE_CASE_ : Optional[Any] = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
SCREAMING_SNAKE_CASE_ : int = parent
SCREAMING_SNAKE_CASE_ : str = batch_size
SCREAMING_SNAKE_CASE_ : str = num_channels
SCREAMING_SNAKE_CASE_ : List[Any] = image_size
SCREAMING_SNAKE_CASE_ : str = min_resolution
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max_resolution
SCREAMING_SNAKE_CASE_ : int = do_resize
SCREAMING_SNAKE_CASE_ : List[Any] = size
SCREAMING_SNAKE_CASE_ : Optional[int] = do_center_crop
SCREAMING_SNAKE_CASE_ : Any = crop_size
SCREAMING_SNAKE_CASE_ : List[Any] = do_normalize
SCREAMING_SNAKE_CASE_ : List[str] = image_mean
SCREAMING_SNAKE_CASE_ : Optional[int] = image_std
def UpperCamelCase__ ( self ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowercase (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase = LevitImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = LevitImageProcessingTester(self )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_std' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'size' ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 1_8} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
SCREAMING_SNAKE_CASE_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE_ : str = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : str = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE_ : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : Optional[int] = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE_ : Dict = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 162 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
a_ =None
a_ =BloomTokenizerFast
a_ =BloomTokenizerFast
a_ =True
a_ =False
a_ ="""tokenizer_file"""
a_ ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
def _lowercase ( self : List[Any] ) -> int:
super().setUp()
__lowerCamelCase : List[Any] = BloomTokenizerFast.from_pretrained('bigscience/tokenizer' )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : List[str] , **_a : Optional[Any] ) -> Tuple:
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _lowercase ( self : Dict ) -> int:
__lowerCamelCase : int = self.get_rust_tokenizer()
__lowerCamelCase : Any = ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
__lowerCamelCase : Union[str, Any] = [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]
__lowerCamelCase : Union[str, Any] = tokenizer.batch_encode_plus(_a )['input_ids']
self.assertListEqual(_a , _a )
__lowerCamelCase : List[str] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _lowercase ( self : Optional[Any] , _a : Union[str, Any]=6 ) -> str:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
__lowerCamelCase : List[Any] = 'This is a simple input'
__lowerCamelCase : str = ['This is a simple input 1', 'This is a simple input 2']
__lowerCamelCase : str = ('This is a simple input', 'This is a pair')
__lowerCamelCase : Optional[int] = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
try:
tokenizer_r.encode(_a , max_length=_a )
tokenizer_r.encode_plus(_a , max_length=_a )
tokenizer_r.batch_encode_plus(_a , max_length=_a )
tokenizer_r.encode(_a , max_length=_a )
tokenizer_r.batch_encode_plus(_a , max_length=_a )
except ValueError:
self.fail('Bloom Tokenizer should be able to deal with padding' )
__lowerCamelCase : Optional[Any] = None # Hotfixing padding = None
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding='max_length' )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding='max_length' )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding='max_length' , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding='max_length' )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding='max_length' )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding='max_length' , )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
__lowerCamelCase : Any = self.get_rust_tokenizer()
__lowerCamelCase : List[Any] = load_dataset('xnli' , 'all_languages' , split='test' , streaming=_a )
__lowerCamelCase : Union[str, Any] = next(iter(_a ) )['premise'] # pick up one data
__lowerCamelCase : Any = list(sample_data.values() )
__lowerCamelCase : Optional[int] = list(map(tokenizer.encode , _a ) )
__lowerCamelCase : Optional[Any] = [tokenizer.decode(_a , clean_up_tokenization_spaces=_a ) for x in output_tokens]
self.assertListEqual(_a , _a )
def _lowercase ( self : Dict ) -> Optional[int]:
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 208 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase=True ,_lowerCAmelCase=2 ) -> List[str]:
from .. import __version__
__lowerCamelCase : Any = take_from
__lowerCamelCase : Optional[int] = ()
if not isinstance(args[0] ,_lowerCAmelCase ):
__lowerCamelCase : Optional[Any] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_lowerCAmelCase ).base_version ) >= version.parse(_lowerCAmelCase ):
raise ValueError(
F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
F' version {__version__} is >= {version_name}' )
__lowerCamelCase : Union[str, Any] = None
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_lowerCAmelCase ),)
__lowerCamelCase : Optional[Any] = F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
elif hasattr(_lowerCAmelCase ,_lowerCAmelCase ):
values += (getattr(_lowerCAmelCase ,_lowerCAmelCase ),)
__lowerCamelCase : List[str] = F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
elif deprecated_kwargs is None:
__lowerCamelCase : Optional[Any] = F'`{attribute}` is deprecated and will be removed in version {version_name}.'
if warning is not None:
__lowerCamelCase : Optional[int] = warning + ' ' if standard_warn else ''
warnings.warn(warning + message ,_lowerCAmelCase ,stacklevel=_lowerCAmelCase )
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) and len(_lowerCAmelCase ) > 0:
__lowerCamelCase : Optional[Any] = inspect.getouterframes(inspect.currentframe() )[1]
__lowerCamelCase : List[str] = call_frame.filename
__lowerCamelCase : int = call_frame.lineno
__lowerCamelCase : Union[str, Any] = call_frame.function
__lowerCamelCase ,__lowerCamelCase : Union[str, Any] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`' )
if len(_lowerCAmelCase ) == 0:
return
elif len(_lowerCAmelCase ) == 1:
return values[0]
return values
| 208 | 1 |
import mpmath # for roots of unity
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__( self : str , lowerCAmelCase__ : int=None , lowerCAmelCase__ : int=None):
# Input as list
SCREAMING_SNAKE_CASE_: int = list(poly_a or [0])[:]
SCREAMING_SNAKE_CASE_: Tuple = list(poly_b or [0])[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
SCREAMING_SNAKE_CASE_: Tuple = len(self.polyA)
while self.polyB[-1] == 0:
self.polyB.pop()
SCREAMING_SNAKE_CASE_: Any = len(self.polyB)
# Add 0 to make lengths equal a power of 2
SCREAMING_SNAKE_CASE_: Tuple = 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
SCREAMING_SNAKE_CASE_: Tuple = complex(mpmath.root(x=1 , n=self.c_max_length , k=1))
# The product
SCREAMING_SNAKE_CASE_: Union[str, Any] = self.__multiply()
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : int):
SCREAMING_SNAKE_CASE_: Dict = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
# Corner case
if len(lowerCamelCase__) <= 1:
return dft[0]
#
SCREAMING_SNAKE_CASE_: Optional[Any] = self.c_max_length // 2
while next_ncol > 0:
SCREAMING_SNAKE_CASE_: Any = [[] for i in range(lowerCamelCase__)]
SCREAMING_SNAKE_CASE_: Optional[Any] = self.root**next_ncol
# First half of next step
SCREAMING_SNAKE_CASE_: Optional[Any] = 1
for j in range(self.c_max_length // (next_ncol * 2)):
for i in range(lowerCamelCase__):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
current_root *= root
# Second half of next step
SCREAMING_SNAKE_CASE_: Optional[Any] = 1
for j in range(self.c_max_length // (next_ncol * 2)):
for i in range(lowerCamelCase__):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
current_root *= root
# Update
SCREAMING_SNAKE_CASE_: Optional[int] = new_dft
SCREAMING_SNAKE_CASE_: Any = next_ncol // 2
return dft[0]
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Optional[Any] = self.__dft("A")
SCREAMING_SNAKE_CASE_: Tuple = self.__dft("B")
SCREAMING_SNAKE_CASE_: Any = [[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
SCREAMING_SNAKE_CASE_: Union[str, Any] = 2
while next_ncol <= self.c_max_length:
SCREAMING_SNAKE_CASE_: Union[str, Any] = [[] for i in range(lowerCamelCase__)]
SCREAMING_SNAKE_CASE_: Any = self.root ** (next_ncol // 2)
SCREAMING_SNAKE_CASE_: int = 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
SCREAMING_SNAKE_CASE_: List[str] = new_inverse_c
next_ncol *= 2
# Unpack
SCREAMING_SNAKE_CASE_: List[Any] = [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 : List[Any]):
SCREAMING_SNAKE_CASE_: str = "A = " + " + ".join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A]))
SCREAMING_SNAKE_CASE_: List[str] = "B = " + " + ".join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B]))
SCREAMING_SNAKE_CASE_: Tuple = "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()
| 357 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : Any = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
lowerCAmelCase : str = {
"""b0""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Dict = EfficientNetConfig()
SCREAMING_SNAKE_CASE_: Any = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE_: Optional[Any] = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE_: List[Any] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE_: Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE_: Optional[int] = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE_: Dict = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE_: str = "huggingface/label-files"
SCREAMING_SNAKE_CASE_: str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE_: int = 10_00
SCREAMING_SNAKE_CASE_: int = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_: int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_: Any = idalabel
SCREAMING_SNAKE_CASE_: Optional[Any] = {v: k for k, v in idalabel.items()}
return config
def A_ ( ):
SCREAMING_SNAKE_CASE_: Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_: int = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE_: Optional[Any] = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=_UpperCAmelCase , )
return preprocessor
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE_: Optional[Any] = sorted(set(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: int = len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] = {b: str(_UpperCAmelCase ) for b, i in zip(_UpperCAmelCase , range(_UpperCAmelCase ) )}
SCREAMING_SNAKE_CASE_: List[Any] = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE_: List[str] = block_name_mapping[b]
rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE_: Optional[Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE_: str = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE_: List[str] = "classifier.weight"
SCREAMING_SNAKE_CASE_: Optional[Any] = "classifier.bias"
return key_mapping
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE_: List[str] = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE_: str = torch.from_numpy(_UpperCAmelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE_: int = torch.from_numpy(_UpperCAmelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE_: Tuple = torch.from_numpy(np.transpose(_UpperCAmelCase ) )
else:
SCREAMING_SNAKE_CASE_: List[str] = torch.from_numpy(_UpperCAmelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_UpperCAmelCase )
@torch.no_grad()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = model_classes[model_name](
include_top=_UpperCAmelCase , weights="imagenet" , input_tensor=_UpperCAmelCase , input_shape=_UpperCAmelCase , pooling=_UpperCAmelCase , classes=10_00 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE_: Tuple = original_model.trainable_variables
SCREAMING_SNAKE_CASE_: Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE_: List[Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE_: str = param.numpy()
SCREAMING_SNAKE_CASE_: Union[str, Any] = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE_: Any = get_efficientnet_config(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[str] = EfficientNetForImageClassification(_UpperCAmelCase ).eval()
SCREAMING_SNAKE_CASE_: str = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE_: Tuple = rename_keys(_UpperCAmelCase )
replace_params(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE_: Optional[Any] = convert_image_processor(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_: Union[str, Any] = hf_model(**_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Any = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE_: Dict = False
SCREAMING_SNAKE_CASE_: Optional[int] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE_: int = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE_: Tuple = image.img_to_array(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[str] = np.expand_dims(_UpperCAmelCase , axis=0 )
SCREAMING_SNAKE_CASE_: str = original_model.predict(_UpperCAmelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(_UpperCAmelCase ):
os.mkdir(_UpperCAmelCase )
# Save converted model and image processor
hf_model.save_pretrained(_UpperCAmelCase )
preprocessor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
# Push model and image processor to hub
print(f"Pushing converted {model_name} to the hub..." )
SCREAMING_SNAKE_CASE_: Optional[Any] = f"efficientnet-{model_name}"
preprocessor.push_to_hub(_UpperCAmelCase )
hf_model.push_to_hub(_UpperCAmelCase )
if __name__ == "__main__":
lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
lowerCAmelCase : Any = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 127 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''vit'''
def __init__(self , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase=2_2_4 , UpperCAmelCase=1_6 , UpperCAmelCase=3 , UpperCAmelCase=True , UpperCAmelCase=1_6 , **UpperCAmelCase , ) -> List[str]:
super().__init__(**UpperCAmelCase )
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =initializer_range
_lowercase =layer_norm_eps
_lowercase =image_size
_lowercase =patch_size
_lowercase =num_channels
_lowercase =qkv_bias
_lowercase =encoder_stride
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = version.parse('''1.11''')
@property
def __A (self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __A (self ) -> float:
return 1e-4
| 5 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_A ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
lowerCAmelCase_ = QuantumRegister(_A , '''qr''' )
lowerCAmelCase_ = ClassicalRegister(_A , '''cr''' )
lowerCAmelCase_ = QuantumCircuit(_A , _A )
lowerCAmelCase_ = number_of_qubits
for i in range(_A ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_A ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _A , _A )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_A , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_A , _A )
# simulate with 10000 shots
lowerCAmelCase_ = Aer.get_backend('''qasm_simulator''' )
lowerCAmelCase_ = execute(_A , _A , shots=10000 )
return job.result().get_counts(_A )
if __name__ == "__main__":
print(
f"Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}"
)
| 278 | 0 |
from scipy.stats import pearsonr
import datasets
UpperCamelCase = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
UpperCamelCase = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
UpperCamelCase = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class snake_case_ ( datasets.Metric ):
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"] , )
def __UpperCamelCase ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Tuple=False ) -> Optional[Any]:
if return_pvalue:
lowercase__ : List[Any] = pearsonr(lowercase_ , lowercase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(lowercase_ , lowercase_ )[0] )}
| 366 | import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int=False):
try:
lowercase__ : str = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
lowercase__ : Union[str, Any] = default
else:
# KEY is set, convert it to True or False.
try:
lowercase__ : Union[str, Any] = strtobool(_lowerCamelCase)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'''If set, {key} must be yes or no.''')
return _value
UpperCamelCase = parse_flag_from_env('''RUN_SLOW''', default=False)
def lowercase_ ( _lowerCamelCase : int):
return unittest.skip("Test was skipped")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Tuple):
return unittest.skipUnless(_run_slow_tests , "test is slow")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Dict):
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : int):
return unittest.skipUnless(is_xpu_available() , "test requires a XPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(is_tpu_available() , "test requires TPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[Any]):
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : int):
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : str):
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Any):
return unittest.skipUnless(is_torch_version(">=" , "1.12.0") , "test requires torch version >= 1.12.0")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None):
if test_case is None:
return partial(_lowerCamelCase , version=_lowerCamelCase)
return unittest.skipUnless(is_torch_version(">=" , _lowerCamelCase) , f'''test requires torch version >= {version}''')(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[Any]):
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : int):
return unittest.skipUnless(is_wandb_available() , "test requires wandb")(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml")(_lowerCamelCase)
UpperCamelCase = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def lowercase_ ( _lowerCamelCase : Any):
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_lowerCamelCase)
class snake_case_ ( unittest.TestCase ):
__A : int = True
@classmethod
def __UpperCamelCase ( cls : str ) -> str:
lowercase__ : str = tempfile.mkdtemp()
@classmethod
def __UpperCamelCase ( cls : List[str] ) -> Optional[Any]:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def __UpperCamelCase ( self : str ) -> Optional[int]:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(lowercase_ )
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : List[Any] , lowercase_ : Union[mock.Mock, List[mock.Mock]] ) -> str:
lowercase__ : Tuple = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def lowercase_ ( _lowerCamelCase : int):
lowercase__ : Tuple = AcceleratorState()
lowercase__ : Optional[int] = tensor[None].clone().to(state.device)
lowercase__ : Optional[int] = gather(_lowerCamelCase).cpu()
lowercase__ : Optional[Any] = tensor[0].cpu()
for i in range(tensors.shape[0]):
if not torch.equal(tensors[i] , _lowerCamelCase):
return False
return True
class snake_case_ :
def __init__( self : str , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int ) -> Union[str, Any]:
lowercase__ : int = returncode
lowercase__ : Dict = stdout
lowercase__ : List[Any] = stderr
async def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str):
while True:
lowercase__ : int = await stream.readline()
if line:
callback(_lowerCamelCase)
else:
break
async def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Tuple=False , _lowerCamelCase : str=False):
if echo:
print("\nRunning: " , " ".join(_lowerCamelCase))
lowercase__ : str = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_lowerCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCamelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
lowercase__ : Tuple = []
lowercase__ : List[Any] = []
def tee(_lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : int , _lowerCamelCase : Optional[int]=""):
lowercase__ : Optional[int] = line.decode("utf-8").rstrip()
sink.append(_lowerCamelCase)
if not quiet:
print(_lowerCamelCase , _lowerCamelCase , file=_lowerCamelCase)
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stdout , label="stdout:"))),
asyncio.create_task(_read_stream(p.stderr , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stderr , label="stderr:"))),
] , timeout=_lowerCamelCase , )
return _RunOutput(await p.wait() , _lowerCamelCase , _lowerCamelCase)
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[str]=180 , _lowerCamelCase : Dict=False , _lowerCamelCase : Dict=True):
lowercase__ : Optional[Any] = asyncio.get_event_loop()
lowercase__ : List[Any] = loop.run_until_complete(
_stream_subprocess(_lowerCamelCase , env=_lowerCamelCase , stdin=_lowerCamelCase , timeout=_lowerCamelCase , quiet=_lowerCamelCase , echo=_lowerCamelCase))
lowercase__ : str = " ".join(_lowerCamelCase)
if result.returncode > 0:
lowercase__ : Dict = "\n".join(result.stderr)
raise RuntimeError(
f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
f'''The combined stderr from workers follows:\n{stderr}''')
return result
class snake_case_ ( __A ):
pass
def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any=False):
try:
lowercase__ : Optional[int] = subprocess.check_output(_lowerCamelCase , stderr=subprocess.STDOUT)
if return_stdout:
if hasattr(_lowerCamelCase , "decode"):
lowercase__ : Optional[Any] = output.decode("utf-8")
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f'''Command `{" ".join(_lowerCamelCase)}` failed with the following error:\n\n{e.output.decode()}''') from e
| 333 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 16 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ = {
'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 : str = "tapas"
def __init__( self : List[Any] ,_snake_case : Dict=30_522 ,_snake_case : Union[str, Any]=768 ,_snake_case : int=12 ,_snake_case : Union[str, Any]=12 ,_snake_case : Union[str, Any]=3_072 ,_snake_case : List[Any]="gelu" ,_snake_case : Optional[int]=0.1 ,_snake_case : Tuple=0.1 ,_snake_case : List[Any]=1_024 ,_snake_case : Any=[3, 256, 256, 2, 256, 256, 10] ,_snake_case : List[Any]=0.02 ,_snake_case : Union[str, Any]=1e-12 ,_snake_case : str=0 ,_snake_case : Any=10.0 ,_snake_case : int=0 ,_snake_case : Optional[Any]=1.0 ,_snake_case : List[str]=None ,_snake_case : Tuple=1.0 ,_snake_case : Tuple=False ,_snake_case : List[Any]=None ,_snake_case : int=1.0 ,_snake_case : List[Any]=1.0 ,_snake_case : Optional[int]=False ,_snake_case : Optional[int]=False ,_snake_case : Optional[int]="ratio" ,_snake_case : Any=None ,_snake_case : Union[str, Any]=None ,_snake_case : List[str]=64 ,_snake_case : Optional[Any]=32 ,_snake_case : Optional[Any]=False ,_snake_case : Optional[int]=True ,_snake_case : Dict=False ,_snake_case : Tuple=False ,_snake_case : int=True ,_snake_case : List[str]=False ,_snake_case : Dict=None ,_snake_case : Optional[int]=None ,**_snake_case : int ,) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_snake_case ,**_snake_case )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
lowercase__ : Optional[int] = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Optional[int] = hidden_act
lowercase__ : List[Any] = intermediate_size
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : Dict = attention_probs_dropout_prob
lowercase__ : str = max_position_embeddings
lowercase__ : Dict = type_vocab_sizes
lowercase__ : Optional[Any] = initializer_range
lowercase__ : Dict = layer_norm_eps
# Fine-tuning task hyperparameters
lowercase__ : Any = positive_label_weight
lowercase__ : int = num_aggregation_labels
lowercase__ : List[str] = aggregation_loss_weight
lowercase__ : Optional[int] = use_answer_as_supervision
lowercase__ : Optional[Any] = answer_loss_importance
lowercase__ : Union[str, Any] = use_normalized_answer_loss
lowercase__ : str = huber_loss_delta
lowercase__ : str = temperature
lowercase__ : int = aggregation_temperature
lowercase__ : List[Any] = use_gumbel_for_cells
lowercase__ : Tuple = use_gumbel_for_aggregation
lowercase__ : Union[str, Any] = average_approximation_function
lowercase__ : Union[str, Any] = cell_selection_preference
lowercase__ : Any = answer_loss_cutoff
lowercase__ : List[Any] = max_num_rows
lowercase__ : str = max_num_columns
lowercase__ : int = average_logits_per_cell
lowercase__ : str = select_one_column
lowercase__ : str = allow_empty_column_selection
lowercase__ : Any = init_cell_selection_weights_to_zero
lowercase__ : Optional[int] = reset_position_index_per_cell
lowercase__ : Union[str, Any] = disable_per_token_loss
# Aggregation hyperparameters
lowercase__ : Optional[Any] = aggregation_labels
lowercase__ : List[Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels ,_snake_case ):
lowercase__ : Union[str, Any] = {int(_snake_case ): v for k, v in aggregation_labels.items()}
| 16 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCAmelCase = 1
if upper_limit > 0:
UpperCAmelCase = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(lowerCAmelCase ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
lowerCAmelCase_ : Any = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(F'The Catalan numbers from 0 through {N} are:')
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 248 |
"""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
lowerCAmelCase_ : Any = ['''gpt2''']
lowerCAmelCase_ : Optional[int] = '''gpt2'''
if is_tf_available():
class UpperCamelCase_ ( tf.Module ):
def __init__( self , snake_case__ ) -> List[str]:
"""simple docstring"""
super().__init__()
UpperCAmelCase = tokenizer
UpperCAmelCase = AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase = TFGPTaLMHeadModel.from_config(snake_case__ )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) )
def UpperCamelCase_ ( self , snake_case__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.tokenizer(snake_case__ )
UpperCAmelCase = tokenized["""input_ids"""].to_tensor()
UpperCAmelCase = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
UpperCAmelCase = self.model(input_ids=snake_case__ , attention_mask=snake_case__ )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class UpperCamelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
UpperCAmelCase = [GPTaTokenizer.from_pretrained(snake_case__ ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
UpperCAmelCase = [TFGPTaTokenizer.from_pretrained(snake_case__ ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
UpperCAmelCase = [
"""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""",
]
UpperCAmelCase = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
UpperCAmelCase = tokenizer([test_inputs] , return_tensors="""tf""" )
UpperCAmelCase = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
UpperCAmelCase = python_outputs[key].numpy()
UpperCAmelCase = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(snake_case__ , tf.intaa ) == tf_outputs_values ) )
@slow
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase = tf.function(snake_case__ )
for test_inputs in self.test_sentences:
UpperCAmelCase = tf.constant(snake_case__ )
UpperCAmelCase = compiled_tokenizer(snake_case__ )
UpperCAmelCase = tf_tokenizer(snake_case__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase = ModelToSave(tokenizer=snake_case__ )
UpperCAmelCase = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase = model.serving(snake_case__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
UpperCAmelCase = Path(snake_case__ ) / """saved.model"""
tf.saved_model.save(snake_case__ , snake_case__ , signatures={"""serving_default""": model.serving} )
UpperCAmelCase = tf.saved_model.load(snake_case__ )
UpperCAmelCase = loaded_model.signatures["""serving_default"""](snake_case__ )["""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 UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
UpperCAmelCase = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase = tf_tokenizer(snake_case__ ) # Build model with some sample inputs
UpperCAmelCase = tf_tokenizer.get_config()
UpperCAmelCase = TFGPTaTokenizer.from_config(snake_case__ )
UpperCAmelCase = model_from_config(snake_case__ )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
UpperCAmelCase = 12_31_23
for max_length in [3, 5, 10_24]:
UpperCAmelCase = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCAmelCase = tf_tokenizer(snake_case__ , max_length=snake_case__ )
UpperCAmelCase = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 248 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Optional[int] = tempfile.mkdtemp()
# fmt: off
__lowerCamelCase : Dict = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
__lowerCamelCase : Dict = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
__lowerCamelCase : List[Any] = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
__lowerCamelCase : Tuple = {'unk_token': '<unk>'}
__lowerCamelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__lowerCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(SCREAMING_SNAKE_CASE_ ) )
__lowerCamelCase : str = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
__lowerCamelCase : Optional[Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , **SCREAMING_SNAKE_CASE_ ) -> Any:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def lowercase_ ( self ) -> List[str]:
__lowerCamelCase : int = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowerCamelCase : Union[str, Any] = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase_ ( self ) -> Optional[int]:
__lowerCamelCase : str = self.get_tokenizer()
__lowerCamelCase : str = self.get_rust_tokenizer()
__lowerCamelCase : Any = self.get_image_processor()
__lowerCamelCase : Optional[Any] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
processor_slow.save_pretrained(self.tmpdirname )
__lowerCamelCase : str = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
processor_fast.save_pretrained(self.tmpdirname )
__lowerCamelCase : List[Any] = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Tuple:
__lowerCamelCase : Optional[Any] = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase : Tuple = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__lowerCamelCase : List[str] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
__lowerCamelCase : Optional[int] = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Dict = self.get_image_processor()
__lowerCamelCase : Optional[int] = self.get_tokenizer()
__lowerCamelCase : Optional[int] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = self.prepare_image_inputs()
__lowerCamelCase : Any = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='np' )
__lowerCamelCase : int = processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowercase_ ( self ) -> Optional[int]:
__lowerCamelCase : Optional[Any] = self.get_image_processor()
__lowerCamelCase : Any = self.get_tokenizer()
__lowerCamelCase : Tuple = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = 'lower newer'
__lowerCamelCase : Tuple = processor(text=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = tokenizer(SCREAMING_SNAKE_CASE_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase_ ( self ) -> Union[str, Any]:
__lowerCamelCase : Tuple = self.get_image_processor()
__lowerCamelCase : Optional[Any] = self.get_tokenizer()
__lowerCamelCase : List[str] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = 'lower newer'
__lowerCamelCase : List[Any] = self.prepare_image_inputs()
__lowerCamelCase : Tuple = processor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
processor()
def lowercase_ ( self ) -> Dict:
__lowerCamelCase : List[Any] = self.get_image_processor()
__lowerCamelCase : Optional[int] = self.get_tokenizer()
__lowerCamelCase : Optional[int] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = self.prepare_image_inputs()
__lowerCamelCase : Any = self.prepare_image_inputs()
__lowerCamelCase : Tuple = processor(images=SCREAMING_SNAKE_CASE_ , visual_prompt=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'conditional_pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
processor()
def lowercase_ ( self ) -> Any:
__lowerCamelCase : int = self.get_image_processor()
__lowerCamelCase : List[Any] = self.get_tokenizer()
__lowerCamelCase : List[str] = CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowerCamelCase : List[str] = processor.batch_decode(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 185 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCAmelCase__ ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 185 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = math.inf , _SCREAMING_SNAKE_CASE = -math.inf , _SCREAMING_SNAKE_CASE = math.inf , _SCREAMING_SNAKE_CASE = -math.inf , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = 0.01 , _SCREAMING_SNAKE_CASE = 1 , ) ->Any:
a__: Tuple = False
a__: Tuple = search_prob
a__: int = start_temperate
a__: Dict = []
a__: List[str] = 0
a__: List[str] = None
while not search_end:
a__: Optional[int] = current_state.score()
if best_state is None or current_score > best_state.score():
a__: List[Any] = current_state
scores.append(_SCREAMING_SNAKE_CASE )
iterations += 1
a__: int = None
a__: str = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
a__: Optional[int] = random.randint(0 , len(_SCREAMING_SNAKE_CASE ) - 1 ) # picking a random neighbor
a__: Tuple = neighbors.pop(_SCREAMING_SNAKE_CASE )
a__: str = 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__: List[Any] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
a__: str = picked_neighbor
else:
a__: int = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
a__: Tuple = picked_neighbor
a__: str = 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__: List[Any] = True
else:
a__: Any = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
plt.xlabel('Iterations' )
plt.ylabel('Function values' )
plt.show()
return best_state
if __name__ == "__main__":
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
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 __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[Any]:
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()}"
)
| 203 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = 0
a__: Dict = [0]
a__: int = [0]
a__: Optional[Any] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 0)
a__: str = [60]
a__: Dict = [10]
a__: List[str] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 0)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: int = 3
a__: str = [1, 2, 3]
a__: Dict = [3, 2, 1]
a__: Optional[int] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 5)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Any = 50
a__: Optional[int] = [60, 1_00, 1_20]
a__: str = [10, 20, 30]
a__: int = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 2_20)
if __name__ == "__main__":
unittest.main()
| 203 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'''FlaxBlenderbotForConditionalGeneration''',
'''FlaxBlenderbotModel''',
'''FlaxBlenderbotPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 319 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
UpperCamelCase = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
UpperCamelCase = {
'''allenai/longformer-base-4096''': 4096,
'''allenai/longformer-large-4096''': 4096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def SCREAMING_SNAKE_CASE( ) -> Dict:
A: Dict = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
A: Union[str, Any] = bs[:]
A: List[str] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowercase )
cs.append(2**8 + n )
n += 1
A: List[Any] = [chr(__lowercase ) for n in cs]
return dict(zip(__lowercase , __lowercase ) )
def SCREAMING_SNAKE_CASE( __lowercase ) -> Optional[int]:
A: Optional[Any] = set()
A: Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
A: List[Any] = char
return pairs
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : int = VOCAB_FILES_NAMES
UpperCamelCase_ : int = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = ["""input_ids""", """attention_mask"""]
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str="replace" , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Any="</s>" , SCREAMING_SNAKE_CASE_ : int="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="<s>" , SCREAMING_SNAKE_CASE_ : str="<unk>" , SCREAMING_SNAKE_CASE_ : Dict="<pad>" , SCREAMING_SNAKE_CASE_ : Dict="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , **SCREAMING_SNAKE_CASE_ : Tuple , ) -> List[str]:
'''simple docstring'''
A: int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
A: int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
A: Any = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
A: str = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as vocab_handle:
A: str = json.load(SCREAMING_SNAKE_CASE_ )
A: str = {v: k for k, v in self.encoder.items()}
A: Union[str, Any] = errors # how to handle errors in decoding
A: Optional[int] = bytes_to_unicode()
A: Union[str, Any] = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as merges_handle:
A: int = merges_handle.read().split('''\n''' )[1:-1]
A: str = [tuple(merge.split() ) for merge in bpe_merges]
A: Any = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
A: Union[str, Any] = {}
A: Tuple = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
A: Dict = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def _snake_case ( self : int ) -> List[Any]:
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[Any] ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
A: str = tuple(SCREAMING_SNAKE_CASE_ )
A: str = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
A: Dict = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
A , A: Optional[Any] = bigram
A: Tuple = []
A: List[Any] = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
A: Union[str, Any] = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A: int = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A: Optional[Any] = tuple(SCREAMING_SNAKE_CASE_ )
A: Any = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
A: Union[str, Any] = get_pairs(SCREAMING_SNAKE_CASE_ )
A: str = ''' '''.join(SCREAMING_SNAKE_CASE_ )
A: str = word
return word
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
A: Dict = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
A: Tuple = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) )
return bpe_tokens
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> str:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Tuple:
'''simple docstring'''
A: Optional[int] = ''''''.join(SCREAMING_SNAKE_CASE_ )
A: Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
A: Union[str, Any] = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
A: int = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '''\n''' )
A: Any = 0
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
A: Union[str, Any] = token_index
writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A: int = [self.cls_token_id]
A: str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE_ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A: Dict = [self.sep_token_id]
A: Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict=False , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> int:
'''simple docstring'''
A: Tuple = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
A: List[Any] = ''' ''' + text
return (text, kwargs)
| 319 | 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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case_ (self ):
_UpperCAmelCase : Any = tempfile.mkdtemp()
_UpperCAmelCase : str = BlipImageProcessor()
_UpperCAmelCase : Any = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel""" )
_UpperCAmelCase : Any = BlipProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
def snake_case_ (self , **lowerCAmelCase__ ):
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).tokenizer
def snake_case_ (self , **lowerCAmelCase__ ):
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).image_processor
def snake_case_ (self ):
shutil.rmtree(self.tmpdirname )
def snake_case_ (self ):
_UpperCAmelCase : str = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase : List[str] = [Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ (self ):
_UpperCAmelCase : List[str] = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Optional[int] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_UpperCAmelCase : Optional[int] = self.get_image_processor(do_normalize=lowerCAmelCase__ , padding_value=1.0 )
_UpperCAmelCase : List[str] = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCAmelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase : Dict = self.get_image_processor()
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
_UpperCAmelCase : str = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
_UpperCAmelCase : int = self.prepare_image_inputs()
_UpperCAmelCase : Optional[Any] = image_processor(lowerCAmelCase__ , return_tensors="""np""" )
_UpperCAmelCase : Optional[Any] = processor(images=lowerCAmelCase__ , 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 snake_case_ (self ):
_UpperCAmelCase : Optional[int] = self.get_image_processor()
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
_UpperCAmelCase : List[str] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = "lower newer"
_UpperCAmelCase : Optional[Any] = processor(text=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer(lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ (self ):
_UpperCAmelCase : str = self.get_image_processor()
_UpperCAmelCase : Optional[Any] = self.get_tokenizer()
_UpperCAmelCase : Optional[int] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
_UpperCAmelCase : str = "lower newer"
_UpperCAmelCase : List[str] = self.prepare_image_inputs()
_UpperCAmelCase : Optional[int] = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def snake_case_ (self ):
_UpperCAmelCase : List[str] = self.get_image_processor()
_UpperCAmelCase : int = self.get_tokenizer()
_UpperCAmelCase : Tuple = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase : str = processor.batch_decode(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase : Dict = self.get_image_processor()
_UpperCAmelCase : Tuple = self.get_tokenizer()
_UpperCAmelCase : List[str] = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = "lower newer"
_UpperCAmelCase : Optional[Any] = self.prepare_image_inputs()
_UpperCAmelCase : Optional[Any] = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 367 |
'''simple docstring'''
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = 0
while len(lowerCAmelCase_ ) > 1:
_UpperCAmelCase : List[Any] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_UpperCAmelCase : Optional[Any] = files.index(min(lowerCAmelCase_ ) )
temp += files[min_index]
files.pop(lowerCAmelCase_ )
files.append(lowerCAmelCase_ )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = None
__lowerCamelCase = None
def lowerCAmelCase__ ( ) -> Node | None:
'''simple docstring'''
A__ = Node(1 )
A__ = Node(2 )
A__ = Node(3 )
A__ = Node(4 )
A__ = Node(5 )
return tree
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> int:
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
if root is None:
return output
A__ = deque([root] )
while process_queue:
A__ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
def populate_output(SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
def populate_output(SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> Sequence[Node | None] | list[Any]:
'''simple docstring'''
if root is None:
return []
A__ = []
A__ = 0
A__ = height(SCREAMING_SNAKE_CASE_ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
A__ = 1
else:
output.append(get_nodes_from_right_to_left(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
A__ = 0
return output
def lowerCAmelCase__ ( ) -> None: # Main function for testing.
'''simple docstring'''
A__ = make_tree()
print(F'In-order Traversal: {inorder(SCREAMING_SNAKE_CASE_ )}' )
print(F'Pre-order Traversal: {preorder(SCREAMING_SNAKE_CASE_ )}' )
print(F'Post-order Traversal: {postorder(SCREAMING_SNAKE_CASE_ )}' , "\n" )
print(F'Height of Tree: {height(SCREAMING_SNAKE_CASE_ )}' , "\n" )
print("Complete Level Order Traversal: " )
print(level_order(SCREAMING_SNAKE_CASE_ ) , "\n" )
print("Level-wise order Traversal: " )
for level in range(1 , height(SCREAMING_SNAKE_CASE_ ) + 1 ):
print(F'Level {level}:' , get_nodes_from_left_to_right(SCREAMING_SNAKE_CASE_ , level=SCREAMING_SNAKE_CASE_ ) )
print("\nZigZag order Traversal: " )
print(zigzag(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCamelCase : Any = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = ["pixel_values"]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = 8 , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__UpperCamelCase = do_rescale
__UpperCamelCase = rescale_factor
__UpperCamelCase = do_pad
__UpperCamelCase = pad_size
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = get_image_size(__UpperCAmelCase )
__UpperCamelCase = (old_height // size + 1) * size - old_height
__UpperCamelCase = (old_width // size + 1) * size - old_width
return pad(__UpperCAmelCase , ((0, pad_height), (0, pad_width)) , mode='symmetric' , data_format=__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
__UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCamelCase = do_pad if do_pad is not None else self.do_pad
__UpperCamelCase = pad_size if pad_size is not None else self.pad_size
__UpperCamelCase = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
# All transformations expect numpy arrays.
__UpperCamelCase = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_rescale:
__UpperCamelCase = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_pad:
__UpperCamelCase = [self.pad(__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
__UpperCamelCase = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__UpperCamelCase = {'pixel_values': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 316 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCamelCase_ : Optional[int] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
lowerCamelCase_ : Optional[int] = parser.parse_args()
if args.model_type == "roberta":
lowerCamelCase_ : int = RobertaForMaskedLM.from_pretrained(args.model_name)
lowerCamelCase_ : Optional[Any] = """roberta"""
elif args.model_type == "gpt2":
lowerCamelCase_ : Dict = GPTaLMHeadModel.from_pretrained(args.model_name)
lowerCamelCase_ : List[str] = """transformer"""
lowerCamelCase_ : Optional[Any] = model.state_dict()
lowerCamelCase_ : Union[str, Any] = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
lowerCamelCase_ : Dict = state_dict[F'''{prefix}.{param_name}''']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
lowerCamelCase_ : Optional[Any] = F'''{prefix}.embeddings.{w}.weight'''
lowerCamelCase_ : Any = state_dict[param_name]
for w in ["weight", "bias"]:
lowerCamelCase_ : str = F'''{prefix}.embeddings.LayerNorm.{w}'''
lowerCamelCase_ : Optional[Any] = state_dict[param_name]
# Transformer Blocks #
lowerCamelCase_ : List[str] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
lowerCamelCase_ : Dict = state_dict[
F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
]
lowerCamelCase_ : List[str] = state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
lowerCamelCase_ : Optional[int] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
lowerCamelCase_ : Optional[Any] = state_dict[F'''{layer}''']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCamelCase_ : List[Any] = state_dict[F'''lm_head.dense.{w}''']
lowerCamelCase_ : Union[str, Any] = state_dict[F'''lm_head.layer_norm.{w}''']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
lowerCamelCase_ : Optional[int] = state_dict[F'''{prefix}.ln_f.{w}''']
lowerCamelCase_ : List[str] = state_dict["""lm_head.weight"""]
print(F'''N layers selected for distillation: {std_idx}''')
print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 197 | from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__snake_case ):
A__ : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> List[str]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Dict:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Dict = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Optional[Any]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Dict = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Optional[int]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> str:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> List[str]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 197 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'biogpt'
def __init__( self : Optional[int] ,snake_case : Optional[int]=42384 ,snake_case : List[Any]=1024 ,snake_case : Optional[int]=24 ,snake_case : Optional[int]=16 ,snake_case : Tuple=4096 ,snake_case : Any="gelu" ,snake_case : Tuple=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Tuple=1024 ,snake_case : Any=0.02 ,snake_case : Optional[Any]=1e-12 ,snake_case : List[str]=True ,snake_case : Optional[int]=True ,snake_case : Optional[Any]=0.0 ,snake_case : Optional[Any]=0.0 ,snake_case : Union[str, Any]=1 ,snake_case : List[str]=0 ,snake_case : Union[str, Any]=2 ,**snake_case : Any ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =scale_embedding
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =layerdrop
SCREAMING_SNAKE_CASE =activation_dropout
super().__init__(pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,**snake_case )
| 334 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 211 | 0 |
'''simple docstring'''
UpperCamelCase = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 334 |
'''simple docstring'''
import os
from distutils.util import strtobool
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]:
for e in env_keys:
A: Dict = int(os.environ.get(__lowercase , -1 ) )
if val >= 0:
return val
return default
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> List[str]:
A: str = os.environ.get(__lowercase , str(__lowercase ) )
return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int...
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase="no" ) -> str:
A: Optional[int] = os.environ.get(__lowercase , str(__lowercase ) )
return value
| 334 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowerCAmelCase__ : Optional[int] =[
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
'''models/graphormer/algos_graphormer.pyx''',
]
def __lowercase ( a__ ) -> List[Any]:
# Test all the extensions added in the setup
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] =argparse.ArgumentParser()
parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
lowerCAmelCase__ : int =parser.parse_args()
if args.check_lib:
lowerCAmelCase__ : str =importlib.import_module('''transformers''')
lowerCAmelCase__ : Dict =Path(transformers_module.__file__).parent
else:
lowerCAmelCase__ : List[Any] =Path.cwd() / '''build/lib/transformers'''
if not test_custom_files_are_present(transformers_path):
raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
| 257 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = process
__SCREAMING_SNAKE_CASE = params
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.dataset[i]
__SCREAMING_SNAKE_CASE = self.process(_A , **self.params )
return processed
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A , _A , _A=None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = loader
__SCREAMING_SNAKE_CASE = infer
__SCREAMING_SNAKE_CASE = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = loader_batch_size
# Internal bookkeeping
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
def __len__( self ):
'''simple docstring'''
return len(self.loader )
def __iter__( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = iter(self.loader )
return self
def _A ( self ):
'''simple docstring'''
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
__SCREAMING_SNAKE_CASE = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
__SCREAMING_SNAKE_CASE = {}
for k, element in self._loader_batch_data.items():
if isinstance(_A , _A ):
# Convert ModelOutput to tuple first
__SCREAMING_SNAKE_CASE = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
__SCREAMING_SNAKE_CASE = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__SCREAMING_SNAKE_CASE = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_A , _A ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
__SCREAMING_SNAKE_CASE = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__SCREAMING_SNAKE_CASE = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
__SCREAMING_SNAKE_CASE = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__SCREAMING_SNAKE_CASE = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__SCREAMING_SNAKE_CASE = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
__SCREAMING_SNAKE_CASE = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
__SCREAMING_SNAKE_CASE = self._loader_batch_data.__class__(_A )
self._loader_batch_index += 1
return result
def _A ( self ):
'''simple docstring'''
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
__SCREAMING_SNAKE_CASE = next(self.iterator )
__SCREAMING_SNAKE_CASE = self.infer(_A , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(_A , torch.Tensor ):
__SCREAMING_SNAKE_CASE = processed
else:
__SCREAMING_SNAKE_CASE = list(processed.keys() )[0]
__SCREAMING_SNAKE_CASE = processed[key]
if isinstance(_A , _A ):
__SCREAMING_SNAKE_CASE = len(_A )
else:
__SCREAMING_SNAKE_CASE = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__SCREAMING_SNAKE_CASE = observed_batch_size
# Setting internal index to unwrap the batch
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A , _A , _A=None ):
'''simple docstring'''
super().__init__(_A , _A , _A )
def __iter__( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = iter(self.loader )
__SCREAMING_SNAKE_CASE = None
return self
def _A ( self ):
'''simple docstring'''
if self.subiterator is None:
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
__SCREAMING_SNAKE_CASE = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
__SCREAMING_SNAKE_CASE = next(self.subiterator )
return processed
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __iter__( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = iter(self.loader )
return self
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
__SCREAMING_SNAKE_CASE = self.loader_batch_item()
__SCREAMING_SNAKE_CASE = item.pop('is_last' )
accumulator.append(_A )
if is_last:
return accumulator
while not is_last:
__SCREAMING_SNAKE_CASE = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(_A , torch.Tensor ):
__SCREAMING_SNAKE_CASE = processed
else:
__SCREAMING_SNAKE_CASE = list(processed.keys() )[0]
__SCREAMING_SNAKE_CASE = processed[key]
if isinstance(_A , _A ):
__SCREAMING_SNAKE_CASE = len(_A )
else:
__SCREAMING_SNAKE_CASE = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__SCREAMING_SNAKE_CASE = observed_batch_size
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = 0
while self._loader_batch_index < self.loader_batch_size:
__SCREAMING_SNAKE_CASE = self.loader_batch_item()
__SCREAMING_SNAKE_CASE = item.pop('is_last' )
accumulator.append(_A )
if is_last:
return accumulator
else:
__SCREAMING_SNAKE_CASE = processed
__SCREAMING_SNAKE_CASE = item.pop('is_last' )
accumulator.append(_A )
return accumulator
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = key
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self , _A ):
'''simple docstring'''
return self.dataset[i][self.key]
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = dataset
__SCREAMING_SNAKE_CASE = keya
__SCREAMING_SNAKE_CASE = keya
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self , _A ):
'''simple docstring'''
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 257 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class __magic_name__ (lowerCamelCase__ ):
lowerCamelCase__ = '''gpt_neo'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , _a=50257 , _a=2048 , _a=2048 , _a=24 , _a=[[["global", "local"], 12]] , _a=16 , _a=None , _a=256 , _a="gelu_new" , _a=0.0 , _a=0.0 , _a=0.0 , _a=0.1 , _a=1E-5 , _a=0.0_2 , _a=True , _a=50256 , _a=50256 , **_a , ) -> Any:
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_layers
lowerCAmelCase_ = num_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = window_size
lowerCAmelCase_ = activation_function
lowerCAmelCase_ = resid_dropout
lowerCAmelCase_ = embed_dropout
lowerCAmelCase_ = attention_dropout
lowerCAmelCase_ = classifier_dropout
lowerCAmelCase_ = layer_norm_epsilon
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = use_cache
lowerCAmelCase_ = bos_token_id
lowerCAmelCase_ = eos_token_id
lowerCAmelCase_ = attention_types
lowerCAmelCase_ = self.expand_attention_types_params(_a )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.attention_layers)` == `config.num_layers` "
f"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
f"`config.num_layers = {self.num_layers}`. "
"`config.attention_layers` is prepared using `config.attention_types`. "
"Please verify the value of `config.attention_types` argument." )
super().__init__(bos_token_id=_a , eos_token_id=_a , **_a )
@staticmethod
def __a ( _a ) -> Optional[Any]:
lowerCAmelCase_ = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def A(__a: List[str] , __a: Tuple , __a: List[str] , __a: Any ):
import torch
lowerCAmelCase_ = input.size()
lowerCAmelCase_ = len(_lowerCAmelCase )
lowerCAmelCase_ = shape[dimension]
lowerCAmelCase_ = torch.arange(0 , _lowerCAmelCase , _lowerCAmelCase )
lowerCAmelCase_ = torch.div(sizedim - size , _lowerCAmelCase , rounding_mode="floor" ) + 1
lowerCAmelCase_ = torch.arange(_lowerCAmelCase ) + low_indices[:min_length][:, None]
lowerCAmelCase_ = [slice(_lowerCAmelCase )] * rank
lowerCAmelCase_ = indices
lowerCAmelCase_ = input[s]
lowerCAmelCase_ = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(_lowerCAmelCase )
def A(__a: List[str] , __a: int ):
import torch
lowerCAmelCase_ = torch.arange(1 , _lowerCAmelCase )
lowerCAmelCase_ = torch.remainder(_lowerCAmelCase , _lowerCAmelCase )
lowerCAmelCase_ = remainders == 0
lowerCAmelCase_ = candidates[divisor_indices]
lowerCAmelCase_ = torch.max(_lowerCAmelCase )
return largest_divisor, torch.div(_lowerCAmelCase , _lowerCAmelCase , rounding_mode="floor" )
class __magic_name__ (lowerCamelCase__ ):
@property
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(_a , direction="inputs" )
lowerCAmelCase_ = {0: "batch", 1: "past_sequence + sequence"}
else:
lowerCAmelCase_ = {0: "batch", 1: "sequence"}
return common_inputs
@property
def __a ( self ) -> List[Any]:
return self._config.num_heads
def __a ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> Dict:
lowerCAmelCase_ = 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()
lowerCAmelCase_ = 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
lowerCAmelCase_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowerCAmelCase_ = seqlen + 2
lowerCAmelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCAmelCase_ = [
(torch.zeros(_a ), torch.zeros(_a )) for _ in range(self.num_layers )
]
lowerCAmelCase_ = common_inputs["attention_mask"]
if self.use_past:
lowerCAmelCase_ = ordered_inputs["attention_mask"].dtype
lowerCAmelCase_ = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(_a , _a , dtype=_a )] , dim=1 )
return ordered_inputs
@property
def __a ( self ) -> str:
return 13
| 353 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ (__lowercase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViTImageProcessor'''
lowerCamelCase__ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self , _a=None , _a=None , **_a ) -> Tuple:
lowerCAmelCase_ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , _a , )
lowerCAmelCase_ = kwargs.pop("feature_extractor" )
lowerCAmelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(_a , _a )
def __call__( self , _a=None , _a=None , _a=None , _a=None , **_a ) -> Dict:
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
lowerCAmelCase_ = self.tokenizer(_a , return_tensors=_a , **_a )
if visual_prompt is not None:
lowerCAmelCase_ = self.image_processor(_a , return_tensors=_a , **_a )
if images is not None:
lowerCAmelCase_ = self.image_processor(_a , return_tensors=_a , **_a )
if visual_prompt is not None and images is not None:
lowerCAmelCase_ = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
lowerCAmelCase_ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
lowerCAmelCase_ = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def __a ( self , *_a , **_a ) -> List[str]:
return self.tokenizer.batch_decode(*_a , **_a )
def __a ( self , *_a , **_a ) -> Optional[int]:
return self.tokenizer.decode(*_a , **_a )
@property
def __a ( self ) -> List[str]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , _a , )
return self.image_processor_class
@property
def __a ( self ) -> Optional[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , _a , )
return self.image_processor
| 22 | 0 |
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
UpperCamelCase = logging.get_logger(__name__)
class snake_case_ ( __A ):
__A : Tuple = "linear"
__A : Union[str, Any] = "cosine"
__A : Any = "cosine_with_restarts"
__A : int = "polynomial"
__A : Union[str, Any] = "constant"
__A : Tuple = "constant_with_warmup"
__A : str = "piecewise_constant"
def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int = -1):
return LambdaLR(_lowerCamelCase , lambda _lowerCamelCase: 1 , last_epoch=_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int = -1):
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1.0 , _lowerCamelCase))
return 1.0
return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : str , _lowerCamelCase : int = -1):
lowercase__ : Optional[Any] = {}
lowercase__ : Any = step_rules.split(",")
for rule_str in rule_list[:-1]:
lowercase__ , lowercase__ : str = rule_str.split(":")
lowercase__ : Optional[Any] = int(_lowerCamelCase)
lowercase__ : Optional[Any] = float(_lowerCamelCase)
lowercase__ : Union[str, Any] = value
lowercase__ : Optional[int] = float(rule_list[-1])
def create_rules_function(_lowerCamelCase : str , _lowerCamelCase : Tuple):
def rule_func(_lowerCamelCase : int) -> float:
lowercase__ : str = sorted(rules_dict.keys())
for i, sorted_step in enumerate(_lowerCamelCase):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
lowercase__ : Optional[int] = create_rules_function(_lowerCamelCase , _lowerCamelCase)
return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any=-1):
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
return max(
0.0 , float(num_training_steps - current_step) / float(max(1 , num_training_steps - num_warmup_steps)))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0.5 , _lowerCamelCase : int = -1):
def lr_lambda(_lowerCamelCase : List[str]):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
lowercase__ : str = 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(_lowerCamelCase) * 2.0 * progress)))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int = 1 , _lowerCamelCase : int = -1):
def lr_lambda(_lowerCamelCase : Optional[Any]):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
lowercase__ : Tuple = 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(_lowerCamelCase) * progress) % 1.0))))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any]=1E-7 , _lowerCamelCase : Optional[Any]=1.0 , _lowerCamelCase : int=-1):
lowercase__ : Any = 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(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lowercase__ : Any = lr_init - lr_end
lowercase__ : List[Any] = num_training_steps - num_warmup_steps
lowercase__ : Any = 1 - (current_step - num_warmup_steps) / decay_steps
lowercase__ : List[str] = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
UpperCamelCase = {
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 lowercase_ ( _lowerCamelCase : Union[str, SchedulerType] , _lowerCamelCase : Optimizer , _lowerCamelCase : Optional[str] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : int = 1 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : int = -1 , ):
lowercase__ : List[str] = SchedulerType(_lowerCamelCase)
lowercase__ : int = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(_lowerCamelCase , last_epoch=_lowerCamelCase)
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(_lowerCamelCase , step_rules=_lowerCamelCase , last_epoch=_lowerCamelCase)
# 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(_lowerCamelCase , num_warmup_steps=_lowerCamelCase , last_epoch=_lowerCamelCase)
# 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(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , num_cycles=_lowerCamelCase , last_epoch=_lowerCamelCase , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , power=_lowerCamelCase , last_epoch=_lowerCamelCase , )
return schedule_func(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , last_epoch=_lowerCamelCase)
| 87 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class lowercase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
lowercase_ : Optional[Any] =IFPipeline
lowercase_ : List[str] =TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
lowercase_ : List[str] =TEXT_TO_IMAGE_BATCH_PARAMS
lowercase_ : int =PipelineTesterMixin.required_optional_params - {'''latents'''}
def A__ ( self):
return self._get_dummy_components()
def A__ ( self ,A__ ,A__=0):
if str(A__).startswith('''mps'''):
lowercase = torch.manual_seed(A__)
else:
lowercase = torch.Generator(device=A__).manual_seed(A__)
lowercase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def A__ ( self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' ,reason='''float16 requires CUDA''')
def A__ ( self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1)
def A__ ( self):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def A__ ( self):
self._test_save_load_local()
def A__ ( self):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 ,)
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() ,reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,)
def A__ ( self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
def A__ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self):
# if
lowercase = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' ,variant='''fp16''' ,torch_dtype=torch.floataa)
lowercase = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' ,variant='''fp16''' ,torch_dtype=torch.floataa ,text_encoder=A__ ,tokenizer=A__)
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''')
lowercase , lowercase = pipe_a.encode_prompt('''anime turtle''' ,device='''cuda''')
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
lowercase = None
lowercase = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if(A__ ,A__ ,A__ ,A__)
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
lowercase = IFImgaImgPipeline(**pipe_a.components)
lowercase = IFImgaImgSuperResolutionPipeline(**pipe_a.components)
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if_imgaimg(A__ ,A__ ,A__ ,A__)
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
lowercase = IFInpaintingPipeline(**pipe_a.components)
lowercase = IFInpaintingSuperResolutionPipeline(**pipe_a.components)
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if_inpainting(A__ ,A__ ,A__ ,A__)
def A__ ( self ,A__ ,A__ ,A__ ,A__):
# pipeline 1
_start_torch_memory_measurement()
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,num_inference_steps=2 ,generator=A__ ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_3 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
assert_mean_pixel_difference(A__ ,A__)
# pipeline 2
_start_torch_memory_measurement()
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(0)).to(A__)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,image=A__ ,generator=A__ ,num_inference_steps=2 ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
assert_mean_pixel_difference(A__ ,A__)
def A__ ( self ,A__ ,A__ ,A__ ,A__):
# pipeline 1
_start_torch_memory_measurement()
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(0)).to(A__)
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,image=A__ ,num_inference_steps=2 ,generator=A__ ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_0 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
assert_mean_pixel_difference(A__ ,A__)
# pipeline 2
_start_torch_memory_measurement()
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = floats_tensor((1, 3, 2_5_6, 2_5_6) ,rng=random.Random(0)).to(A__)
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(0)).to(A__)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,image=A__ ,original_image=A__ ,generator=A__ ,num_inference_steps=2 ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
assert_mean_pixel_difference(A__ ,A__)
def A__ ( self ,A__ ,A__ ,A__ ,A__):
# pipeline 1
_start_torch_memory_measurement()
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(0)).to(A__)
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(1)).to(A__)
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,image=A__ ,mask_image=A__ ,num_inference_steps=2 ,generator=A__ ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_0 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
assert_mean_pixel_difference(A__ ,A__)
# pipeline 2
_start_torch_memory_measurement()
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(0)).to(A__)
lowercase = floats_tensor((1, 3, 2_5_6, 2_5_6) ,rng=random.Random(0)).to(A__)
lowercase = floats_tensor((1, 3, 2_5_6, 2_5_6) ,rng=random.Random(1)).to(A__)
lowercase = pipe_a(
prompt_embeds=A__ ,negative_prompt_embeds=A__ ,image=A__ ,mask_image=A__ ,original_image=A__ ,generator=A__ ,num_inference_steps=2 ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
lowercase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
assert_mean_pixel_difference(A__ ,A__)
def UpperCamelCase ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 101 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
_snake_case = logging.getLogger(__name__)
@dataclass
class lowercase :
_a = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
_a = field(
default=UpperCamelCase__,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
_a = field(
default=UpperCamelCase__,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
_a = field(
default=UpperCamelCase__,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},)
_a = field(
default=UpperCamelCase__,metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},)
_a = field(
default="main",metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},)
_a = field(
default=UpperCamelCase__,metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},)
@dataclass
class lowercase :
_a = field(default=UpperCamelCase__,metadata={"help": "The input training data file (a text file)."} )
_a = field(
default=UpperCamelCase__,metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},)
_a = field(
default=UpperCamelCase__,metadata={"help": "Overwrite the cached training and evaluation sets"} )
_a = field(
default=UpperCamelCase__,metadata={"help": "The number of processes to use for the preprocessing."},)
_a = field(
default=UpperCamelCase__,metadata={
"help": (
"The maximum total input sequence length after tokenization. If passed, sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},)
_a = field(
default=UpperCamelCase__,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."
)
},)
_a = field(
default=UpperCamelCase__,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},)
_a = field(
default=UpperCamelCase__,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},)
def a__ ( self ) -> Dict:
if self.train_file is not None:
_A : List[str] = 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 : List[str] = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class lowercase :
_a = 42
_a = True
_a = None
_a = None
def __call__( self , _a ) -> Optional[Any]:
_A : Tuple = """label""" if """label""" in features[0].keys() else """labels"""
_A : Any = [feature.pop(_a ) for feature in features]
_A : List[str] = len(_a )
_A : Optional[int] = len(features[0]["""input_ids"""] )
_A : Tuple = [
[{k: v[i] for k, v in feature.items()} for i in range(_a )] for feature in features
]
_A : str = list(chain(*_a ) )
_A : Optional[Any] = 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 : str = {k: v.view(_a , _a , -1 ) for k, v in batch.items()}
# Add back labels
_A : Union[str, Any] = torch.tensor(_a , dtype=torch.intaa )
return batch
def lowerCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_A : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A : Tuple = 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""",snake_case_,snake_case_ )
# 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 : Tuple = training_args.get_process_log_level()
logger.setLevel(snake_case_ )
datasets.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
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 : List[str] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A : Any = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to 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 : Tuple = {}
if data_args.train_file is not None:
_A : Optional[int] = data_args.train_file
if data_args.validation_file is not None:
_A : Any = data_args.validation_file
_A : Dict = data_args.train_file.split(""".""" )[-1]
_A : Tuple = load_dataset(
snake_case_,data_files=snake_case_,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 : Optional[Any] = 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 : int = 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 : str = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,cache_dir=model_args.cache_dir,use_fast=model_args.use_fast_tokenizer,revision=model_args.model_revision,use_auth_token=True if model_args.use_auth_token else None,)
_A : List[str] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,from_tf=bool(""".ckpt""" in model_args.model_name_or_path ),config=snake_case_,cache_dir=model_args.cache_dir,revision=model_args.model_revision,use_auth_token=True if model_args.use_auth_token else None,)
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_A : Dict = [f'''ending{i}''' for i in range(4 )]
_A : Tuple = """sent1"""
_A : Union[str, Any] = """sent2"""
if data_args.max_seq_length is None:
_A : Optional[Any] = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
_A : Tuple = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
_A : List[Any] = min(data_args.max_seq_length,tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(snake_case_ ):
_A : Optional[int] = [[context] * 4 for context in examples[context_name]]
_A : Optional[Any] = examples[question_header_name]
_A : Union[str, Any] = [
[f'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(snake_case_ )
]
# Flatten out
_A : Union[str, Any] = list(chain(*snake_case_ ) )
_A : Optional[Any] = list(chain(*snake_case_ ) )
# Tokenize
_A : Union[str, Any] = tokenizer(
snake_case_,snake_case_,truncation=snake_case_,max_length=snake_case_,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(snake_case_ ),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 : List[Any] = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
_A : Tuple = min(len(snake_case_ ),data_args.max_train_samples )
_A : Any = train_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
_A : Optional[Any] = train_dataset.map(
snake_case_,batched=snake_case_,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 : List[str] = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
_A : int = min(len(snake_case_ ),data_args.max_eval_samples )
_A : List[str] = eval_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
_A : Optional[int] = eval_dataset.map(
snake_case_,batched=snake_case_,num_proc=data_args.preprocessing_num_workers,load_from_cache_file=not data_args.overwrite_cache,)
# Data collator
_A : int = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=snake_case_,pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(snake_case_ ):
_A , _A : Dict = eval_predictions
_A : str = np.argmax(snake_case_,axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_A : List[str] = Trainer(
model=snake_case_,args=snake_case_,train_dataset=train_dataset if training_args.do_train else None,eval_dataset=eval_dataset if training_args.do_eval else None,tokenizer=snake_case_,data_collator=snake_case_,compute_metrics=snake_case_,)
# Training
if training_args.do_train:
_A : Tuple = None
if training_args.resume_from_checkpoint is not None:
_A : int = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A : Dict = last_checkpoint
_A : Optional[Any] = trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model() # Saves the tokenizer too for easy upload
_A : Optional[int] = train_result.metrics
_A : Dict = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_ )
)
_A : Tuple = min(snake_case_,len(snake_case_ ) )
trainer.log_metrics("""train""",snake_case_ )
trainer.save_metrics("""train""",snake_case_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_A : Any = trainer.evaluate()
_A : List[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case_ )
_A : Dict = min(snake_case_,len(snake_case_ ) )
trainer.log_metrics("""eval""",snake_case_ )
trainer.save_metrics("""eval""",snake_case_ )
_A : Union[str, Any] = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """multiple-choice""",
"""dataset_tags""": """swag""",
"""dataset_args""": """regular""",
"""dataset""": """SWAG""",
"""language""": """en""",
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case_ )
else:
trainer.create_model_card(**snake_case_ )
def lowerCAmelCase_ ( snake_case_ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 343 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase ( unittest.TestCase ):
def a__ ( self ) -> List[str]:
debug_launcher(test_script.main )
def a__ ( self ) -> Any:
debug_launcher(test_ops.main )
| 343 | 1 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
__snake_case =True
from torch.cuda.amp import autocast
__snake_case =logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
lowerCamelCase : Optional[bool] = field(
default=__lowercase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
lowerCamelCase : Optional[bool] = field(
default=__lowercase , metadata={'''help''': '''Whether to log verbose messages or not.'''} , )
lowerCamelCase : Optional[float] = field(
default=2.0 , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''} )
lowerCamelCase : Optional[float] = field(
default=0.5 , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''} )
lowerCamelCase : Optional[float] = field(
default=0.9_9_9_9_9_5 , metadata={'''help''': '''Decay of gumbel temperature during training.'''} )
def a_ ( lowerCamelCase : ModelArguments , lowerCamelCase : TrainingArguments ):
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
lowerCAmelCase = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
lowerCAmelCase = logging.INFO
logger.setLevel(lowerCamelCase )
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : str = field(
default=__lowercase , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowerCamelCase : Optional[str] = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
lowerCamelCase : Optional[str] = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
lowerCamelCase : Optional[str] = field(
default='''file''' , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} , )
lowerCamelCase : bool = field(
default=__lowercase , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
lowerCamelCase : Optional[int] = field(
default=1 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
lowerCamelCase : Optional[int] = field(
default=__lowercase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
lowerCamelCase : Optional[float] = field(
default=2_0.0 , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''} )
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : WavaVecaForPreTraining
lowerCamelCase : WavaVecaFeatureExtractor
lowerCamelCase : Union[bool, str] = "longest"
lowerCamelCase : Optional[int] = None
lowerCamelCase : Optional[int] = None
def __call__( self : Union[str, Any] , UpperCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
# reformat list to dict and set to pytorch format
lowerCAmelCase = self.feature_extractor.pad(
UpperCAmelCase__ , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
lowerCAmelCase = self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1] )
lowerCAmelCase = batch['input_values'].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
lowerCAmelCase = self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1 ) ).to(
torch.long )
lowerCAmelCase = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch['input_values'].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
lowerCAmelCase = 1
lowerCAmelCase = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=UpperCAmelCase__ , min_masks=2 , )
return batch
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Optional[int] , *UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int]=1 , UpperCAmelCase__ : Dict=0 , UpperCAmelCase__ : List[str]=1.0 , **UpperCAmelCase__ : int ) -> int:
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
lowerCAmelCase = 0
lowerCAmelCase = max_gumbel_temp
lowerCAmelCase = min_gumbel_temp
lowerCAmelCase = gumbel_temp_decay
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : nn.Module , UpperCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
model.train()
lowerCAmelCase = self._prepare_inputs(UpperCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase = self.compute_loss(UpperCAmelCase__ , UpperCAmelCase__ )
else:
lowerCAmelCase = self.compute_loss(UpperCAmelCase__ , UpperCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase = loss.sum() / (inputs['mask_time_indices']).sum()
else:
raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )
if self.args.gradient_accumulation_steps > 1:
lowerCAmelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(UpperCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(UpperCAmelCase__ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(UpperCAmelCase__ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def a_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = parser.parse_args_into_dataclasses()
configure_logger(lowerCamelCase , lowerCamelCase )
# Downloading and loading a dataset from the hub.
lowerCAmelCase = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
lowerCAmelCase = DatasetDict()
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]''' , cache_dir=model_args.cache_dir , )
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]''' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
lowerCAmelCase = DatasetDict()
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='validation' , cache_dir=model_args.cache_dir , )
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}''' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
lowerCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=lowerCamelCase )
def prepare_dataset(lowerCamelCase : Optional[Any] ):
# check that all files have the correct sampling rate
lowerCAmelCase , lowerCAmelCase = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
lowerCAmelCase = datasets.map(
lowerCamelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['train'].column_names )
# filter audio files that are too long
lowerCAmelCase = vectorized_datasets.filter(
lambda lowerCamelCase : len(data['speech'] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(lowerCamelCase : Dict ):
return feature_extractor(batch['speech'] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
lowerCAmelCase = vectorized_datasets.map(
lowerCamelCase , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['train'].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
lowerCAmelCase = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
' ``config.feat_extract_norm=\'layer\'' )
lowerCAmelCase = WavaVecaForPreTraining(lowerCamelCase )
lowerCAmelCase = DataCollatorForWavaVecaPretraining(model=lowerCamelCase , feature_extractor=lowerCamelCase )
lowerCAmelCase = WavaVecaPreTrainer(
model=lowerCamelCase , data_collator=lowerCamelCase , args=lowerCamelCase , train_dataset=vectorized_datasets['train'] , eval_dataset=vectorized_datasets['validation'] , tokenizer=lowerCamelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 4 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=True, UpperCAmelCase__="pt" ) -> str:
A_ = {"""add_prefix_space""": True} if isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and not line.startswith(""" """ ) else {}
A_ = padding_side
return tokenizer(
[line], max_length=UpperCAmelCase__, padding="""max_length""" if pad_to_max_length else None, truncation=UpperCAmelCase__, return_tensors=UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, **UpperCAmelCase__, )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=None, ) -> List[str]:
A_ = input_ids.ne(UpperCAmelCase__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class A__ ( _snake_case ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="train" , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="" , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
A_ = Path(UpperCamelCase__ ).joinpath(type_path + """.source""" )
A_ = Path(UpperCamelCase__ ).joinpath(type_path + """.target""" )
A_ = self.get_char_lens(self.src_file )
A_ = max_source_length
A_ = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
A_ = tokenizer
A_ = prefix
if n_obs is not None:
A_ = self.src_lens[:n_obs]
A_ = src_lang
A_ = tgt_lang
def __len__( self ) -> Dict:
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self , UpperCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
A_ = index + 1 # linecache starts at 1
A_ = self.prefix + linecache.getline(str(self.src_file ) , UpperCamelCase__ ).rstrip("""\n""" )
A_ = linecache.getline(str(self.tgt_file ) , UpperCamelCase__ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , UpperCamelCase__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , UpperCamelCase__ ) else self.tokenizer
)
A_ = self.tokenizer.generator if isinstance(self.tokenizer , UpperCamelCase__ ) else self.tokenizer
A_ = encode_line(UpperCamelCase__ , UpperCamelCase__ , self.max_source_length , """right""" )
A_ = encode_line(UpperCamelCase__ , UpperCamelCase__ , self.max_target_length , """right""" )
A_ = source_inputs["""input_ids"""].squeeze()
A_ = target_inputs["""input_ids"""].squeeze()
A_ = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def snake_case_ ( UpperCamelCase__ ) -> Any:
'''simple docstring'''
return [len(UpperCamelCase__ ) for x in Path(UpperCamelCase__ ).open().readlines()]
def snake_case_ ( self , UpperCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
A_ = torch.stack([x["""input_ids"""] for x in batch] )
A_ = torch.stack([x["""attention_mask"""] for x in batch] )
A_ = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase__ )
else self.tokenizer.pad_token_id
)
A_ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase__ )
else self.tokenizer.pad_token_id
)
A_ = trim_batch(UpperCamelCase__ , UpperCamelCase__ )
A_ , A_ = trim_batch(UpperCamelCase__ , UpperCamelCase__ , attention_mask=UpperCamelCase__ )
A_ = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__lowerCamelCase = getLogger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
return list(itertools.chain.from_iterable(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
A_ = get_git_info()
save_json(UpperCAmelCase__, os.path.join(UpperCAmelCase__, """git_log.json""" ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=4, **UpperCAmelCase__ ) -> Dict:
with open(UpperCAmelCase__, """w""" ) as f:
json.dump(UpperCAmelCase__, UpperCAmelCase__, indent=UpperCAmelCase__, **UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
with open(UpperCAmelCase__ ) as f:
return json.load(UpperCAmelCase__ )
def UpperCAmelCase__ ( ) -> Any:
A_ = git.Repo(search_parent_directories=UpperCAmelCase__ )
A_ = {
"""repo_id""": str(UpperCAmelCase__ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> List:
return list(map(UpperCAmelCase__, UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> List[Any]:
with open(UpperCAmelCase__, """wb""" ) as f:
return pickle.dump(UpperCAmelCase__, UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Union[str, Any]:
def remove_articles(UpperCAmelCase__ ):
return re.sub(r"""\b(a|an|the)\b""", """ """, UpperCAmelCase__ )
def white_space_fix(UpperCAmelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCAmelCase__ ):
A_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCAmelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__ ) ) ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[int]:
A_ = normalize_answer(UpperCAmelCase__ ).split()
A_ = normalize_answer(UpperCAmelCase__ ).split()
A_ = Counter(UpperCAmelCase__ ) & Counter(UpperCAmelCase__ )
A_ = sum(common.values() )
if num_same == 0:
return 0
A_ = 1.0 * num_same / len(UpperCAmelCase__ )
A_ = 1.0 * num_same / len(UpperCAmelCase__ )
A_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[Any]:
return normalize_answer(UpperCAmelCase__ ) == normalize_answer(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Dict:
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )
A_ = 0
for hypo, pred in zip(UpperCAmelCase__, UpperCAmelCase__ ):
em += exact_match_score(UpperCAmelCase__, UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 0:
em /= len(UpperCAmelCase__ )
return {"em": em}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[Any]:
return model_prefix.startswith("""rag""" )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> List[str]:
A_ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ = """dropout_rate"""
for p in extra_params:
if getattr(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ):
if not hasattr(UpperCAmelCase__, UpperCAmelCase__ ) and not hasattr(UpperCAmelCase__, equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(UpperCAmelCase__ ) )
delattr(UpperCAmelCase__, UpperCAmelCase__ )
continue
A_ = p if hasattr(UpperCAmelCase__, UpperCAmelCase__ ) else equivalent_param[p]
setattr(UpperCAmelCase__, UpperCAmelCase__, getattr(UpperCAmelCase__, UpperCAmelCase__ ) )
delattr(UpperCAmelCase__, UpperCAmelCase__ )
return hparams, config
| 162 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["ViTFeatureExtractor"]
__a = ["ViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTForImageClassification",
"ViTForMaskedImageModeling",
"ViTModel",
"ViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"TFViTForImageClassification",
"TFViTModel",
"TFViTPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"FlaxViTForImageClassification",
"FlaxViTModel",
"FlaxViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 354 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_lowerCAmelCase , _lowerCAmelCase )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(_lowerCAmelCase ) != input_a)
or (math.floor(_lowerCAmelCase ) != input_a)
or (math.floor(_lowerCAmelCase ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
snake_case__ : List[str] = qiskit.QuantumRegister(4 , """qr""" )
snake_case__ : Optional[int] = qiskit.ClassicalRegister(2 , """cr""" )
# list the entries
snake_case__ : List[Any] = [input_a, input_a, carry_in]
snake_case__ : Union[str, Any] = qiskit.QuantumCircuit(_lowerCAmelCase , _lowerCAmelCase )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(_lowerCAmelCase ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(_lowerCAmelCase ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(_lowerCAmelCase ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , _lowerCAmelCase ) # measure the last two qbits
snake_case__ : int = qiskit.Aer.get_backend("""aer_simulator""" )
snake_case__ : Tuple = qiskit.execute(_lowerCAmelCase , _lowerCAmelCase , shots=1_000 )
return job.result().get_counts(_lowerCAmelCase )
if __name__ == "__main__":
print(F"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 43 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
A__ : int =False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Union[str, Any] , __snake_case : int , __snake_case : Union[str, Any]=7 , __snake_case : Any=3 , __snake_case : Dict=18 , __snake_case : List[Any]=30 , __snake_case : int=4_00 , __snake_case : List[Any]=None , __snake_case : List[str]=True , __snake_case : Any=True , __snake_case : Tuple=None , ) -> Union[str, Any]:
_lowerCAmelCase = size if size is not None else {"""height""": 20, """width""": 20}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = size
_lowerCAmelCase = do_normalize
_lowerCAmelCase = do_convert_rgb
_lowerCAmelCase = [5_12, 10_24, 20_48, 40_96]
_lowerCAmelCase = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
def lowercase__ ( self : Any ) -> str:
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def lowercase__ ( self : int ) -> Optional[int]:
_lowerCAmelCase = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
_lowerCAmelCase = Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert("""RGB""" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class UpperCAmelCase ( snake_case_ , unittest.TestCase ):
_lowercase: str = PixaStructImageProcessor if is_vision_available() else None
def lowercase__ ( self : Optional[Any] ) -> Optional[int]:
_lowerCAmelCase = PixaStructImageProcessingTester(self )
@property
def lowercase__ ( self : List[Any] ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase__ ( self : List[str] ) -> Any:
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , """do_normalize""" ) )
self.assertTrue(hasattr(__snake_case , """do_convert_rgb""" ) )
def lowercase__ ( self : str ) -> List[str]:
_lowerCAmelCase = self.image_processor_tester.prepare_dummy_image()
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
_lowerCAmelCase = 20_48
_lowerCAmelCase = image_processor(__snake_case , return_tensors="""pt""" , max_patches=__snake_case )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.06_06 ) , atol=1E-3 , rtol=1E-3 ) )
def lowercase__ ( self : Union[str, Any] ) -> Tuple:
# Initialize image_processor
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
_lowerCAmelCase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_lowerCAmelCase = image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase__ ( self : Any ) -> Tuple:
# Initialize image_processor
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
_lowerCAmelCase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
_lowerCAmelCase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(__snake_case ):
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
_lowerCAmelCase = """Hello"""
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case , header_text=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_lowerCAmelCase = image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case , header_text=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
# Initialize image_processor
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
_lowerCAmelCase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_lowerCAmelCase = image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
# Initialize image_processor
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
_lowerCAmelCase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_lowerCAmelCase = image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class UpperCAmelCase ( snake_case_ , unittest.TestCase ):
_lowercase: Tuple = PixaStructImageProcessor if is_vision_available() else None
def lowercase__ ( self : Optional[Any] ) -> int:
_lowerCAmelCase = PixaStructImageProcessingTester(self , num_channels=4 )
_lowerCAmelCase = 3
@property
def lowercase__ ( self : List[str] ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase__ ( self : Optional[Any] ) -> List[str]:
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , """do_normalize""" ) )
self.assertTrue(hasattr(__snake_case , """do_convert_rgb""" ) )
def lowercase__ ( self : Optional[int] ) -> Tuple:
# Initialize image_processor
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
_lowerCAmelCase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_lowerCAmelCase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_lowerCAmelCase = image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 70 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A__ :
"""simple docstring"""
__magic_name__ = XGLMConfig
__magic_name__ = {}
__magic_name__ = 'gelu'
def __init__( self , __snake_case , __snake_case=1_4 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=9_9 , __snake_case=3_2 , __snake_case=2 , __snake_case=4 , __snake_case=3_7 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=5_1_2 , __snake_case=0.02 , ):
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_labels
snake_case = vocab_size
snake_case = d_model
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = ffn_dim
snake_case = activation_function
snake_case = activation_dropout
snake_case = attention_dropout
snake_case = max_position_embeddings
snake_case = initializer_range
snake_case = None
snake_case = 0
snake_case = 2
snake_case = 1
def a_ ( self ):
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def a_ ( self ):
snake_case = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
snake_case = None
if self.use_input_mask:
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = self.get_config()
snake_case = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def a_ ( self ):
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=__snake_case , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=__snake_case , )
def a_ ( self ):
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) = config_and_inputs
snake_case = {
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class A__ ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
__magic_name__ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
__magic_name__ = (TFXGLMForCausalLM,) if is_tf_available() else ()
__magic_name__ = (
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
def a_ ( self ):
snake_case = TFXGLMModelTester(self )
snake_case = ConfigTester(self , config_class=__snake_case , n_embd=3_7 )
def a_ ( self ):
self.config_tester.run_common_tests()
@slow
def a_ ( self ):
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = TFXGLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def a_ ( self ):
super().test_resize_token_embeddings()
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def a_ ( self , __snake_case=True ):
snake_case = TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
snake_case = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
snake_case = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
snake_case = model.generate(__snake_case , do_sample=__snake_case , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , __snake_case )
@slow
def a_ ( self ):
snake_case = XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
snake_case = TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
snake_case = tokenizer('''Today is a nice day and''' , return_tensors='''tf''' )
snake_case = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
snake_case = model.generate(__snake_case , do_sample=__snake_case , seed=[7, 0] )
snake_case = tokenizer.decode(output_ids[0] , skip_special_tokens=__snake_case )
snake_case = (
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(__snake_case , __snake_case )
@slow
def a_ ( self ):
snake_case = TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
snake_case = XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
snake_case = '''left'''
# use different length sentences to test batching
snake_case = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
snake_case = tokenizer(__snake_case , return_tensors='''tf''' , padding=__snake_case )
snake_case = inputs['''input_ids''']
snake_case = model.generate(input_ids=__snake_case , attention_mask=inputs['''attention_mask'''] , max_new_tokens=1_2 )
snake_case = tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
snake_case = model.generate(input_ids=__snake_case , max_new_tokens=1_2 )
snake_case = tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
snake_case = model.generate(input_ids=__snake_case , max_new_tokens=1_2 )
snake_case = tokenizer.batch_decode(__snake_case , skip_special_tokens=__snake_case )
snake_case = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__snake_case )
snake_case = tokenizer.decode(output_padded[0] , skip_special_tokens=__snake_case )
snake_case = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(__snake_case , __snake_case )
self.assertListEqual(__snake_case , [non_padded_sentence, padded_sentence] )
| 127 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCAmelCase__ = (DEISMultistepScheduler,)
lowerCAmelCase__ = (("""num_inference_steps""", 25),)
def __lowerCamelCase ( self : Tuple , **_lowerCAmelCase : Dict):
'''simple docstring'''
__lowercase ={
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
}
config.update(**__a)
return config
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : List[str]=0 , **_lowerCAmelCase : List[str]):
'''simple docstring'''
__lowercase =dict(self.forward_default_kwargs)
__lowercase =kwargs.pop('num_inference_steps' , __a)
__lowercase =self.dummy_sample
__lowercase =0.1 * sample
__lowercase =[residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowercase =self.get_scheduler_config(**__a)
__lowercase =scheduler_class(**__a)
scheduler.set_timesteps(__a)
# copy over dummy past residuals
__lowercase =dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a)
__lowercase =scheduler_class.from_pretrained(__a)
new_scheduler.set_timesteps(__a)
# copy over dummy past residuals
__lowercase =dummy_past_residuals[: new_scheduler.config.solver_order]
__lowercase =sample, sample
for t in range(__a , time_step + scheduler.config.solver_order + 1):
__lowercase =scheduler.step(__a , __a , __a , **__a).prev_sample
__lowercase =new_scheduler.step(__a , __a , __a , **__a).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
pass
def __lowerCamelCase ( self : Tuple , _lowerCAmelCase : List[str]=0 , **_lowerCAmelCase : List[str]):
'''simple docstring'''
__lowercase =dict(self.forward_default_kwargs)
__lowercase =kwargs.pop('num_inference_steps' , __a)
__lowercase =self.dummy_sample
__lowercase =0.1 * sample
__lowercase =[residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowercase =self.get_scheduler_config()
__lowercase =scheduler_class(**__a)
scheduler.set_timesteps(__a)
# copy over dummy past residuals (must be after setting timesteps)
__lowercase =dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a)
__lowercase =scheduler_class.from_pretrained(__a)
# copy over dummy past residuals
new_scheduler.set_timesteps(__a)
# copy over dummy past residual (must be after setting timesteps)
__lowercase =dummy_past_residuals[: new_scheduler.config.solver_order]
__lowercase =scheduler.step(__a , __a , __a , **__a).prev_sample
__lowercase =new_scheduler.step(__a , __a , __a , **__a).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : List[str]):
'''simple docstring'''
if scheduler is None:
__lowercase =self.scheduler_classes[0]
__lowercase =self.get_scheduler_config(**__a)
__lowercase =scheduler_class(**__a)
__lowercase =self.scheduler_classes[0]
__lowercase =self.get_scheduler_config(**__a)
__lowercase =scheduler_class(**__a)
__lowercase =1_0
__lowercase =self.dummy_model()
__lowercase =self.dummy_sample_deter
scheduler.set_timesteps(__a)
for i, t in enumerate(scheduler.timesteps):
__lowercase =model(__a , __a)
__lowercase =scheduler.step(__a , __a , __a).prev_sample
return sample
def __lowerCamelCase ( self : Any):
'''simple docstring'''
__lowercase =dict(self.forward_default_kwargs)
__lowercase =kwargs.pop('num_inference_steps' , __a)
for scheduler_class in self.scheduler_classes:
__lowercase =self.get_scheduler_config()
__lowercase =scheduler_class(**__a)
__lowercase =self.dummy_sample
__lowercase =0.1 * sample
if num_inference_steps is not None and hasattr(__a , 'set_timesteps'):
scheduler.set_timesteps(__a)
elif num_inference_steps is not None and not hasattr(__a , 'set_timesteps'):
__lowercase =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__lowercase =[residual + 0.2, residual + 0.15, residual + 0.10]
__lowercase =dummy_past_residuals[: scheduler.config.solver_order]
__lowercase =scheduler.timesteps[5]
__lowercase =scheduler.timesteps[6]
__lowercase =scheduler.step(__a , __a , __a , **__a).prev_sample
__lowercase =scheduler.step(__a , __a , __a , **__a).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =DEISMultistepScheduler(**self.get_scheduler_config())
__lowercase =self.full_loop(scheduler=__a)
__lowercase =torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_3916) < 1e-3
__lowercase =DPMSolverSinglestepScheduler.from_config(scheduler.config)
__lowercase =DPMSolverMultistepScheduler.from_config(scheduler.config)
__lowercase =UniPCMultistepScheduler.from_config(scheduler.config)
__lowercase =DEISMultistepScheduler.from_config(scheduler.config)
__lowercase =self.full_loop(scheduler=__a)
__lowercase =torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_3916) < 1e-3
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__a)
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , algorithm_type='deis' , solver_order=__a , solver_type=__a , )
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
__lowercase =self.full_loop(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
assert not torch.isnan(__a).any(), "Samples have nan numbers"
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
self.check_over_configs(lower_order_final=__a)
self.check_over_configs(lower_order_final=__a)
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__a , time_step=0)
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =self.full_loop()
__lowercase =torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_3916) < 1e-3
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__lowercase =self.full_loop(prediction_type='v_prediction')
__lowercase =torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.091) < 1e-3
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.scheduler_classes[0]
__lowercase =self.get_scheduler_config(thresholding=__a , dynamic_thresholding_ratio=0)
__lowercase =scheduler_class(**__a)
__lowercase =1_0
__lowercase =self.dummy_model()
__lowercase =self.dummy_sample_deter.half()
scheduler.set_timesteps(__a)
for i, t in enumerate(scheduler.timesteps):
__lowercase =model(__a , __a)
__lowercase =scheduler.step(__a , __a , __a).prev_sample
assert sample.dtype == torch.floataa
| 350 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class _UpperCamelCase ( A , A ):
'''simple docstring'''
lowerCAmelCase__ = """resnet"""
lowerCAmelCase__ = ["""basic""", """bottleneck"""]
def __init__( self : Any , _lowerCAmelCase : List[str]=3 , _lowerCAmelCase : Optional[int]=6_4 , _lowerCAmelCase : str=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , _lowerCAmelCase : Any=[3, 4, 6, 3] , _lowerCAmelCase : List[Any]="bottleneck" , _lowerCAmelCase : List[str]="relu" , _lowerCAmelCase : int=False , _lowerCAmelCase : int=None , _lowerCAmelCase : Any=None , **_lowerCAmelCase : Any , ):
'''simple docstring'''
super().__init__(**_lowerCAmelCase)
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types)}""")
__lowercase =num_channels
__lowercase =embedding_size
__lowercase =hidden_sizes
__lowercase =depths
__lowercase =layer_type
__lowercase =hidden_act
__lowercase =downsample_in_first_stage
__lowercase =['stem'] + [f"""stage{idx}""" for idx in range(1 , len(_lowerCAmelCase) + 1)]
__lowercase , __lowercase =get_aligned_output_features_output_indices(
out_features=_lowerCAmelCase , out_indices=_lowerCAmelCase , stage_names=self.stage_names)
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = version.parse("""1.11""" )
@property
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
return 1e-3
| 48 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
a_ = NewType('DataClass', Any)
a_ = NewType('DataClassType', Any)
def __UpperCAmelCase ( __UpperCamelCase ):
if isinstance(__UpperCamelCase , __UpperCamelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : int = {str(__UpperCamelCase ): choice for choice in choices}
return lambda __UpperCamelCase : str_to_choice.get(__UpperCamelCase , __UpperCamelCase )
def __UpperCAmelCase ( *,
__UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = dataclasses.MISSING , __UpperCamelCase = dataclasses.MISSING , __UpperCamelCase = None , **__UpperCamelCase , ):
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__lowercase : List[str] = {}
if aliases is not None:
__lowercase : Dict = aliases
if help is not None:
__lowercase : Tuple = help
return dataclasses.field(metadata=__UpperCamelCase , default=__UpperCamelCase , default_factory=__UpperCamelCase , **__UpperCamelCase )
class UpperCAmelCase_ ( _a ):
UpperCamelCase =42
def __init__( self , UpperCamelCase_ , **UpperCamelCase_ ) -> int:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__lowercase : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**lowercase__ )
if dataclasses.is_dataclass(lowercase__ ):
__lowercase : Union[str, Any] = [dataclass_types]
__lowercase : List[Any] = list(lowercase__ )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(lowercase__ )
@staticmethod
def _lowerCamelCase ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
__lowercase : Optional[Any] = F"""--{field.name}"""
__lowercase : str = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , lowercase__ ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
__lowercase : Optional[int] = kwargs.pop('''aliases''' , [] )
if isinstance(lowercase__ , lowercase__ ):
__lowercase : Optional[Any] = [aliases]
__lowercase : Union[str, Any] = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(lowercase__ , '''UnionType''' ) and isinstance(lowercase__ , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(lowercase__ ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
F""" Problem encountered in field \'{field.name}\'.""" )
if type(lowercase__ ) not in field.type.__args__:
# filter `str` in Union
__lowercase : Optional[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__lowercase : Any = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__lowercase : Dict = (
field.type.__args__[0] if isinstance(lowercase__ , field.type.__args__[1] ) else field.type.__args__[1]
)
__lowercase : Union[str, Any] = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__lowercase : Optional[int] = {}
if origin_type is Literal or (isinstance(field.type , lowercase__ ) and issubclass(field.type , lowercase__ )):
if origin_type is Literal:
__lowercase : List[str] = field.type.__args__
else:
__lowercase : Optional[Any] = [x.value for x in field.type]
__lowercase : Union[str, Any] = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
__lowercase : Optional[int] = field.default
else:
__lowercase : Optional[Any] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__lowercase : Any = copy(lowercase__ )
# Hack because type=bool in argparse does not behave as we want.
__lowercase : Any = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__lowercase : Union[str, Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__lowercase : Tuple = default
# This tells argparse we accept 0 or 1 value after --field_name
__lowercase : Dict = '''?'''
# This is the value that will get picked if we do --field_name (without value)
__lowercase : Dict = True
elif isclass(lowercase__ ) and issubclass(lowercase__ , lowercase__ ):
__lowercase : int = field.type.__args__[0]
__lowercase : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
__lowercase : List[str] = field.default_factory()
elif field.default is dataclasses.MISSING:
__lowercase : Tuple = True
else:
__lowercase : Any = field.type
if field.default is not dataclasses.MISSING:
__lowercase : int = field.default
elif field.default_factory is not dataclasses.MISSING:
__lowercase : Optional[int] = field.default_factory()
else:
__lowercase : List[Any] = True
parser.add_argument(lowercase__ , *lowercase__ , **lowercase__ )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__lowercase : str = False
parser.add_argument(F"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **lowercase__ )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> str:
if hasattr(lowercase__ , '''_argument_group_name''' ):
__lowercase : int = self.add_argument_group(dtype._argument_group_name )
else:
__lowercase : Dict = self
try:
__lowercase : List[Any] = get_type_hints(lowercase__ )
except NameError:
raise RuntimeError(
F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowercase__ ):
__lowercase : Any = '''.'''.join(map(lowercase__ , sys.version_info[:3] ) )
raise RuntimeError(
F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(lowercase__ ):
if not field.init:
continue
__lowercase : str = type_hints[field.name]
self._parse_dataclass_field(lowercase__ , lowercase__ )
def _lowerCamelCase ( self , UpperCamelCase_=None , UpperCamelCase_=False , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__lowercase : List[Any] = []
if args_filename:
args_files.append(Path(lowercase__ ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__lowercase : Optional[Any] = ArgumentParser()
args_file_parser.add_argument(lowercase__ , type=lowercase__ , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
__lowercase ,__lowercase : Any = args_file_parser.parse_known_args(args=lowercase__ )
__lowercase : Tuple = vars(lowercase__ ).get(args_file_flag.lstrip('''-''' ) , lowercase__ )
if cmd_args_file_paths:
args_files.extend([Path(lowercase__ ) for p in cmd_args_file_paths] )
__lowercase : int = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__lowercase : int = file_args + args if args is not None else file_args + sys.argv[1:]
__lowercase ,__lowercase : List[Any] = self.parse_known_args(args=lowercase__ )
__lowercase : Dict = []
for dtype in self.dataclass_types:
__lowercase : Union[str, Any] = {f.name for f in dataclasses.fields(lowercase__ ) if f.init}
__lowercase : int = {k: v for k, v in vars(lowercase__ ).items() if k in keys}
for k in keys:
delattr(lowercase__ , lowercase__ )
__lowercase : Dict = dtype(**lowercase__ )
outputs.append(lowercase__ )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(lowercase__ )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = False ) -> Tuple[DataClass, ...]:
__lowercase : str = set(args.keys() )
__lowercase : Tuple = []
for dtype in self.dataclass_types:
__lowercase : List[Any] = {f.name for f in dataclasses.fields(lowercase__ ) if f.init}
__lowercase : Optional[Any] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__lowercase : Optional[Any] = dtype(**lowercase__ )
outputs.append(lowercase__ )
if not allow_extra_keys and unused_keys:
raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(lowercase__ )}""" )
return tuple(lowercase__ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = False ) -> Tuple[DataClass, ...]:
with open(Path(lowercase__ ) , encoding='''utf-8''' ) as open_json_file:
__lowercase : str = json.loads(open_json_file.read() )
__lowercase : Union[str, Any] = self.parse_dict(lowercase__ , allow_extra_keys=lowercase__ )
return tuple(lowercase__ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = False ) -> Tuple[DataClass, ...]:
__lowercase : Any = self.parse_dict(yaml.safe_load(Path(lowercase__ ).read_text() ) , allow_extra_keys=lowercase__ )
return tuple(lowercase__ )
| 249 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Dict:
'''simple docstring'''
model.train()
__UpperCAmelCase = model(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = F.mse_loss(SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> List[Any]:
'''simple docstring'''
set_seed(4_2 )
__UpperCAmelCase = RegressionModel()
__UpperCAmelCase = deepcopy(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = RegressionDataset(length=8_0 )
__UpperCAmelCase = DataLoader(SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__UpperCAmelCase = AdamW(params=model.parameters() , lr=1e-3 )
__UpperCAmelCase = AdamW(params=ddp_model.parameters() , lr=1e-3 )
__UpperCAmelCase = LambdaLR(SCREAMING_SNAKE_CASE , lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
__UpperCAmelCase = LambdaLR(SCREAMING_SNAKE_CASE , lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
# Make a copy of `model`
if sched:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def __a ( SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
# Test when on a single CPU or GPU that the context manager does nothing
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = get_training_setup(SCREAMING_SNAKE_CASE )
# Use a single batch
__UpperCAmelCase , __UpperCAmelCase = next(iter(SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__UpperCAmelCase , __UpperCAmelCase = accelerator.gather((ddp_input, ddp_target) )
__UpperCAmelCase , __UpperCAmelCase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__UpperCAmelCase = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
def __a ( SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
# Test on distributed setup that context manager behaves properly
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = get_training_setup(SCREAMING_SNAKE_CASE )
# Use a single batch
__UpperCAmelCase , __UpperCAmelCase = next(iter(SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__UpperCAmelCase , __UpperCAmelCase = accelerator.gather((ddp_input, ddp_target) )
__UpperCAmelCase , __UpperCAmelCase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__UpperCAmelCase = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
def __a ( SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase = Accelerator(
split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = get_training_setup(SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
__UpperCAmelCase , __UpperCAmelCase = batch.values()
# Gather the distributed inputs and targs for the base model
__UpperCAmelCase , __UpperCAmelCase = accelerator.gather((ddp_input, ddp_target) )
__UpperCAmelCase , __UpperCAmelCase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__UpperCAmelCase = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def __a ( SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase = Accelerator(
split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = get_training_setup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
__UpperCAmelCase , __UpperCAmelCase = batch.values()
# Gather the distributed inputs and targs for the base model
__UpperCAmelCase , __UpperCAmelCase = accelerator.gather((ddp_input, ddp_target) )
__UpperCAmelCase , __UpperCAmelCase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
__UpperCAmelCase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def __a ( ) -> str:
'''simple docstring'''
__UpperCAmelCase = Accelerator()
__UpperCAmelCase = RegressionDataset(length=8_0 )
__UpperCAmelCase = DataLoader(SCREAMING_SNAKE_CASE , batch_size=1_6 )
__UpperCAmelCase = RegressionDataset(length=9_6 )
__UpperCAmelCase = DataLoader(SCREAMING_SNAKE_CASE , batch_size=1_6 )
__UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(SCREAMING_SNAKE_CASE )
if iteration < len(SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(SCREAMING_SNAKE_CASE )
if batch_num < len(SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def __a ( ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase = Accelerator()
__UpperCAmelCase = accelerator.state
if state.local_process_index == 0:
print('''**Test `accumulate` gradient accumulation with dataloader break**''' )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print('''**Test NOOP `no_sync` context manager**''' )
test_noop_sync(SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print('''**Test Distributed `no_sync` context manager**''' )
test_distributed_sync(SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation, ''' , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version('''<''' , '''2.0''' ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' , '''`split_batches=False`, `dispatch_batches=False`**''' , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
'''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation_with_opt_and_scheduler(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 333 | 0 |
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 snake_case__ (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :List[Any] = DebertaTokenizer
__lowerCAmelCase :List[Any] = True
__lowerCAmelCase :Union[str, Any] = DebertaTokenizerFast
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a__ : int = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
a__ : Optional[int] = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
a__ : Tuple = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
a__ : List[Any] = {'''unk_token''': '''[UNK]'''}
a__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
a__ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCamelCase__ ) )
def SCREAMING_SNAKE_CASE__( self , **__lowercase ) -> Dict:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Optional[int]:
"""simple docstring"""
a__ : Tuple = '''lower newer'''
a__ : Optional[Any] = '''lower newer'''
return input_text, output_text
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Dict = self.get_tokenizer()
a__ : Optional[Any] = '''lower newer'''
a__ : Tuple = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
a__ : Optional[Any] = tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
a__ : Union[str, Any] = tokens + [tokenizer.unk_token]
a__ : List[str] = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : int = self.get_tokenizer()
a__ : Optional[int] = tokenizer("""Hello""" , """World""" )
a__ : Optional[Any] = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["""token_type_ids"""] , UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
a__ : Tuple = tokenizer.encode("""sequence builders""" , add_special_tokens=UpperCamelCase__ )
a__ : List[Any] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=UpperCamelCase__ )
a__ : Dict = tokenizer.encode(
"""sequence builders""" , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ )
a__ : Optional[int] = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ )
a__ : List[Any] = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ )
a__ : int = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ , UpperCamelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Dict = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
a__ : Any = tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
a__ : Optional[Any] = [
'''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__ : Optional[Any] = tokenizer(UpperCamelCase__ , padding=UpperCamelCase__ )
a__ : List[str] = [tokenizer.decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ ) for seq in encoding['''input_ids''']]
# fmt: off
a__ : Optional[int] = {
'''input_ids''': [
[1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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__ : Any = [
'''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 , UpperCamelCase__ )
for expected, decoded in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
| 367 |
from string import ascii_uppercase
_lowercase : str ={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase : Dict =dict(enumerate(ascii_uppercase))
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Any = len(_lowercase)
a__ : Optional[int] = 0
while True:
if x == i:
a__ : Optional[Any] = 0
if len(_lowercase) == len(_lowercase):
break
key += key[i]
i += 1
return key
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Tuple = """"""
a__ : str = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
a__ : List[str] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : int = """"""
a__ : int = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
a__ : Dict = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
a__ : List[Any] = """THE GERMAN ATTACK"""
a__ : List[Any] = """SECRET"""
a__ : Tuple = generate_key(_lowercase , _lowercase)
a__ : str = cipher_text(_lowercase , _lowercase)
print(F'''Encrypted Text = {s}''')
print(F'''Original Text = {original_text(_lowercase , _lowercase)}''')
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 266 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case : Dict = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : int = ["""ConvNextFeatureExtractor"""]
__snake_case : int = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Tuple = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[Any] = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__snake_case : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 248 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
__snake_case : Dict = logging.get_logger(__name__)
class A__(a_ ):
"""simple docstring"""
_A : Dict = ['''pixel_values''']
def __init__( self , _lowercase = True , _lowercase = 1 / 255 , _lowercase = True , _lowercase = 8 , **_lowercase , ) -> None:
super().__init__(**_lowercase )
a_ : Tuple = do_rescale
a_ : Dict = rescale_factor
a_ : int = do_pad
a_ : Optional[int] = pad_size
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase ) -> np.ndarray:
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase = None ) -> int:
a_ , a_ : str = get_image_size(_lowercase )
a_ : Tuple = (old_height // size + 1) * size - old_height
a_ : List[Any] = (old_width // size + 1) * size - old_width
return pad(_lowercase , ((0, pad_height), (0, pad_width)) , mode="""symmetric""" , data_format=_lowercase )
def UpperCamelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ) -> List[str]:
a_ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
a_ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
a_ : Tuple = do_pad if do_pad is not None else self.do_pad
a_ : Tuple = pad_size if pad_size is not None else self.pad_size
a_ : Tuple = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
a_ : Tuple = [to_numpy_array(_lowercase ) for image in images]
if do_rescale:
a_ : Dict = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_pad:
a_ : str = [self.pad(_lowercase , size=_lowercase ) for image in images]
a_ : Optional[int] = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
a_ : Optional[Any] = {"""pixel_values""": images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 248 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["""XGLMTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["""XGLMTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XGLMForCausalLM""",
"""XGLMModel""",
"""XGLMPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""FlaxXGLMForCausalLM""",
"""FlaxXGLMModel""",
"""FlaxXGLMPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXGLMForCausalLM""",
"""TFXGLMModel""",
"""TFXGLMPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure) | 267 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["""XGLMTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["""XGLMTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XGLMForCausalLM""",
"""XGLMModel""",
"""XGLMPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""FlaxXGLMForCausalLM""",
"""FlaxXGLMModel""",
"""FlaxXGLMPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"""TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXGLMForCausalLM""",
"""TFXGLMModel""",
"""TFXGLMPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure) | 267 | 1 |
"""simple docstring"""
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 203 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
snake_case : List[str] = sum(lowercase ) / len(lowercase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 203 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = BertTokenizer
UpperCamelCase = BertTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = filter_non_english
def a__ ( self : Any ) -> Any:
"""simple docstring"""
super().setUp()
lowerCamelCase_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def a__ ( self : List[str] , A_ : List[str] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = 'unwanted, running'
return input_text, output_text
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer_class(self.vocab_file )
lowerCamelCase_ = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(A_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , [9, 6, 7, 12, 10, 11] )
def a__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = tokenizer.tokenize(A_ )
lowerCamelCase_ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = tokenizer.encode(A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
# With lower casing
lowerCamelCase_ = self.get_tokenizer(do_lower_case=A_ )
lowerCamelCase_ = self.get_rust_tokenizer(do_lower_case=A_ )
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = tokenizer.tokenize(A_ )
lowerCamelCase_ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = tokenizer.encode(A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
def a__ ( self : Dict ) -> int:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def a__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def a__ ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : str ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : Optional[int] ) -> str:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def a__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer()
lowerCamelCase_ = 'a\n\'ll !!to?\'d of, can\'t.'
lowerCamelCase_ = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(A_ ) , A_ )
def a__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
lowerCamelCase_ = {}
for i, token in enumerate(A_ ):
lowerCamelCase_ = i
lowerCamelCase_ = WordpieceTokenizer(vocab=A_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def a__ ( self : Tuple ) -> Any:
"""simple docstring"""
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def a__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def a__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def a__ ( self : int ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(A_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def a__ ( self : List[str] ) -> int:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer_class.from_pretrained('bert-base-uncased' )
lowerCamelCase_ = tokenizer.encode('sequence builders' , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer.encode('multi-sequence build' , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(A_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(A_ , A_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def a__ ( self : int ) -> str:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
lowerCamelCase_ = tokenizer_r.encode_plus(
A_ , return_attention_mask=A_ , return_token_type_ids=A_ , return_offsets_mapping=A_ , add_special_tokens=A_ , )
lowerCamelCase_ = tokenizer_r.do_lower_case if hasattr(A_ , 'do_lower_case' ) else False
lowerCamelCase_ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def a__ ( self : Any ) -> Any:
"""simple docstring"""
lowerCamelCase_ = ['的', '人', '有']
lowerCamelCase_ = ''.join(A_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCamelCase_ = True
lowerCamelCase_ = self.tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = tokenizer_p.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.convert_ids_to_tokens(A_ )
lowerCamelCase_ = tokenizer_p.convert_ids_to_tokens(A_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A_ , A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = False
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = tokenizer_r.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_p.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.convert_ids_to_tokens(A_ )
lowerCamelCase_ = tokenizer_p.convert_ids_to_tokens(A_ )
# it is expected that only the first Chinese character is not preceded by "##".
lowerCamelCase_ = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(A_ )
]
self.assertListEqual(A_ , A_ )
self.assertListEqual(A_ , A_ )
| 208 |
import math
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : float ):
'''simple docstring'''
return math.pow(lowercase , 2 ) - a
def _SCREAMING_SNAKE_CASE ( lowercase : float ):
'''simple docstring'''
return 2 * x
def _SCREAMING_SNAKE_CASE ( lowercase : float ):
'''simple docstring'''
lowerCamelCase_ = 2.0
while start <= a:
lowerCamelCase_ = math.pow(lowercase , 2 )
return start
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : int = 99_99 , lowercase : float = 0.00_0000_0000_0001 ):
'''simple docstring'''
if a < 0:
raise ValueError('math domain error' )
lowerCamelCase_ = get_initial_point(lowercase )
for _ in range(lowercase ):
lowerCamelCase_ = value
lowerCamelCase_ = value - fx(lowercase , lowercase ) / fx_derivative(lowercase )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 208 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCAmelCase : Any = Mapping[str, np.ndarray]
lowerCAmelCase : int = Mapping[str, Any] # Is a nested dict.
lowerCAmelCase : Optional[Any] = 0.01
@dataclasses.dataclass(frozen=UpperCAmelCase_ )
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_UpperCAmelCase : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_UpperCAmelCase : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_UpperCAmelCase : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_UpperCAmelCase : Optional[str] = None
# Templates used to generate this protein (prediction-only)
_UpperCAmelCase : Optional[Sequence[str]] = None
# Chain corresponding to each parent
_UpperCAmelCase : Optional[Sequence[int]] = None
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = R"(\[[A-Z]+\]\n)"
SCREAMING_SNAKE_CASE_: List[str] = [tag.strip() for tag in re.split(_UpperCAmelCase , _UpperCAmelCase ) if len(_UpperCAmelCase ) > 0]
SCREAMING_SNAKE_CASE_: Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("\n" ) for l in tags[1::2]] )
SCREAMING_SNAKE_CASE_: List[str] = ["N", "CA", "C"]
SCREAMING_SNAKE_CASE_: Any = None
SCREAMING_SNAKE_CASE_: Optional[Any] = None
SCREAMING_SNAKE_CASE_: List[str] = None
for g in groups:
if "[PRIMARY]" == g[0]:
SCREAMING_SNAKE_CASE_: Optional[int] = g[1][0].strip()
for i in range(len(_UpperCAmelCase ) ):
if seq[i] not in residue_constants.restypes:
SCREAMING_SNAKE_CASE_: Union[str, Any] = "X" # FIXME: strings are immutable
SCREAMING_SNAKE_CASE_: Tuple = np.array(
[residue_constants.restype_order.get(_UpperCAmelCase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
SCREAMING_SNAKE_CASE_: List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_UpperCAmelCase , g[1][axis].split() ) ) )
SCREAMING_SNAKE_CASE_: List[Any] = np.array(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: int = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
SCREAMING_SNAKE_CASE_: Optional[int] = np.array(list(map({"-": 0, "+": 1}.get , g[1][0].strip() ) ) )
SCREAMING_SNAKE_CASE_: Any = np.zeros(
(
len(_UpperCAmelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_UpperCAmelCase , atom_mask=_UpperCAmelCase , aatype=_UpperCAmelCase , residue_index=np.arange(len(_UpperCAmelCase ) ) , b_factors=_UpperCAmelCase , )
def A_ ( _UpperCAmelCase , _UpperCAmelCase = 0 ):
SCREAMING_SNAKE_CASE_: List[str] = []
SCREAMING_SNAKE_CASE_: Any = prot.remark
if remark is not None:
pdb_headers.append(f"REMARK {remark}" )
SCREAMING_SNAKE_CASE_: Any = prot.parents
SCREAMING_SNAKE_CASE_: Dict = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
SCREAMING_SNAKE_CASE_: Optional[int] = [p for i, p in zip(_UpperCAmelCase , _UpperCAmelCase ) if i == chain_id]
if parents is None or len(_UpperCAmelCase ) == 0:
SCREAMING_SNAKE_CASE_: Optional[int] = ["N/A"]
pdb_headers.append(f"PARENT {' '.join(_UpperCAmelCase )}" )
return pdb_headers
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = []
SCREAMING_SNAKE_CASE_: List[str] = pdb_str.split("\n" )
SCREAMING_SNAKE_CASE_: Optional[int] = prot.remark
if remark is not None:
out_pdb_lines.append(f"REMARK {remark}" )
SCREAMING_SNAKE_CASE_: List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
SCREAMING_SNAKE_CASE_: Optional[int] = []
if prot.parents_chain_index is not None:
SCREAMING_SNAKE_CASE_: Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_UpperCAmelCase ) , [] )
parent_dict[str(_UpperCAmelCase )].append(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[str] = max([int(_UpperCAmelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
SCREAMING_SNAKE_CASE_: List[str] = parent_dict.get(str(_UpperCAmelCase ) , ["N/A"] )
parents_per_chain.append(_UpperCAmelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
SCREAMING_SNAKE_CASE_: List[Any] = [["N/A"]]
def make_parent_line(_UpperCAmelCase ) -> str:
return f"PARENT {' '.join(_UpperCAmelCase )}"
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
SCREAMING_SNAKE_CASE_: Union[str, Any] = 0
for i, l in enumerate(_UpperCAmelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_UpperCAmelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = parents_per_chain[chain_counter]
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] = ["N/A"]
out_pdb_lines.append(make_parent_line(_UpperCAmelCase ) )
return "\n".join(_UpperCAmelCase )
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = residue_constants.restypes + ["X"]
def res_atoa(_UpperCAmelCase ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , "UNK" )
SCREAMING_SNAKE_CASE_: int = residue_constants.atom_types
SCREAMING_SNAKE_CASE_: List[str] = []
SCREAMING_SNAKE_CASE_: Optional[int] = prot.atom_mask
SCREAMING_SNAKE_CASE_: Optional[Any] = prot.aatype
SCREAMING_SNAKE_CASE_: Optional[Any] = prot.atom_positions
SCREAMING_SNAKE_CASE_: int = prot.residue_index.astype(np.intaa )
SCREAMING_SNAKE_CASE_: Dict = prot.b_factors
SCREAMING_SNAKE_CASE_: str = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
SCREAMING_SNAKE_CASE_: Optional[int] = get_pdb_headers(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
pdb_lines.extend(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = aatype.shape[0]
SCREAMING_SNAKE_CASE_: str = 1
SCREAMING_SNAKE_CASE_: List[Any] = 0
SCREAMING_SNAKE_CASE_: List[Any] = string.ascii_uppercase
SCREAMING_SNAKE_CASE_: int = None
# Add all atom sites.
for i in range(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_UpperCAmelCase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
SCREAMING_SNAKE_CASE_: List[Any] = "ATOM"
SCREAMING_SNAKE_CASE_: Optional[Any] = atom_name if len(_UpperCAmelCase ) == 4 else f" {atom_name}"
SCREAMING_SNAKE_CASE_: List[str] = ""
SCREAMING_SNAKE_CASE_: Optional[int] = ""
SCREAMING_SNAKE_CASE_: List[str] = 1.0_0
SCREAMING_SNAKE_CASE_: int = atom_name[0] # Protein supports only C, N, O, S, this works.
SCREAMING_SNAKE_CASE_: Optional[Any] = ""
SCREAMING_SNAKE_CASE_: Dict = "A"
if chain_index is not None:
SCREAMING_SNAKE_CASE_: int = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
SCREAMING_SNAKE_CASE_: Tuple = (
f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
f"{res_name_a:>3} {chain_tag:>1}"
f"{residue_index[i]:>4}{insertion_code:>1} "
f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
f"{occupancy:>6.2f}{b_factor:>6.2f} "
f"{element:>2}{charge:>2}"
)
pdb_lines.append(_UpperCAmelCase )
atom_index += 1
SCREAMING_SNAKE_CASE_: Optional[Any] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
SCREAMING_SNAKE_CASE_: Dict = True
SCREAMING_SNAKE_CASE_: List[str] = chain_index[i + 1]
if should_terminate:
# Close the chain.
SCREAMING_SNAKE_CASE_: int = "TER"
SCREAMING_SNAKE_CASE_: int = (
f"{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"
)
pdb_lines.append(_UpperCAmelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_UpperCAmelCase , _UpperCAmelCase ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(_UpperCAmelCase )
def A_ ( _UpperCAmelCase ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , ):
return Protein(
aatype=features["aatype"] , atom_positions=result["final_atom_positions"] , atom_mask=result["final_atom_mask"] , residue_index=features["residue_index"] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ) , chain_index=_UpperCAmelCase , remark=_UpperCAmelCase , parents=_UpperCAmelCase , parents_chain_index=_UpperCAmelCase , )
| 13 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase_ ( _lowercase : Dict , _lowercase : str , _lowercase : str , _lowercase : Optional[Any]=1024) -> List[Any]:
"""simple docstring"""
a__ , a__ : Optional[int] = [], []
a__ : Union[str, Any] = list(zip(_lowercase , _lowercase))
a__ , a__ : List[Any] = sorted_examples[0]
def is_too_big(_lowercase : Tuple):
return tok(_lowercase , return_tensors="""pt""").input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:]):
a__ : Tuple = new_src + """ """ + src
a__ : Any = new_tgt + """ """ + tgt
if is_too_big(_lowercase) or is_too_big(_lowercase): # cant fit, finalize example
finished_src.append(_lowercase)
finished_tgt.append(_lowercase)
a__ , a__ : List[Any] = src, tgt
else: # can fit, keep adding
a__ , a__ : Tuple = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(_lowercase)
finished_tgt.append(_lowercase)
return finished_src, finished_tgt
def lowerCAmelCase_ ( _lowercase : str , _lowercase : Path , _lowercase : Any , _lowercase : str) -> Tuple:
"""simple docstring"""
a__ : Any = Path(_lowercase)
save_path.mkdir(exist_ok=_lowercase)
for split in ["train"]:
a__ , a__ : List[Any] = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
a__ : Dict = [x.rstrip() for x in Path(_lowercase).open().readlines()]
a__ : Optional[Any] = [x.rstrip() for x in Path(_lowercase).open().readlines()]
a__ , a__ : List[Any] = pack_examples(_lowercase , _lowercase , _lowercase , _lowercase)
print(F'''packed {split} split from {len(_lowercase)} examples -> {len(_lowercase)}.''')
Path(save_path / F'''{split}.source''').open("""w""").write("""\n""".join(_lowercase))
Path(save_path / F'''{split}.target''').open("""w""").write("""\n""".join(_lowercase))
for split in ["val", "test"]:
a__ , a__ : Any = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(_lowercase , save_path / F'''{split}.source''')
shutil.copyfile(_lowercase , save_path / F'''{split}.target''')
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
a__ : Tuple = argparse.ArgumentParser()
parser.add_argument("""--tok_name""" , type=_lowercase , help="""like facebook/bart-large-cnn,t5-base, etc.""")
parser.add_argument("""--max_seq_len""" , type=_lowercase , default=128)
parser.add_argument("""--data_dir""" , type=_lowercase)
parser.add_argument("""--save_path""" , type=_lowercase)
a__ : List[Any] = parser.parse_args()
a__ : List[Any] = AutoTokenizer.from_pretrained(args.tok_name)
return pack_data_dir(_lowercase , Path(args.data_dir) , args.max_seq_len , args.save_path)
if __name__ == "__main__":
packer_cli()
| 170 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {'vocab_file': 'vocab.txt'}
__SCREAMING_SNAKE_CASE : Any = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
__SCREAMING_SNAKE_CASE : Any = {
'openbmb/cpm-ant-10b': 1_024,
}
def _a ( _SCREAMING_SNAKE_CASE ) -> Any:
snake_case_ = collections.OrderedDict()
with open(_SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" ) as reader:
snake_case_ = reader.readlines()
for index, token in enumerate(_SCREAMING_SNAKE_CASE ):
snake_case_ = token.rstrip("""\n""" )
snake_case_ = index
return vocab
class __A (snake_case__):
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : Any , UpperCAmelCase_ : Dict="<unk>" , UpperCAmelCase_ : List[Any]=200 ) ->Any:
"""simple docstring"""
snake_case_ = vocab
snake_case_ = unk_token
snake_case_ = max_input_chars_per_word
def lowerCAmelCase ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = list(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > self.max_input_chars_per_word:
return [self.unk_token]
snake_case_ = 0
snake_case_ = []
while start < len(UpperCAmelCase_ ):
snake_case_ = len(UpperCAmelCase_ )
snake_case_ = None
while start < end:
snake_case_ = """""".join(chars[start:end] )
if substr in self.vocab:
snake_case_ = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(UpperCAmelCase_ )
snake_case_ = end
return sub_tokens
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[str] = VOCAB_FILES_NAMES
__lowercase: str = PRETRAINED_VOCAB_FILES_MAP
__lowercase: Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase: str = ["""input_ids""", """attention_mask"""]
__lowercase: Dict = False
def __init__( self : Tuple , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any="<d>" , UpperCAmelCase_ : List[Any]="</d>" , UpperCAmelCase_ : Any="<s>" , UpperCAmelCase_ : Tuple="</s>" , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : List[Any]="<unk>" , UpperCAmelCase_ : Tuple="</n>" , UpperCAmelCase_ : Tuple="</_>" , UpperCAmelCase_ : List[Any]="left" , **UpperCAmelCase_ : Optional[int] , ) ->int:
"""simple docstring"""
requires_backends(self , ["""jieba"""] )
super().__init__(
bod_token=UpperCAmelCase_ , eod_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , line_token=UpperCAmelCase_ , space_token=UpperCAmelCase_ , padding_side=UpperCAmelCase_ , **UpperCAmelCase_ , )
snake_case_ = bod_token
snake_case_ = eod_token
snake_case_ = load_vocab(UpperCAmelCase_ )
snake_case_ = self.encoder[space_token]
snake_case_ = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
snake_case_ = collections.OrderedDict(sorted(self.encoder.items() , key=lambda UpperCAmelCase_ : x[1] ) )
snake_case_ = {v: k for k, v in self.encoder.items()}
snake_case_ = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowerCAmelCase ( self : Any ) ->int:
"""simple docstring"""
return self.encoder[self.bod_token]
@property
def lowerCAmelCase ( self : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.encoder[self.eod_token]
@property
def lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
return self.encoder["\n"]
@property
def lowerCAmelCase ( self : Dict ) ->int:
"""simple docstring"""
return len(self.encoder )
def lowerCAmelCase ( self : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase ( self : Union[str, Any] , UpperCAmelCase_ : int ) ->int:
"""simple docstring"""
snake_case_ = []
for x in jieba.cut(UpperCAmelCase_ , cut_all=UpperCAmelCase_ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCAmelCase_ ) )
return output_tokens
def lowerCAmelCase ( self : str , UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Tuple ) ->Optional[int]:
"""simple docstring"""
snake_case_ = [i for i in token_ids if i >= 0]
snake_case_ = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(UpperCAmelCase_ , **UpperCAmelCase_ )
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : str ) ->Tuple:
"""simple docstring"""
return token in self.encoder
def lowerCAmelCase ( self : int , UpperCAmelCase_ : List[str] ) ->str:
"""simple docstring"""
return "".join(UpperCAmelCase_ )
def lowerCAmelCase ( self : int , UpperCAmelCase_ : Tuple ) ->int:
"""simple docstring"""
return self.encoder.get(UpperCAmelCase_ , self.encoder.get(self.unk_token ) )
def lowerCAmelCase ( self : str , UpperCAmelCase_ : str ) ->Dict:
"""simple docstring"""
return self.decoder.get(UpperCAmelCase_ , self.unk_token )
def lowerCAmelCase ( self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if os.path.isdir(UpperCAmelCase_ ):
snake_case_ = os.path.join(
UpperCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
snake_case_ = (filename_prefix + """-""" if filename_prefix else """""") + save_directory
snake_case_ = 0
if " " in self.encoder:
snake_case_ = self.encoder[""" """]
del self.encoder[" "]
if "\n" in self.encoder:
snake_case_ = self.encoder["""\n"""]
del self.encoder["\n"]
snake_case_ = collections.OrderedDict(sorted(self.encoder.items() , key=lambda UpperCAmelCase_ : x[1] ) )
with open(UpperCAmelCase_ , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
""" Please check that the vocabulary is not corrupted!""" )
snake_case_ = token_index
writer.write(token + """\n""" )
index += 1
return (vocab_file,)
def lowerCAmelCase ( self : str , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : List[int] = None ) ->List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowerCAmelCase ( self : Optional[int] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
if token_ids_a is not None:
return [1] + ([0] * len(UpperCAmelCase_ )) + [1] + ([0] * len(UpperCAmelCase_ ))
return [1] + ([0] * len(UpperCAmelCase_ ))
| 233 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 ) -> list:
snake_case_ = length or len(_SCREAMING_SNAKE_CASE )
snake_case_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
snake_case_ , snake_case_ = list_data[i + 1], list_data[i]
snake_case_ = True
return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 233 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__lowerCAmelCase : Dict =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def UpperCAmelCase__ ( lowerCAmelCase__ :int , lowerCAmelCase__ :tuple , lowerCAmelCase__ :Path , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Any , lowerCAmelCase__ :List[str]=False , ) -> Union[str, Any]:
'''simple docstring'''
output_path.parent.mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , use_external_data_format=lowerCAmelCase__ , enable_onnx_checker=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
else:
export(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
@torch.no_grad()
def UpperCAmelCase__ ( lowerCAmelCase__ :str , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :bool = False ) -> str:
'''simple docstring'''
lowercase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowercase = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
lowercase = """cpu"""
lowercase = Path(lowerCAmelCase__ )
# VAE DECODER
lowercase = AutoencoderKL.from_pretrained(model_path + """/vae""" )
lowercase = vae_decoder.config.latent_channels
# forward only through the decoder part
lowercase = vae_decoder.decode
onnx_export(
lowerCAmelCase__ , model_args=(
torch.randn(1 , lowerCAmelCase__ , 2_5 , 2_5 ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=lowerCAmelCase__ , )
del vae_decoder
if __name__ == "__main__":
__lowerCAmelCase : Tuple =argparse.ArgumentParser()
parser.add_argument(
"""--model_path""",
type=str,
required=True,
help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
)
parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--opset""",
default=1_4,
type=int,
help="""The version of the ONNX operator set to use.""",
)
parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")
__lowerCAmelCase : Dict =parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("""SD: Done: ONNX""")
| 197 | """simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCAmelCase__ ( lowerCAmelCase__ :Any , lowerCAmelCase__ :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Path , lowerCAmelCase__ :str = None , lowerCAmelCase__ :str = None , lowerCAmelCase__ :str = None , ) -> Optional[int]:
'''simple docstring'''
if config_name_or_path is None:
lowercase = """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""
if generator_tokenizer_name_or_path is None:
lowercase = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
lowercase = question_encoder_name_or_path
lowercase = RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration
# Save model.
lowercase = RagConfig.from_pretrained(lowerCAmelCase__ )
lowercase = AutoConfig.from_pretrained(lowerCAmelCase__ )
lowercase = AutoConfig.from_pretrained(lowerCAmelCase__ )
lowercase = gen_config
lowercase = question_encoder_config
lowercase = model_class.from_pretrained_question_encoder_generator(
lowerCAmelCase__ , lowerCAmelCase__ , config=lowerCAmelCase__ )
rag_model.save_pretrained(lowerCAmelCase__ )
# Sanity check.
model_class.from_pretrained(lowerCAmelCase__ )
# Save tokenizers.
lowercase = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
lowercase = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
__lowerCAmelCase : int =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``"""
),
)
__lowerCAmelCase : List[str] =parser.parse_args()
__lowerCAmelCase : Dict =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,
)
| 197 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : List[Any] = {
'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class _a ( _a ):
A = '''xmod'''
def __init__(self, SCREAMING_SNAKE_CASE_=30522, SCREAMING_SNAKE_CASE_=768, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=3072, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.0_2, SCREAMING_SNAKE_CASE_=1E-12, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_="absolute", SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=("en_XX",), SCREAMING_SNAKE_CASE_=None, **SCREAMING_SNAKE_CASE_, ) -> List[Any]:
super().__init__(pad_token_id=_a, bos_token_id=_a, eos_token_id=_a, **_a )
UpperCAmelCase_: Union[str, Any] = vocab_size
UpperCAmelCase_: Any = hidden_size
UpperCAmelCase_: Union[str, Any] = num_hidden_layers
UpperCAmelCase_: Any = num_attention_heads
UpperCAmelCase_: Tuple = hidden_act
UpperCAmelCase_: str = intermediate_size
UpperCAmelCase_: Tuple = hidden_dropout_prob
UpperCAmelCase_: Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase_: Dict = max_position_embeddings
UpperCAmelCase_: Dict = type_vocab_size
UpperCAmelCase_: Optional[int] = initializer_range
UpperCAmelCase_: Dict = layer_norm_eps
UpperCAmelCase_: Optional[int] = position_embedding_type
UpperCAmelCase_: int = use_cache
UpperCAmelCase_: Optional[Any] = classifier_dropout
UpperCAmelCase_: List[Any] = pre_norm
UpperCAmelCase_: List[str] = adapter_reduction_factor
UpperCAmelCase_: int = adapter_layer_norm
UpperCAmelCase_: List[Any] = adapter_reuse_layer_norm
UpperCAmelCase_: Tuple = ln_before_adapter
UpperCAmelCase_: Union[str, Any] = list(_a )
UpperCAmelCase_: Any = default_language
class _a ( _a ):
@property
def __snake_case (self ) -> Tuple:
if self.task == "multiple-choice":
UpperCAmelCase_: str = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase_: List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 370 |
from collections import defaultdict
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCAmelCase_: Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCAmelCase_: List[Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(SCREAMING_SNAKE_CASE_ ) )
]
UpperCAmelCase_: Union[str, Any] = defaultdict(SCREAMING_SNAKE_CASE_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCAmelCase_: List[Any] = (1 << len(SCREAMING_SNAKE_CASE_ )) - 1
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCAmelCase_: List[Any] = self.count_ways_until(SCREAMING_SNAKE_CASE_, task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p), task_no + 1 )
# save the value.
UpperCAmelCase_: List[Any] = total_ways_util
return self.dp[mask][task_no]
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> str:
# Store the list of persons for each task
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in task_performed[i]:
self.task[j].append(SCREAMING_SNAKE_CASE_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0, 1 )
if __name__ == "__main__":
a : Optional[Any] = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
a : Optional[Any] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 82 | 0 |
_lowerCamelCase ="0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 334 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_A : List[Any] ='''\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John\",
booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
month = aug # \" 8-12\",
year = \"2006\",
address = \"Cambridge, Massachusetts, USA\",
publisher = \"Association for Machine Translation in the Americas\",
url = \"https://aclanthology.org/2006.amta-papers.25\",
pages = \"223--231\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
'''
_A : int ='''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''
_A : List[Any] ='''
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
\'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
\'num_edits\' (int): The cumulative number of edits
\'ref_length\' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}
Example 2:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}
Example 3:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}
Example 4:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}
Example 5:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def lowerCamelCase_ ( self: Optional[Any] ):
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""http://www.cs.umd.edu/~snover/tercom/""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""] , reference_urls=[
"""https://github.com/jhclark/tercom""",
] , )
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase__: Union[str, Any] , UpperCamelCase__: Optional[Any] , UpperCamelCase__: int = False , UpperCamelCase__: Dict = False , UpperCamelCase__: Dict = False , UpperCamelCase__: int = False , ):
lowerCamelCase__ : Any = len(references[0] )
if any(len(__a ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
lowerCamelCase__ : int = [[refs[i] for refs in references] for i in range(__a )]
lowerCamelCase__ : List[str] = TER(
normalized=__a , no_punct=__a , asian_support=__a , case_sensitive=__a , )
lowerCamelCase__ : Optional[int] = sb_ter.corpus_score(__a , __a )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 362 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_A : Optional[Any] =logging.get_logger(__name__)
@add_end_docstrings(_lowercase )
class _lowercase ( _lowercase ):
def __init__( self: Union[str, Any] , **UpperCamelCase__: str ):
super().__init__(**UpperCamelCase__ )
if self.framework != "pt":
raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self: Optional[Any] , UpperCamelCase__: Union[np.ndarray, bytes, str] , **UpperCamelCase__: int ):
return super().__call__(UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[Any] , **UpperCamelCase__: int ):
lowerCamelCase__ : Optional[Any] = {}
if "candidate_labels" in kwargs:
lowerCamelCase__ : str = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
lowerCamelCase__ : int = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase__: int , UpperCamelCase__: List[str]=None , UpperCamelCase__: Dict="This is a sound of {}." ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase__ : int = requests.get(UpperCamelCase__ ).content
else:
with open(UpperCamelCase__ , """rb""" ) as f:
lowerCamelCase__ : Dict = f.read()
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase__ : str = ffmpeg_read(UpperCamelCase__ , self.feature_extractor.sampling_rate )
if not isinstance(UpperCamelCase__ , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
lowerCamelCase__ : Optional[Any] = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
lowerCamelCase__ : Any = candidate_labels
lowerCamelCase__ : Any = [hypothesis_template.format(UpperCamelCase__ ) for x in candidate_labels]
lowerCamelCase__ : int = self.tokenizer(UpperCamelCase__ , return_tensors=self.framework , padding=UpperCamelCase__ )
lowerCamelCase__ : Tuple = [text_inputs]
return inputs
def lowerCamelCase_ ( self: Tuple , UpperCamelCase__: Union[str, Any] ):
lowerCamelCase__ : Any = model_inputs.pop("""candidate_labels""" )
lowerCamelCase__ : Union[str, Any] = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , UpperCamelCase__ ):
lowerCamelCase__ : Optional[int] = text_inputs[0]
else:
# Batching case.
lowerCamelCase__ : Tuple = text_inputs[0][0]
lowerCamelCase__ : Tuple = self.model(**UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase__ : str = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def lowerCamelCase_ ( self: List[str] , UpperCamelCase__: Union[str, Any] ):
lowerCamelCase__ : List[str] = model_outputs.pop("""candidate_labels""" )
lowerCamelCase__ : int = model_outputs["""logits"""][0]
if self.framework == "pt":
lowerCamelCase__ : Optional[int] = logits.softmax(dim=0 )
lowerCamelCase__ : Any = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
lowerCamelCase__ : Tuple = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(UpperCamelCase__ , UpperCamelCase__ ) , key=lambda UpperCamelCase__ : -x[0] )
]
return result
| 129 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = (UniPCMultistepScheduler,)
SCREAMING_SNAKE_CASE_ = (("num_inference_steps", 2_5),)
def a_ ( self, **lowerCAmelCase__) -> List[Any]:
snake_case_ = {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'solver_type': 'bh2',
}
config.update(**lowerCAmelCase__)
return config
def a_ ( self, lowerCAmelCase__=0, **lowerCAmelCase__) -> Any:
snake_case_ = dict(self.forward_default_kwargs)
snake_case_ = kwargs.pop('num_inference_steps', lowerCAmelCase__)
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config(**lowerCAmelCase__)
snake_case_ = scheduler_class(**lowerCAmelCase__)
scheduler.set_timesteps(lowerCAmelCase__)
# copy over dummy past residuals
snake_case_ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__)
snake_case_ = scheduler_class.from_pretrained(lowerCAmelCase__)
new_scheduler.set_timesteps(lowerCAmelCase__)
# copy over dummy past residuals
snake_case_ = dummy_past_residuals[: new_scheduler.config.solver_order]
snake_case_ , snake_case_ = sample, sample
for t in range(lowerCAmelCase__, time_step + scheduler.config.solver_order + 1):
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
snake_case_ = new_scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def a_ ( self, lowerCAmelCase__=0, **lowerCAmelCase__) -> Dict:
snake_case_ = dict(self.forward_default_kwargs)
snake_case_ = kwargs.pop('num_inference_steps', lowerCAmelCase__)
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowerCAmelCase__)
scheduler.set_timesteps(lowerCAmelCase__)
# copy over dummy past residuals (must be after setting timesteps)
snake_case_ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__)
snake_case_ = scheduler_class.from_pretrained(lowerCAmelCase__)
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase__)
# copy over dummy past residual (must be after setting timesteps)
snake_case_ = dummy_past_residuals[: new_scheduler.config.solver_order]
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
snake_case_ = new_scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def a_ ( self, lowerCAmelCase__=None, **lowerCAmelCase__) -> List[Any]:
if scheduler is None:
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config(**lowerCAmelCase__)
snake_case_ = scheduler_class(**lowerCAmelCase__)
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config(**lowerCAmelCase__)
snake_case_ = scheduler_class(**lowerCAmelCase__)
snake_case_ = 10
snake_case_ = self.dummy_model()
snake_case_ = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__)
for i, t in enumerate(scheduler.timesteps):
snake_case_ = model(lowerCAmelCase__, lowerCAmelCase__)
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__).prev_sample
return sample
def a_ ( self) -> List[str]:
snake_case_ = dict(self.forward_default_kwargs)
snake_case_ = kwargs.pop('num_inference_steps', lowerCAmelCase__)
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowerCAmelCase__)
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase__, 'set_timesteps'):
scheduler.set_timesteps(lowerCAmelCase__)
elif num_inference_steps is not None and not hasattr(lowerCAmelCase__, 'set_timesteps'):
snake_case_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case_ = [residual + 0.2, residual + 0.15, residual + 0.10]
snake_case_ = dummy_past_residuals[: scheduler.config.solver_order]
snake_case_ = scheduler.timesteps[5]
snake_case_ = scheduler.timesteps[6]
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__).prev_sample
self.assertEqual(output_a.shape, sample.shape)
self.assertEqual(output_a.shape, output_a.shape)
def a_ ( self) -> Tuple:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
snake_case_ = UniPCMultistepScheduler(**self.get_scheduler_config())
snake_case_ = self.full_loop(scheduler=lowerCAmelCase__)
snake_case_ = torch.mean(torch.abs(lowerCAmelCase__))
assert abs(result_mean.item() - 0.2464) < 1e-3
snake_case_ = DPMSolverSinglestepScheduler.from_config(scheduler.config)
snake_case_ = DEISMultistepScheduler.from_config(scheduler.config)
snake_case_ = DPMSolverMultistepScheduler.from_config(scheduler.config)
snake_case_ = UniPCMultistepScheduler.from_config(scheduler.config)
snake_case_ = self.full_loop(scheduler=lowerCAmelCase__)
snake_case_ = torch.mean(torch.abs(lowerCAmelCase__))
assert abs(result_mean.item() - 0.2464) < 1e-3
def a_ ( self) -> str:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__)
def a_ ( self) -> List[Any]:
self.check_over_configs(thresholding=lowerCAmelCase__)
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowerCAmelCase__, prediction_type=lowerCAmelCase__, sample_max_value=lowerCAmelCase__, solver_order=lowerCAmelCase__, solver_type=lowerCAmelCase__, )
def a_ ( self) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__)
def a_ ( self) -> Tuple:
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowerCAmelCase__, solver_type=lowerCAmelCase__, prediction_type=lowerCAmelCase__, )
snake_case_ = self.full_loop(
solver_order=lowerCAmelCase__, solver_type=lowerCAmelCase__, prediction_type=lowerCAmelCase__, )
assert not torch.isnan(lowerCAmelCase__).any(), "Samples have nan numbers"
def a_ ( self) -> Any:
self.check_over_configs(lower_order_final=lowerCAmelCase__)
self.check_over_configs(lower_order_final=lowerCAmelCase__)
def a_ ( self) -> int:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowerCAmelCase__, time_step=0)
def a_ ( self) -> Tuple:
snake_case_ = self.full_loop()
snake_case_ = torch.mean(torch.abs(lowerCAmelCase__))
assert abs(result_mean.item() - 0.2464) < 1e-3
def a_ ( self) -> str:
snake_case_ = self.full_loop(prediction_type='v_prediction')
snake_case_ = torch.mean(torch.abs(lowerCAmelCase__))
assert abs(result_mean.item() - 0.1014) < 1e-3
def a_ ( self) -> Dict:
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config(thresholding=lowerCAmelCase__, dynamic_thresholding_ratio=0)
snake_case_ = scheduler_class(**lowerCAmelCase__)
snake_case_ = 10
snake_case_ = self.dummy_model()
snake_case_ = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowerCAmelCase__)
for i, t in enumerate(scheduler.timesteps):
snake_case_ = model(lowerCAmelCase__, lowerCAmelCase__)
snake_case_ = scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__).prev_sample
assert sample.dtype == torch.floataa
def a_ ( self, **lowerCAmelCase__) -> str:
for scheduler_class in self.scheduler_classes:
snake_case_ = self.get_scheduler_config(**lowerCAmelCase__)
snake_case_ = scheduler_class(**lowerCAmelCase__)
scheduler.set_timesteps(scheduler.config.num_train_timesteps)
assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps
| 69 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 355 | from math import sqrt
def lowerCAmelCase( __lowerCamelCase ):
__a = 0
for i in range(1 , int(sqrt(__lowerCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__lowerCamelCase ):
total += i + n // i
elif i == sqrt(__lowerCamelCase ):
total += i
return total - n
def lowerCAmelCase( __lowerCamelCase = 1_0000 ):
__a = sum(
i
for i in range(1 , __lowerCamelCase )
if sum_of_divisors(sum_of_divisors(__lowerCamelCase ) ) == i and sum_of_divisors(__lowerCamelCase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 197 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def __lowerCAmelCase ( snake_case__ ):
if not isinstance(snake_case__ , snake_case__ ):
__UpperCamelCase : Optional[int] = F"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 298 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase = logging.getLogger()
def __lowerCAmelCase ( ):
__UpperCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument("-f" )
__UpperCamelCase : Any = parser.parse_args()
return args.f
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Dict = {}
__UpperCamelCase : Dict = os.path.join(snake_case__ , "all_results.json" )
if os.path.exists(snake_case__ ):
with open(snake_case__ , "r" ) as f:
__UpperCamelCase : Any = json.load(snake_case__ )
else:
raise ValueError(F"can't find {path}" )
return results
def __lowerCAmelCase ( ):
__UpperCamelCase : Any = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
_lowerCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@classmethod
def a_ (cls ) -> Union[str, Any]:
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__UpperCamelCase : Optional[Any] = tempfile.mkdtemp()
__UpperCamelCase : List[str] = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
__UpperCamelCase : Optional[Any] = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def a_ (cls ) -> Union[str, Any]:
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --seed=42\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
__UpperCamelCase : Tuple = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__UpperCamelCase : int = get_results(_UpperCAmelCase )
self.assertLess(result["perplexity"] , 1_0_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Any:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Optional[Any] = f"\n {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --num_train_epochs=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[Any] = get_results(_UpperCAmelCase )
self.assertLess(result["perplexity"] , 4_2 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__UpperCamelCase : int = 7 if get_gpu_count() > 1 else 2
__UpperCamelCase : int = self.get_auto_remove_tmp_dir()
__UpperCamelCase : str = f"\n {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : List[Any] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Any:
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : str = f"\n {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --seed=42\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(_UpperCAmelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 2_8 )
self.assertGreaterEqual(result["eval_exact"] , 2_8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Dict:
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[str] = f"\n {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/swag/sample.json\n --validation_file tests/fixtures/tests_samples/swag/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=20\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Tuple = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase : str = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Dict = f"\n {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Dict = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_rouge1"] , 1_0 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n --model_name_or_path sshleifer/student_marian_en_ro_6_1\n --source_lang en\n --target_lang ro\n --train_file tests/fixtures/tests_samples/wmt16/sample.json\n --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --num_beams=6\n --learning_rate=3e-3\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --source_lang en_XX\n --target_lang ro_RO\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : List[Any] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_bleu"] , 3_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "translation_no_trainer" ) ) )
@slow
def a_ (self ) -> List[Any]:
__UpperCamelCase : Tuple = logging.StreamHandler(sys.stdout )
logger.addHandler(_UpperCAmelCase )
__UpperCamelCase : Dict = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n --dataset_name huggingface/semantic-segmentation-test-sample\n --output_dir {tmp_dir}\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Tuple:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Optional[Any] = f"\n {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n --model_name_or_path google/vit-base-patch16-224-in21k\n --dataset_name hf-internal-testing/cats_vs_dogs_sample\n --learning_rate 1e-4\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 1\n --max_train_steps 2\n --train_val_split 0.1\n --seed 42\n --output_dir {tmp_dir}\n --with_tracking\n --checkpointing_steps 1\n ".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
__UpperCamelCase : str = get_results(_UpperCAmelCase )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "image_classification_no_trainer" ) ) )
| 298 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ):
'''simple docstring'''
lowerCamelCase : List[str] = HfArgumentParser(a_ )
lowerCamelCase : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCamelCase : str = TensorFlowBenchmark(args=a_ )
try:
lowerCamelCase : int = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCamelCase : Optional[Any] = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
lowerCamelCase : int = ' '.join(str(a_ ).split(' ' )[:-1] )
lowerCamelCase : List[str] = ''
lowerCamelCase : Dict = eval(str(a_ ).split(' ' )[-1] )
lowerCamelCase : Dict = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(a_ )
if len(a_ ) > 0:
lowerCamelCase : str = full_error_msg + begin_error_msg + str(a_ )
raise ValueError(a_ )
benchmark.run()
if __name__ == "__main__":
main()
| 205 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def UpperCAmelCase ( ):
'''simple docstring'''
if os.name == "nt":
lowerCamelCase : Optional[int] = CursorInfo()
lowerCamelCase : Union[str, Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(a_, ctypes.byref(a_ ) )
lowerCamelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(a_, ctypes.byref(a_ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def UpperCAmelCase ( ):
'''simple docstring'''
if os.name == "nt":
lowerCamelCase : List[str] = CursorInfo()
lowerCamelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(a_, ctypes.byref(a_ ) )
lowerCamelCase : Optional[Any] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(a_, ctypes.byref(a_ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def UpperCAmelCase ( ):
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 205 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). ", UpperCAmelCase_, )
class lowercase__ ( UpperCAmelCase_ ):
_UpperCAmelCase :Optional[int] = RobertaConfig
_UpperCAmelCase :List[str] = """roberta"""
def __init__( self : int , snake_case__ : Union[str, Any] ):
super().__init__(__lowercase )
lowerCamelCase_ : Dict =RobertaEmbeddings(__lowercase )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ", UpperCAmelCase_, )
class lowercase__ ( UpperCAmelCase_ ):
_UpperCAmelCase :List[Any] = RobertaConfig
_UpperCAmelCase :str = """roberta"""
def __init__( self : Optional[Any] , snake_case__ : Any ):
super().__init__(__lowercase )
lowerCamelCase_ : List[str] =config.num_labels
lowerCamelCase_ : List[Any] =config.num_hidden_layers
lowerCamelCase_ : Optional[Any] =DeeRobertaModel(__lowercase )
lowerCamelCase_ : Any =nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase_ : str =nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(__lowercase )
def UpperCAmelCase__ ( self : Dict , snake_case__ : List[str]=None , snake_case__ : Optional[Any]=None , snake_case__ : Union[str, Any]=None , snake_case__ : Optional[Any]=None , snake_case__ : Optional[Any]=None , snake_case__ : Dict=None , snake_case__ : Optional[Any]=None , snake_case__ : Tuple=-1 , snake_case__ : int=False , ):
lowerCamelCase_ : List[str] =self.num_layers
try:
lowerCamelCase_ : Tuple =self.roberta(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , position_ids=__lowercase , head_mask=__lowercase , inputs_embeds=__lowercase , )
lowerCamelCase_ : int =outputs[1]
lowerCamelCase_ : Optional[int] =self.dropout(__lowercase )
lowerCamelCase_ : Any =self.classifier(__lowercase )
lowerCamelCase_ : Tuple =(logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowerCamelCase_ : Optional[Any] =e.message
lowerCamelCase_ : List[str] =e.exit_layer
lowerCamelCase_ : Any =outputs[0]
if not self.training:
lowerCamelCase_ : Optional[Any] =entropy(__lowercase )
lowerCamelCase_ : Dict =[]
lowerCamelCase_ : int =[]
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowerCamelCase_ : Dict =MSELoss()
lowerCamelCase_ : str =loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
lowerCamelCase_ : Optional[Any] =CrossEntropyLoss()
lowerCamelCase_ : Any =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
lowerCamelCase_ : List[Any] =[]
for highway_exit in outputs[-1]:
lowerCamelCase_ : Any =highway_exit[0]
if not self.training:
highway_logits_all.append(__lowercase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
lowerCamelCase_ : Any =MSELoss()
lowerCamelCase_ : Optional[Any] =loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
lowerCamelCase_ : Union[str, Any] =CrossEntropyLoss()
lowerCamelCase_ : Union[str, Any] =loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__lowercase )
if train_highway:
lowerCamelCase_ : Any =(sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
lowerCamelCase_ : Union[str, Any] =(loss,) + outputs
if not self.training:
lowerCamelCase_ : Tuple =outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowerCamelCase_ : List[str] =(
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 144 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for attribute in key.split('''.''' ):
__UpperCamelCase :str = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if weight_type is not None:
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).shape
else:
__UpperCamelCase :Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
__UpperCamelCase :str = value
elif weight_type == "weight_g":
__UpperCamelCase :List[str] = value
elif weight_type == "weight_v":
__UpperCamelCase :str = value
elif weight_type == "bias":
__UpperCamelCase :Union[str, Any] = value
else:
__UpperCamelCase :str = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[Any] = []
__UpperCamelCase :int = fairseq_model.state_dict()
__UpperCamelCase :List[Any] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__UpperCamelCase :List[Any] = False
if "conv_layers" in name:
load_conv_layer(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
__UpperCamelCase :List[str] = True
else:
for key, mapped_key in MAPPING.items():
__UpperCamelCase :Dict = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
__UpperCamelCase :Optional[Any] = True
if "*" in mapped_key:
__UpperCamelCase :List[str] = name.split(SCREAMING_SNAKE_CASE )[0].split('''.''' )[-2]
__UpperCamelCase :Optional[int] = mapped_key.replace('''*''' , SCREAMING_SNAKE_CASE )
if "weight_g" in name:
__UpperCamelCase :int = '''weight_g'''
elif "weight_v" in name:
__UpperCamelCase :List[Any] = '''weight_v'''
elif "weight" in name:
__UpperCamelCase :Dict = '''weight'''
elif "bias" in name:
__UpperCamelCase :Dict = '''bias'''
else:
__UpperCamelCase :Dict = None
set_recursively(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(SCREAMING_SNAKE_CASE )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = full_name.split('''conv_layers.''' )[-1]
__UpperCamelCase :Optional[int] = name.split('''.''' )
__UpperCamelCase :str = int(items[0] )
__UpperCamelCase :List[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
__UpperCamelCase :Dict = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
__UpperCamelCase :Any = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
__UpperCamelCase :int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
__UpperCamelCase :Union[str, Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(SCREAMING_SNAKE_CASE )
@torch.no_grad()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
if config_path is not None:
__UpperCamelCase :Tuple = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Optional[int] = HubertConfig()
if is_finetuned:
if dict_path:
__UpperCamelCase :Optional[int] = Dictionary.load(SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__UpperCamelCase :Optional[int] = target_dict.pad_index
__UpperCamelCase :Dict = target_dict.bos_index
__UpperCamelCase :str = target_dict.eos_index
__UpperCamelCase :Dict = len(target_dict.symbols )
__UpperCamelCase :List[Any] = os.path.join(SCREAMING_SNAKE_CASE , '''vocab.json''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE ) )
return
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , SCREAMING_SNAKE_CASE )
__UpperCamelCase :Optional[int] = WavaVecaCTCTokenizer(
SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=SCREAMING_SNAKE_CASE , )
__UpperCamelCase :Union[str, Any] = True if config.feat_extract_norm == '''layer''' else False
__UpperCamelCase :Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE , return_attention_mask=SCREAMING_SNAKE_CASE , )
__UpperCamelCase :Any = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = HubertForCTC(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :str = HubertModel(SCREAMING_SNAKE_CASE )
if is_finetuned:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__UpperCamelCase :Dict = model[0].eval()
recursively_load_weights(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__lowercase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 43 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 277 |
__A = 6_5521
def __a ( lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
UpperCAmelCase_= 1
UpperCAmelCase_= 0
for plain_chr in plain_text:
UpperCAmelCase_= (a + ord(lowerCAmelCase_ )) % MOD_ADLER
UpperCAmelCase_= (b + a) % MOD_ADLER
return (b << 16) | a
| 277 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> Any:
super().__init__()
if safety_checker is None:
logger.warning(
F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
speech_model=UpperCamelCase__ , speech_processor=UpperCamelCase__ , vae=UpperCamelCase__ , text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , )
def _lowercase ( self , UpperCamelCase__ = "auto" ) -> Optional[Any]:
if slice_size == "auto":
lowerCamelCase : Union[str, Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCamelCase__ )
def _lowercase ( self ) -> Union[str, Any]:
self.enable_attention_slicing(UpperCamelCase__ )
@torch.no_grad()
def __call__( self , UpperCamelCase__ , UpperCamelCase__=1_6000 , UpperCamelCase__ = 512 , UpperCamelCase__ = 512 , UpperCamelCase__ = 50 , UpperCamelCase__ = 7.5 , UpperCamelCase__ = None , UpperCamelCase__ = 1 , UpperCamelCase__ = 0.0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = "pil" , UpperCamelCase__ = True , UpperCamelCase__ = None , UpperCamelCase__ = 1 , **UpperCamelCase__ , ) -> str:
lowerCamelCase : Dict = self.speech_processor.feature_extractor(
UpperCamelCase__ , return_tensors="pt" , sampling_rate=UpperCamelCase__ ).input_features.to(self.device )
lowerCamelCase : Any = self.speech_model.generate(UpperCamelCase__ , max_length=48_0000 )
lowerCamelCase : List[str] = self.speech_processor.tokenizer.batch_decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , normalize=UpperCamelCase__ )[
0
]
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : str = 1
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : Any = len(UpperCamelCase__ )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__ )}''' )
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(UpperCamelCase__ )}.''' )
# get prompt text embeddings
lowerCamelCase : Union[str, Any] = self.tokenizer(
UpperCamelCase__ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
lowerCamelCase : List[str] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase : Any = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase : Any = text_input_ids[:, : self.tokenizer.model_max_length]
lowerCamelCase : Any = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = text_embeddings.shape
lowerCamelCase : Dict = text_embeddings.repeat(1 , UpperCamelCase__ , 1 )
lowerCamelCase : int = text_embeddings.view(bs_embed * num_images_per_prompt , UpperCamelCase__ , -1 )
# 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.
lowerCamelCase : Union[str, Any] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase : List[str]
if negative_prompt is None:
lowerCamelCase : Dict = [""] * batch_size
elif type(UpperCamelCase__ ) is not type(UpperCamelCase__ ):
raise TypeError(
F'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__ )} !='''
F''' {type(UpperCamelCase__ )}.''' )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : Union[str, Any] = [negative_prompt]
elif batch_size != len(UpperCamelCase__ ):
raise ValueError(
F'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__ )}, but `prompt`:'''
F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
lowerCamelCase : Tuple = negative_prompt
lowerCamelCase : Any = text_input_ids.shape[-1]
lowerCamelCase : Dict = self.tokenizer(
UpperCamelCase__ , padding="max_length" , max_length=UpperCamelCase__ , truncation=UpperCamelCase__ , return_tensors="pt" , )
lowerCamelCase : Optional[int] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase : Optional[Any] = uncond_embeddings.shape[1]
lowerCamelCase : Dict = uncond_embeddings.repeat(1 , UpperCamelCase__ , 1 )
lowerCamelCase : List[str] = uncond_embeddings.view(batch_size * num_images_per_prompt , UpperCamelCase__ , -1 )
# 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
lowerCamelCase : Dict = 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`.
lowerCamelCase : Optional[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase : str = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase : Optional[int] = torch.randn(UpperCamelCase__ , generator=UpperCamelCase__ , device="cpu" , dtype=UpperCamelCase__ ).to(
self.device )
else:
lowerCamelCase : Dict = torch.randn(UpperCamelCase__ , generator=UpperCamelCase__ , device=self.device , dtype=UpperCamelCase__ )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase : List[str] = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCamelCase__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase : Any = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase : Dict = 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]
lowerCamelCase : int = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase : List[str] = {}
if accepts_eta:
lowerCamelCase : Tuple = eta
for i, t in enumerate(self.progress_bar(UpperCamelCase__ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase : List[Any] = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
# predict the noise residual
lowerCamelCase : Optional[int] = self.unet(UpperCamelCase__ , UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase , lowerCamelCase : Tuple = noise_pred.chunk(2 )
lowerCamelCase : Any = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase : Optional[int] = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[str] = 1 / 0.18215 * latents
lowerCamelCase : List[Any] = self.vae.decode(UpperCamelCase__ ).sample
lowerCamelCase : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase : int = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCamelCase : Optional[int] = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=UpperCamelCase__ , nsfw_content_detected=UpperCamelCase__ )
| 48 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> list:
lowerCamelCase : Dict = len(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = []
for i in range(len(_SCREAMING_SNAKE_CASE ) - pat_len + 1 ):
lowerCamelCase : Dict = True
for j in range(_SCREAMING_SNAKE_CASE ):
if s[i + j] != pattern[j]:
lowerCamelCase : Optional[int] = False
break
if match_found:
position.append(_SCREAMING_SNAKE_CASE )
return position
if __name__ == "__main__":
assert naive_pattern_search('ABCDEFG', 'DE') == [3]
print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
| 48 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase_ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')
UpperCAmelCase_ = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def lowerCamelCase__ ( A__ : Dict ):
'''simple docstring'''
with open(__lowerCAmelCase , """rb""" ) as f:
__lowerCamelCase = Image.open(__lowerCAmelCase )
return im.convert("""RGB""" )
@dataclass
class lowerCamelCase__:
UpperCAmelCase__ : Optional[str] = field(
default=lowerCamelCase__ , metadata={
'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
} , )
UpperCAmelCase__ : Optional[str] = field(
default=lowerCamelCase__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
UpperCAmelCase__ : Optional[str] = field(default=lowerCamelCase__ , metadata={'help': 'A folder containing the training data.'})
UpperCAmelCase__ : Optional[str] = field(default=lowerCamelCase__ , metadata={'help': 'A folder containing the validation data.'})
UpperCAmelCase__ : Optional[float] = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'})
UpperCAmelCase__ : Optional[int] = field(
default=lowerCamelCase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
UpperCAmelCase__ : Optional[int] = field(
default=lowerCamelCase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def lowerCAmelCase__ ( self: Any ):
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
"""You must specify either a dataset name from the hub or a train and/or validation directory.""" )
@dataclass
class lowerCamelCase__:
UpperCAmelCase__ : str = field(
default='google/vit-base-patch16-224-in21k' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
UpperCAmelCase__ : Optional[str] = field(
default=lowerCamelCase__ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCamelCase__)} , )
UpperCAmelCase__ : Optional[str] = field(
default=lowerCamelCase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
UpperCAmelCase__ : Optional[str] = field(
default=lowerCamelCase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'})
UpperCAmelCase__ : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
UpperCAmelCase__ : str = field(default=lowerCamelCase__ , metadata={'help': 'Name or path of preprocessor config.'})
UpperCAmelCase__ : bool = field(
default=lowerCamelCase__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
UpperCAmelCase__ : bool = field(
default=lowerCamelCase__ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def lowerCamelCase__ ( A__ : Optional[int] ):
'''simple docstring'''
__lowerCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
__lowerCamelCase = torch.tensor([example["""labels"""] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = 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.
__lowerCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCamelCase = 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_image_classification""" , __lowerCAmelCase , __lowerCAmelCase )
# 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()
__lowerCamelCase = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
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.
__lowerCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCamelCase = 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 )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
__lowerCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="""image-classification""" , use_auth_token=True if model_args.use_auth_token else None , )
else:
__lowerCamelCase = {}
if data_args.train_dir is not None:
__lowerCamelCase = os.path.join(data_args.train_dir , """**""" )
if data_args.validation_dir is not None:
__lowerCamelCase = os.path.join(data_args.validation_dir , """**""" )
__lowerCamelCase = load_dataset(
"""imagefolder""" , data_files=__lowerCAmelCase , cache_dir=model_args.cache_dir , task="""image-classification""" , )
# If we don't have a validation split, split off a percentage of train as validation.
__lowerCamelCase = None if '''validation''' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __lowerCAmelCase ) and data_args.train_val_split > 0.0:
__lowerCamelCase = dataset['''train'''].train_test_split(data_args.train_val_split )
__lowerCamelCase = split['''train''']
__lowerCamelCase = split['''test''']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__lowerCamelCase = dataset['''train'''].features['''labels'''].names
__lowerCamelCase = {}, {}
for i, label in enumerate(__lowerCAmelCase ):
__lowerCamelCase = str(__lowerCAmelCase )
__lowerCamelCase = label
# Load the accuracy metric from the datasets package
__lowerCamelCase = evaluate.load("""accuracy""" )
# Define our 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__ : Dict ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
__lowerCamelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__lowerCAmelCase ) , labelaid=__lowerCAmelCase , idalabel=__lowerCAmelCase , finetuning_task="""image-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCamelCase = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
__lowerCamelCase = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or 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 , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
__lowerCamelCase = image_processor.size['''shortest_edge''']
else:
__lowerCamelCase = (image_processor.size['''height'''], image_processor.size['''width'''])
__lowerCamelCase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
__lowerCamelCase = Compose(
[
RandomResizedCrop(__lowerCAmelCase ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
__lowerCamelCase = Compose(
[
Resize(__lowerCAmelCase ),
CenterCrop(__lowerCAmelCase ),
ToTensor(),
normalize,
] )
def train_transforms(A__ : Any ):
__lowerCamelCase = [
_train_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch['''image''']
]
return example_batch
def val_transforms(A__ : List[str] ):
__lowerCamelCase = [_val_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch['''image''']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
__lowerCamelCase = (
dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(__lowerCAmelCase )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
__lowerCamelCase = (
dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(__lowerCAmelCase )
# Initalize our trainer
__lowerCamelCase = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=dataset["""train"""] if training_args.do_train else None , eval_dataset=dataset["""validation"""] if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
__lowerCamelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCamelCase = last_checkpoint
__lowerCamelCase = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__lowerCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Write model card and (optionally) push to hub
__lowerCamelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''image-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''image-classification''', '''vision'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__lowerCAmelCase )
else:
trainer.create_model_card(**__lowerCAmelCase )
if __name__ == "__main__":
main()
| 361 |
from math import ceil, sqrt
def lowerCamelCase__ ( A__ : int = 1000000 ):
'''simple docstring'''
__lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCamelCase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
__lowerCamelCase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 29 | 0 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
a_ = 'pytorch_model.bin'
a_ = 'pytorch_model.bin.index.json'
a_ = 'adapter_config.json'
a_ = 'adapter_model.bin'
a_ = 'adapter_model.safetensors'
a_ = 'tf_model.h5'
a_ = 'tf_model.h5.index.json'
a_ = 'model.ckpt'
a_ = 'flax_model.msgpack'
a_ = 'flax_model.msgpack.index.json'
a_ = 'model.safetensors'
a_ = 'model.safetensors.index.json'
a_ = 'config.json'
a_ = 'preprocessor_config.json'
a_ = FEATURE_EXTRACTOR_NAME
a_ = 'generation_config.json'
a_ = 'modelcard.json'
a_ = '▁'
a_ = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
a_ = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
a_ = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
a_ = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def lowerCamelCase__ ( _a):
if version.parse(_a) < version.parse(_a):
if "dev" in min_version:
SCREAMING_SNAKE_CASE : Optional[Any] = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
SCREAMING_SNAKE_CASE : List[Any] = f"This example requires a minimum version of {min_version},"
error_message += f" but the version found is {__version__}.\n"
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers.") | 76 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ = logging.get_logger(__name__)
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int], *_lowerCamelCase : Union[str, Any], **_lowerCamelCase : Dict ):
'''simple docstring'''
warnings.warn(
'''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use SegformerImageProcessor instead.''', _lowerCamelCase, )
super().__init__(*_lowerCamelCase, **_lowerCamelCase )
| 266 | 0 |
"""simple docstring"""
import pprint
import requests
lowerCAmelCase__ = '''https://zenquotes.io/api'''
def a__ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def a__ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/random" ).json()
if __name__ == "__main__":
lowerCAmelCase__ = random_quotes()
pprint.pprint(response)
| 357 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Dict ="autoformer"
a : Dict ={
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , snake_case__ = None , snake_case__ = None , snake_case__ = "student_t" , snake_case__ = "nll" , snake_case__ = 1 , snake_case__ = [1, 2, 3, 4, 5, 6, 7] , snake_case__ = True , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = None , snake_case__ = None , snake_case__ = 64 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 32 , snake_case__ = 32 , snake_case__ = "gelu" , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 100 , snake_case__ = 0.02 , snake_case__ = True , snake_case__=True , snake_case__ = 10 , snake_case__ = 25 , snake_case__ = 3 , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Any = prediction_length
lowerCAmelCase : Dict = context_length if context_length is not None else prediction_length
lowerCAmelCase : Tuple = distribution_output
lowerCAmelCase : List[Any] = loss
lowerCAmelCase : int = input_size
lowerCAmelCase : str = num_time_features
lowerCAmelCase : str = lags_sequence
lowerCAmelCase : List[str] = scaling
lowerCAmelCase : List[Any] = num_dynamic_real_features
lowerCAmelCase : Tuple = num_static_real_features
lowerCAmelCase : Dict = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Any = cardinality
else:
lowerCAmelCase : Union[str, Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Tuple = embedding_dimension
else:
lowerCAmelCase : Any = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase : Any = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase : str = input_size * len(self.lags_sequence ) + self._number_of_features
lowerCAmelCase : Any = d_model
lowerCAmelCase : List[str] = encoder_attention_heads
lowerCAmelCase : Union[str, Any] = decoder_attention_heads
lowerCAmelCase : Optional[int] = encoder_ffn_dim
lowerCAmelCase : Optional[Any] = decoder_ffn_dim
lowerCAmelCase : int = encoder_layers
lowerCAmelCase : int = decoder_layers
lowerCAmelCase : List[Any] = dropout
lowerCAmelCase : Optional[int] = attention_dropout
lowerCAmelCase : Union[str, Any] = activation_dropout
lowerCAmelCase : Optional[int] = encoder_layerdrop
lowerCAmelCase : Dict = decoder_layerdrop
lowerCAmelCase : Tuple = activation_function
lowerCAmelCase : Optional[Any] = init_std
lowerCAmelCase : List[Any] = use_cache
# Autoformer
lowerCAmelCase : Any = label_length
lowerCAmelCase : Any = moving_average
lowerCAmelCase : Optional[Any] = autocorrelation_factor
super().__init__(is_encoder_decoder=snake_case__ , **snake_case__ )
@property
def lowercase__ ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 133 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = SwinConfig(image_size=1_92 )
if "base" in model_name:
__SCREAMING_SNAKE_CASE = 6
__SCREAMING_SNAKE_CASE = 1_28
__SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
elif "large" in model_name:
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = 1_92
__SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
else:
raise ValueError("""Model not supported, only supports base and large variants""" )
__SCREAMING_SNAKE_CASE = window_size
__SCREAMING_SNAKE_CASE = embed_dim
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = num_heads
return config
def a__ ( a__ ):
"""simple docstring"""
if "encoder.mask_token" in name:
__SCREAMING_SNAKE_CASE = name.replace("""encoder.mask_token""" , """embeddings.mask_token""" )
if "encoder.patch_embed.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace("""encoder.patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "encoder.patch_embed.norm" in name:
__SCREAMING_SNAKE_CASE = name.replace("""encoder.patch_embed.norm""" , """embeddings.norm""" )
if "attn.proj" in name:
__SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "encoder.norm.weight":
__SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "encoder.norm.bias":
__SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "decoder" in name:
pass
else:
__SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def a__ ( a__ , a__ ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__SCREAMING_SNAKE_CASE = orig_state_dict.pop(a__ )
if "attn_mask" in key:
pass
elif "qkv" in key:
__SCREAMING_SNAKE_CASE = key.split(""".""" )
__SCREAMING_SNAKE_CASE = int(key_split[2] )
__SCREAMING_SNAKE_CASE = int(key_split[4] )
__SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__SCREAMING_SNAKE_CASE = val[:dim, :]
__SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
__SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
__SCREAMING_SNAKE_CASE = val[
:dim
]
__SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
__SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
__SCREAMING_SNAKE_CASE = val
return orig_state_dict
def a__ ( a__ , a__ , a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch.load(a__ , map_location="""cpu""" )["""model"""]
__SCREAMING_SNAKE_CASE = get_swin_config(a__ )
__SCREAMING_SNAKE_CASE = SwinForMaskedImageModeling(a__ )
model.eval()
__SCREAMING_SNAKE_CASE = convert_state_dict(a__ , a__ )
model.load_state_dict(a__ )
__SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__SCREAMING_SNAKE_CASE = ViTImageProcessor(size={"""height""": 1_92, """width""": 1_92} )
__SCREAMING_SNAKE_CASE = Image.open(requests.get(a__ , stream=a__ ).raw )
__SCREAMING_SNAKE_CASE = image_processor(images=a__ , return_tensors="""pt""" )
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**a__ ).logits
print(outputs.keys() )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(a__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(a__ )
if push_to_hub:
print(F'Pushing model and image processor for {model_name} to hub' )
model.push_to_hub(F'microsoft/{model_name}' )
image_processor.push_to_hub(F'microsoft/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='swin-base-simmim-window6-192',
type=str,
choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
help='Name of the Swin SimMIM model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 267 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
UpperCAmelCase : Any = [
'EAGER',
'AOT_EAGER',
'INDUCTOR',
'NVFUSER',
'AOT_NVFUSER',
'AOT_CUDAGRAPHS',
'OFI',
'FX2TRT',
'ONNXRT',
'IPEX',
]
def a__ ( a__ , a__=None , a__=None , a__=None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = True
while ask_again:
__SCREAMING_SNAKE_CASE = input(a__ )
try:
if default is not None and len(a__ ) == 0:
return default
return convert_value(a__ ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(a__ )
def a__ ( a__ , a__=[] , a__=None , a__=0 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BulletMenu(a__ , a__ )
__SCREAMING_SNAKE_CASE = menu.run(default_choice=a__ )
return convert_value(a__ ) if convert_value is not None else result
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )
def a__ ( a__ ):
"""simple docstring"""
return {"yes": True, "no": False}[value.lower()]
class lowerCAmelCase__ ( argparse.RawDescriptionHelpFormatter ):
"""simple docstring"""
def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = super()._format_usage(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = usage.replace("""<command> [<args>] """ , """""" )
return usage
| 267 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowercase__ ='bert-base-cased'
lowercase__ ='google/pegasus-xsum'
lowercase__ =[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase__ =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase__ ='patrickvonplaten/t5-tiny-random'
lowercase__ ='sshleifer/bart-tiny-random'
lowercase__ ='sshleifer/tiny-mbart'
lowercase__ ='sshleifer/tiny-marian-en-de'
def __UpperCamelCase ( lowerCAmelCase__ : Path , lowerCAmelCase__ : list ):
__a : List[Any] = '''\n'''.join(lowerCAmelCase__ )
Path(lowerCAmelCase__ ).open('''w''' ).writelines(lowerCAmelCase__ )
def __UpperCamelCase ( lowerCAmelCase__ : int ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCAmelCase__ , f"{split}.source" ) , lowerCAmelCase__ )
_dump_articles(os.path.join(lowerCAmelCase__ , f"{split}.target" ) , lowerCAmelCase__ )
return tmp_dir
class UpperCamelCase__ ( __lowercase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def lowerCAmelCase (self : int , snake_case_ : int ):
__a : Optional[Any] = AutoTokenizer.from_pretrained(snake_case_ )
__a : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__a : Union[str, Any] = max(len(tokenizer.encode(snake_case_ ) ) for a in ARTICLES )
__a : str = max(len(tokenizer.encode(snake_case_ ) ) for a in SUMMARIES )
__a : str = 4
__a : Dict = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__a , __a : Any = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
__a : List[Any] = SeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=snake_case_ , max_target_length=snake_case_ , src_lang=snake_case_ , tgt_lang=snake_case_ , )
__a : Dict = DataLoader(snake_case_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(snake_case_ , snake_case_ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__a : Dict = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def lowerCAmelCase (self : Optional[Any] , snake_case_ : str ):
__a : Union[str, Any] = AutoTokenizer.from_pretrained(snake_case_ )
__a : str = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__a : Any = max(len(tokenizer.encode(snake_case_ ) ) for a in ARTICLES )
__a : Any = max(len(tokenizer.encode(snake_case_ ) ) for a in SUMMARIES )
__a : Dict = 4
__a : Optional[int] = LegacySeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=2_0 , max_target_length=snake_case_ , )
__a : Optional[Any] = DataLoader(snake_case_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def lowerCAmelCase (self : List[str] ):
__a : int = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
__a : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__a : Optional[int] = tmp_dir.joinpath('''train.source''' ).open().readlines()
__a : List[Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(snake_case_ , snake_case_ , 1_2_8 , snake_case_ )
__a : Optional[Any] = {x.name for x in tmp_dir.iterdir()}
__a : Union[str, Any] = {x.name for x in save_dir.iterdir()}
__a : str = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(snake_case_ ) < len(snake_case_ )
assert len(snake_case_ ) == 1
assert len(packed_examples[0] ) == sum(len(snake_case_ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def lowerCAmelCase (self : Any ):
if not FAIRSEQ_AVAILABLE:
return
__a , __a , __a : Any = self._get_dataset(max_len=6_4 )
__a : int = 6_4
__a : List[str] = ds.make_dynamic_sampler(snake_case_ , required_batch_size_multiple=snake_case_ )
__a : List[str] = [len(snake_case_ ) for x in batch_sampler]
assert len(set(snake_case_ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(snake_case_ ) == len(snake_case_ ) # no dropped or added examples
__a : Union[str, Any] = DataLoader(snake_case_ , batch_sampler=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 )
__a : Tuple = []
__a : Union[str, Any] = []
for batch in data_loader:
__a : Any = batch['''input_ids'''].shape
__a : str = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__a : Optional[Any] = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(snake_case_ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(snake_case_ )
assert num_src_per_batch[0] == max(snake_case_ )
if failures:
raise AssertionError(f"too many tokens in {len(snake_case_ )} batches" )
def lowerCAmelCase (self : int ):
__a , __a , __a : Optional[int] = self._get_dataset(max_len=5_1_2 )
__a : Union[str, Any] = 2
__a : str = ds.make_sortish_sampler(snake_case_ , shuffle=snake_case_ )
__a : Tuple = DataLoader(snake_case_ , batch_size=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 )
__a : Tuple = DataLoader(snake_case_ , batch_size=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 , sampler=snake_case_ )
__a : Optional[int] = tokenizer.pad_token_id
def count_pad_tokens(snake_case_ : Union[str, Any] , snake_case_ : List[str]="input_ids" ):
return [batch[k].eq(snake_case_ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(snake_case_ , k='''labels''' ) ) < sum(count_pad_tokens(snake_case_ , k='''labels''' ) )
assert sum(count_pad_tokens(snake_case_ ) ) < sum(count_pad_tokens(snake_case_ ) )
assert len(snake_case_ ) == len(snake_case_ )
def lowerCAmelCase (self : int , snake_case_ : int=1_0_0_0 , snake_case_ : Optional[Any]=1_2_8 ):
if os.getenv('''USE_REAL_DATA''' , snake_case_ ):
__a : Optional[int] = '''examples/seq2seq/wmt_en_ro'''
__a : List[Any] = max_len * 2 * 6_4
if not Path(snake_case_ ).joinpath('''train.len''' ).exists():
save_len_file(snake_case_ , snake_case_ )
else:
__a : int = '''examples/seq2seq/test_data/wmt_en_ro'''
__a : List[str] = max_len * 4
save_len_file(snake_case_ , snake_case_ )
__a : str = AutoTokenizer.from_pretrained(snake_case_ )
__a : Optional[int] = SeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=snake_case_ , max_target_length=snake_case_ , n_obs=snake_case_ , )
return ds, max_tokens, tokenizer
def lowerCAmelCase (self : List[str] ):
__a , __a , __a : str = self._get_dataset()
__a : Optional[Any] = set(DistributedSortishSampler(snake_case_ , 2_5_6 , num_replicas=2 , rank=0 , add_extra_examples=snake_case_ ) )
__a : Tuple = set(DistributedSortishSampler(snake_case_ , 2_5_6 , num_replicas=2 , rank=1 , add_extra_examples=snake_case_ ) )
assert idsa.intersection(snake_case_ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def lowerCAmelCase (self : str , snake_case_ : Union[str, Any] ):
__a : Union[str, Any] = AutoTokenizer.from_pretrained(snake_case_ , use_fast=snake_case_ )
if tok_name == MBART_TINY:
__a : Any = SeqaSeqDataset(
snake_case_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
__a : Tuple = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__a : Optional[Any] = SeqaSeqDataset(
snake_case_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
__a : List[Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(snake_case_ ) == 1 if tok_name == BART_TINY else len(snake_case_ ) == 0
| 90 |
from math import pi, sqrt
def __UpperCamelCase ( lowerCAmelCase__ : float ):
if num <= 0:
raise ValueError('''math domain error''' )
if num > 1_71.5:
raise OverflowError('''math range error''' )
elif num - int(lowerCAmelCase__ ) not in (0, 0.5):
raise NotImplementedError('''num must be an integer or a half-integer''' )
elif num == 0.5:
return sqrt(lowerCAmelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def __UpperCamelCase ( ):
assert gamma(0.5 ) == sqrt(lowerCAmelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
lowercase__ =1.0
while num:
lowercase__ =float(input('Gamma of: '))
print(F"""gamma({num}) = {gamma(num)}""")
print('\nEnter 0 to exit...')
| 90 | 1 |
'''simple docstring'''
def a_ ( _lowerCAmelCase ) -> list:
if n_term == "":
return []
__lowerCamelCase : list = []
for temp in range(int(_lowerCAmelCase ) ):
series.append(F'1/{temp + 1}' if series else '1' )
return series
if __name__ == "__main__":
_UpperCamelCase = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
| 208 |
'''simple docstring'''
def a_ ( _lowerCAmelCase ) -> str:
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError('Empty string was passed to the function' )
__lowerCamelCase : int = ''
while len(_lowerCAmelCase ) % 3 != 0:
__lowerCamelCase : str = '0' + bin_string
__lowerCamelCase : Union[str, Any] = [
bin_string[index : index + 3]
for index in range(len(_lowerCAmelCase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
__lowerCamelCase : Tuple = 0
for index, val in enumerate(_lowerCAmelCase ):
oct_val += int(2 ** (2 - index) * int(_lowerCAmelCase ) )
oct_string += str(_lowerCAmelCase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 208 | 1 |
def UpperCamelCase ( __magic_name__ : list ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
lowercase__ = grid[0]
for row_n in range(1 , len(__magic_name__ ) ):
lowercase__ = grid[row_n]
lowercase__ = fill_row(__magic_name__ , __magic_name__ )
lowercase__ = grid[row_n]
return grid[-1][-1]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(__magic_name__ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 146 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
A : List[Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : List[Any] , *_UpperCAmelCase : int , **_UpperCAmelCase : List[str] ) -> None:
"""simple docstring"""
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 146 | 1 |
import math
def snake_case_ ( lowerCAmelCase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case_ ( lowerCAmelCase_ : int = 10001 ):
try:
__lowercase : Optional[int] = int(lowerCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
__lowercase : list[int] = []
__lowercase : str = 2
while len(lowerCAmelCase_ ) < nth:
if is_prime(lowerCAmelCase_ ):
primes.append(lowerCAmelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''') | 233 |
def snake_case_ ( lowerCAmelCase_ : list ):
if len(lowerCAmelCase_ ) <= 1:
return [tuple(lowerCAmelCase_ )]
__lowercase : Any = []
def generate(lowerCAmelCase_ : int , lowerCAmelCase_ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCAmelCase_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__lowercase , __lowercase : List[str] = arr[k - 1], arr[i]
else: # k is odd
__lowercase , __lowercase : Any = arr[k - 1], arr[0]
generate(k - 1 , lowerCAmelCase_ )
generate(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
return res
if __name__ == "__main__":
lowerCamelCase : Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip()
lowerCamelCase : Optional[Any] = [int(item) for item in user_input.split(''',''')]
print(heaps(arr)) | 233 | 1 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
__SCREAMING_SNAKE_CASE :Tuple = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__SCREAMING_SNAKE_CASE :Tuple = 1
if upper_limit > 0:
__SCREAMING_SNAKE_CASE :Optional[Any] = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(a_ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
lowerCamelCase_ = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(f'The Catalan numbers from 0 through {N} are:')
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod() | 239 |
"""simple docstring"""
import math
import unittest
def __lowerCamelCase ( a_ : int ) -> bool:
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(a_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) ,'''Zero doesn\'t have any positive factors, primes must have exactly two.''' ,)
self.assertFalse(
is_prime(1 ) ,'''One only has 1 positive factor, primes must have exactly two.''' ,)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main() | 239 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def lowercase ( A_="" )-> str:
'''simple docstring'''
a : Tuple = tempfile.mkdtemp()
return os.path.join(A_ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Optional[Any]):
a : Union[str, Any] = torch.rand(12 , dtype=torch.floataa) - 0.5
a : Any = AgentAudio(__UpperCAmelCase)
a : List[str] = str(agent_type.to_string())
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(__UpperCAmelCase , agent_type.to_raw() , atol=1e-4))
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(__UpperCAmelCase))
# Ensure that the file contains the same value as the original tensor
a , a : Union[str, Any] = sf.read(__UpperCAmelCase)
self.assertTrue(torch.allclose(__UpperCAmelCase , torch.tensor(__UpperCAmelCase) , atol=1e-4))
def __snake_case ( self : Optional[Any]):
a : str = torch.rand(12 , dtype=torch.floataa) - 0.5
a : int = get_new_path(suffix=".wav")
sf.write(__UpperCAmelCase , __UpperCAmelCase , 16000)
a : List[str] = AgentAudio(__UpperCAmelCase)
self.assertTrue(torch.allclose(__UpperCAmelCase , agent_type.to_raw() , atol=1e-4))
self.assertEqual(agent_type.to_string() , __UpperCAmelCase)
@require_vision
@require_torch
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : List[str]):
a : Dict = torch.randint(0 , 256 , (64, 64, 3))
a : str = AgentImage(__UpperCAmelCase)
a : str = str(agent_type.to_string())
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(__UpperCAmelCase , agent_type._tensor , atol=1e-4))
self.assertIsInstance(agent_type.to_raw() , Image.Image)
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__UpperCAmelCase))
def __snake_case ( self : int):
a : Union[str, Any] = Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
a : int = Image.open(__UpperCAmelCase)
a : Any = AgentImage(__UpperCAmelCase)
self.assertTrue(path.samefile(agent_type.to_string()))
self.assertTrue(image == agent_type.to_raw())
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__UpperCAmelCase))
def __snake_case ( self : str):
a : int = Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
a : str = Image.open(__UpperCAmelCase)
a : Dict = AgentImage(__UpperCAmelCase)
self.assertFalse(path.samefile(agent_type.to_string()))
self.assertTrue(image == agent_type.to_raw())
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__UpperCAmelCase))
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Union[str, Any]):
a : Dict = "Hey!"
a : Tuple = AgentText(__UpperCAmelCase)
self.assertEqual(__UpperCAmelCase , agent_type.to_string())
self.assertEqual(__UpperCAmelCase , agent_type.to_raw())
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase)
| 40 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case , snake_case , snake_case )
order.append(snake_case )
return order
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case , snake_case , snake_case )
return component
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = len(snake_case ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case ):
if not was_visited:
order += topology_sort(snake_case , snake_case , snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case ) * [False]
for i in range(len(snake_case ) ):
_lowerCAmelCase = order[len(snake_case ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case , snake_case , snake_case )
components_list.append(snake_case )
return components_list
| 82 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = KandinskyImgaImgPipeline
_lowerCAmelCase = ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
_lowerCAmelCase = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
]
_lowerCAmelCase = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_lowerCAmelCase = False
@property
def _a ( self : List[str] ):
"""simple docstring"""
return 32
@property
def _a ( self : List[str] ):
"""simple docstring"""
return 32
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self : Tuple ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
return 100
@property
def _a ( self : int ):
"""simple docstring"""
A_ : Dict = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Tuple = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
A_ : int = MultilingualCLIP(_lowerCamelCase )
A_ : Dict = text_encoder.eval()
return text_encoder
@property
def _a ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Optional[Any] = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
A_ : List[Any] = UNetaDConditionModel(**_lowerCamelCase )
return model
@property
def _a ( self : List[str] ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : str ):
"""simple docstring"""
A_ : Tuple = self.dummy_text_encoder
A_ : Optional[int] = self.dummy_tokenizer
A_ : Dict = self.dummy_unet
A_ : Dict = self.dummy_movq
A_ : Any = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_00_85,
'''beta_end''': 0.0_12,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
A_ : int = DDIMScheduler(**_lowerCamelCase )
A_ : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _a ( self : Optional[int] , _lowerCamelCase : Dict , _lowerCamelCase : List[str]=0 ):
"""simple docstring"""
A_ : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
A_ : Dict = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_lowerCamelCase )
# create init_image
A_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
A_ : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A_ : Optional[Any] = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
if str(_lowerCamelCase ).startswith('''mps''' ):
A_ : Optional[int] = torch.manual_seed(_lowerCamelCase )
else:
A_ : Tuple = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
A_ : Optional[Any] = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def _a ( self : Any ):
"""simple docstring"""
A_ : Any = '''cpu'''
A_ : Optional[Any] = self.get_dummy_components()
A_ : Optional[int] = self.pipeline_class(**_lowerCamelCase )
A_ : Dict = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
A_ : Optional[int] = pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
A_ : Optional[Any] = output.images
A_ : Dict = pipe(
**self.get_dummy_inputs(_lowerCamelCase ) , return_dict=_lowerCamelCase , )[0]
A_ : List[str] = image[0, -3:, -3:, -1]
A_ : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : Any = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
A_ : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
A_ : Union[str, Any] = '''A red cartoon frog, 4k'''
A_ : List[str] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_lowerCamelCase )
A_ : Tuple = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
A_ : Any = pipeline.to(_lowerCamelCase )
pipeline.set_progress_bar_config(disable=_lowerCamelCase )
A_ : List[str] = torch.Generator(device='''cpu''' ).manual_seed(0 )
A_ : Optional[int] = pipe_prior(
_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
A_ : Optional[Any] = pipeline(
_lowerCamelCase , image=_lowerCamelCase , image_embeds=_lowerCamelCase , negative_image_embeds=_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
A_ : Union[str, Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
'''simple docstring'''
import os
from distutils.util import strtobool
def a ( __a , __a ) -> Union[str, Any]:
'''simple docstring'''
for e in env_keys:
UpperCamelCase__ :Optional[int] = int(os.environ.get(__a , -1 ) )
if val >= 0:
return val
return default
def a ( __a , __a=False ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = os.environ.get(__a , str(__a ) )
return strtobool(__a ) == 1 # As its name indicates `strtobool` actually returns an int...
def a ( __a , __a="no" ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = os.environ.get(__a , str(__a ) )
return value | 97 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__snake_case : Any =2_0_4_8
__snake_case : Union[str, Any] =4_0_9_6
__snake_case : Optional[Any] =4_2
__snake_case : Dict =os.environ.pop('PROCESS_TRAIN', 'false')
__snake_case : List[str] ={'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any]):
'''simple docstring'''
def choose_first(lowerCamelCase_ : List[str] ,lowerCamelCase_ : Any=False):
assert isinstance(lowerCamelCase_ ,lowerCamelCase_)
if len(lowerCamelCase_) == 1:
lowerCAmelCase__ : Optional[int] = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
lowerCAmelCase__ : Any = {k: [a[k]] for k in a}
if len(a['''start_token''']) > 0:
break
return a
lowerCAmelCase__ : Optional[Any] = {'''id''': example['''id''']}
lowerCAmelCase__ : int = example['''annotations''']
lowerCAmelCase__ : str = annotation['''yes_no_answer''']
if 0 in yes_no_answer or 1 in yes_no_answer:
lowerCAmelCase__ : Union[str, Any] = ['''yes'''] if 1 in yes_no_answer else ['''no''']
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : int = ['''<cls>''']
else:
lowerCAmelCase__ : Tuple = ['''short''']
lowerCAmelCase__ : int = choose_first(annotation['''short_answers'''])
if len(out['''start_token''']) == 0:
# answer will be long if short is not available
lowerCAmelCase__ : Optional[Any] = ['''long''']
lowerCAmelCase__ : str = choose_first(annotation['''long_answer'''] ,is_long_answer=lowerCamelCase_)
lowerCAmelCase__ : Optional[int] = []
answer.update(lowerCamelCase_)
# disregard some samples
if len(answer['''start_token''']) > 1 or answer["start_token"] == answer["end_token"]:
lowerCAmelCase__ : Optional[Any] = True
else:
lowerCAmelCase__ : Union[str, Any] = False
lowerCAmelCase__ : Tuple = ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
if not all(isinstance(answer[k] ,lowerCamelCase_) for k in cols):
raise ValueError('''Issue in ID''' ,example['''id'''])
return answer
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any] ,lowerCamelCase_ : Union[str, Any]=False):
'''simple docstring'''
lowerCAmelCase__ : Any = _get_single_answer(lowerCamelCase_)
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
lowerCAmelCase__ : List[Any] = example['''document''']['''tokens''']
lowerCAmelCase__ : Any = []
for i in range(len(doc['''token'''])):
if not doc["is_html"][i]:
context.append(doc['''token'''][i])
return {
"context": " ".join(lowerCamelCase_),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
lowerCAmelCase__ : Union[str, Any] = ['''start_token''', '''end_token''']
answer.update({k: answer[k][0] if len(answer[k]) > 0 else answer[k] for k in cols}) # e.g. [10] == 10
lowerCAmelCase__ : List[Any] = example['''document''']['''tokens''']
lowerCAmelCase__ : Optional[Any] = answer['''start_token''']
lowerCAmelCase__ : Union[str, Any] = answer['''end_token''']
lowerCAmelCase__ : int = []
for i in range(len(doc['''token'''])):
if not doc["is_html"][i]:
context.append(doc['''token'''][i])
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
lowerCAmelCase__ : List[Any] = ''' '''.join(context[start_token:end_token])
# checking above code
if assertion:
lowerCAmelCase__ : str = doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
lowerCAmelCase__ : List[Any] = doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
lowerCAmelCase__ : Optional[int] = ''' '''.join([old[i] for i in range(len(lowerCamelCase_)) if not is_html[i]])
if new != old:
print('''ID:''' ,example['''id'''])
print('''New:''' ,lowerCamelCase_ ,end='''\n''')
print('''Old:''' ,lowerCamelCase_ ,end='''\n\n''')
return {
"context": " ".join(lowerCamelCase_),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : str ,lowerCamelCase_ : Tuple=2048 ,lowerCamelCase_ : Dict=4096 ,lowerCamelCase_ : Optional[Any]=True):
'''simple docstring'''
lowerCAmelCase__ : int = get_context_and_ans(lowerCamelCase_ ,assertion=lowerCamelCase_)
lowerCAmelCase__ : Union[str, Any] = out['''answer''']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
lowerCAmelCase__ : Union[str, Any] = tokenizer(example['''question''']['''text'''] ,out['''context''']).input_ids
lowerCAmelCase__ : List[str] = input_ids.index(tokenizer.sep_token_id) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : List[Any] = input_ids[:q_len]
lowerCAmelCase__ : List[Any] = range(lowerCamelCase_ ,len(lowerCamelCase_) ,max_length - doc_stride)
for i in doc_start_indices:
lowerCAmelCase__ : Union[str, Any] = i + max_length - q_len
lowerCAmelCase__ : Any = input_ids[i:end_index]
inputs.append(q_indices + slice)
category.append(answer['''category'''][0])
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase_),
"end_token": [-100] * len(lowerCamelCase_),
"category": category,
},
}
lowerCAmelCase__ : Optional[Any] = out['''context'''].split()
lowerCAmelCase__ : Union[str, Any] = splitted_context[answer['''end_token''']]
lowerCAmelCase__ : Optional[int] = len(
tokenizer(
''' '''.join(splitted_context[: answer['''start_token''']]) ,add_special_tokens=lowerCamelCase_ ,).input_ids)
lowerCAmelCase__ : Dict = len(
tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]) ,add_special_tokens=lowerCamelCase_).input_ids)
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
lowerCAmelCase__ : int = len(tokenizer(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_).input_ids)
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
lowerCAmelCase__ : Union[str, Any] = input_ids[answer['''start_token'''] : answer['''end_token'''] + 1] # right & left are inclusive
lowerCAmelCase__ : List[str] = answer['''start_token''']
lowerCAmelCase__ : Union[str, Any] = answer['''end_token''']
if assertion:
lowerCAmelCase__ : int = tokenizer.decode(lowerCamelCase_)
if answer["span"] != new:
print('''ISSUE IN TOKENIZATION''')
print('''OLD:''' ,answer['''span'''])
print('''NEW:''' ,lowerCamelCase_ ,end='''\n\n''')
if len(lowerCamelCase_) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
lowerCAmelCase__ : int = input_ids[:q_len]
lowerCAmelCase__ : Optional[Any] = range(lowerCamelCase_ ,len(lowerCamelCase_) ,max_length - doc_stride)
lowerCAmelCase__ : Tuple = []
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : Any = [] # null, yes, no, long, short
for i in doc_start_indices:
lowerCAmelCase__ : str = i + max_length - q_len
lowerCAmelCase__ : List[str] = input_ids[i:end_index]
inputs.append(q_indices + slice)
assert len(inputs[-1]) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
lowerCAmelCase__ : int = start_token - i + q_len
lowerCAmelCase__ : str = end_token - i + q_len
answers_category.append(answer['''category'''][0]) # ["short"] -> "short"
else:
lowerCAmelCase__ : Tuple = -100
lowerCAmelCase__ : List[str] = -100
answers_category.append('''null''')
lowerCAmelCase__ : int = inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase_)
answers_end_token.append(lowerCamelCase_)
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('''ISSUE in strided for ID:''' ,example['''id'''])
print('''New:''' ,tokenizer.decode(lowerCamelCase_))
print('''Old:''' ,tokenizer.decode(lowerCamelCase_) ,end='''\n\n''')
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase__ ( lowerCamelCase_ : Any ,lowerCamelCase_ : List[Any] ,lowerCamelCase_ : int=2048 ,lowerCamelCase_ : Tuple=4096 ,lowerCamelCase_ : Optional[int]=False):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = get_strided_contexts_and_ans(
lowerCamelCase_ ,lowerCamelCase_ ,doc_stride=lowerCamelCase_ ,max_length=lowerCamelCase_ ,assertion=lowerCamelCase_ ,)
return example
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : int):
'''simple docstring'''
with jsonlines.open(lowerCamelCase_ ,'''a''') as writer:
for example in tqdm(lowerCamelCase_ ,total=len(lowerCamelCase_) ,desc='''Saving samples ... '''):
lowerCAmelCase__ : Optional[Any] = example['''labels''']
for ids, start, end, cat in zip(
example['''input_ids'''] ,labels['''start_token'''] ,labels['''end_token'''] ,labels['''category'''] ,):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'''input_ids''': ids,
'''start_token''': start,
'''end_token''': end,
'''category''': CATEGORY_MAPPING[cat],
})
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__snake_case : Optional[int] =load_dataset('natural_questions')
__snake_case : Union[str, Any] =BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
__snake_case : Tuple =data['train' if PROCESS_TRAIN == 'true' else 'validation']
__snake_case : Optional[int] ={
'tokenizer': tokenizer,
'doc_stride': DOC_STRIDE,
'max_length': MAX_LENGTH,
'assertion': False,
}
__snake_case : Dict =data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__snake_case : Dict =data.remove_columns(['annotations', 'document', 'id', 'question'])
print(data)
np.random.seed(SEED)
__snake_case : int ='nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
save_to_disk(data, file_name=cache_file_name)
| 129 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Tuple = HfArgumentParser(A_ )
_lowerCamelCase : str = parser.parse_args_into_dataclasses()[0]
_lowerCamelCase : int = TensorFlowBenchmark(args=A_ )
try:
_lowerCamelCase : int = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
_lowerCamelCase : Tuple = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
_lowerCamelCase : int = ''' '''.join(str(A_ ).split(''' ''' )[:-1] )
_lowerCamelCase : Dict = ''''''
_lowerCamelCase : str = eval(str(A_ ).split(''' ''' )[-1] )
_lowerCamelCase : Dict = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(A_ )
if len(A_ ) > 0:
_lowerCamelCase : str = full_error_msg + begin_error_msg + str(A_ )
raise ValueError(A_ )
benchmark.run()
if __name__ == "__main__":
main()
| 175 |
"""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
lowerCAmelCase__ = logging.getLogger(__name__)
class __snake_case ( _lowercase):
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int=None ):
"""simple docstring"""
super().__init__(
__lowerCAmelCase , question_encoder_tokenizer=__lowerCAmelCase , generator_tokenizer=__lowerCAmelCase , index=__lowerCAmelCase , init_retrieval=__lowerCAmelCase , )
_lowerCamelCase : Dict = None
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : int ):
"""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
_lowerCamelCase : List[str] = self._infer_socket_ifname()
# avoid clash with the NCCL port
_lowerCamelCase : Dict = str(distributed_port + 1 )
_lowerCamelCase : str = dist.new_group(ranks=__lowerCAmelCase , 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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple=torch.floataa ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = torch.empty(__lowerCAmelCase , dtype=__lowerCAmelCase )
dist.scatter(__lowerCAmelCase , src=0 , scatter_list=__lowerCAmelCase , group=self.process_group )
return target_tensor
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : List[str] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
_lowerCamelCase : str = next((addr for addr in addrs if addr.startswith('''e''' )) , __lowerCAmelCase )
return ifname
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
if not dist.is_initialized():
_lowerCamelCase , _lowerCamelCase : Any = self._main_retrieve(__lowerCAmelCase , __lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase )
# distributed training
_lowerCamelCase : Dict = dist.get_world_size(group=self.process_group )
# gather logic
_lowerCamelCase : str = None
if self._is_main():
_lowerCamelCase : List[Any] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(__lowerCAmelCase )]
dist.gather(torch.tensor(__lowerCAmelCase ) , dst=0 , gather_list=__lowerCAmelCase , group=self.process_group )
# scatter logic
_lowerCamelCase : int = question_hidden_states.shape[0]
_lowerCamelCase : str = []
_lowerCamelCase : Optional[int] = []
if self._is_main():
assert len(__lowerCAmelCase ) == world_size
_lowerCamelCase , _lowerCamelCase : Tuple = self._main_retrieve(torch.cat(__lowerCAmelCase ).numpy() , __lowerCAmelCase )
_lowerCamelCase , _lowerCamelCase : List[str] = torch.tensor(__lowerCAmelCase ), torch.tensor(__lowerCAmelCase )
_lowerCamelCase : List[Any] = self._chunk_tensor(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self._chunk_tensor(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : int = self._scattered(__lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
_lowerCamelCase : str = self._scattered(__lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase )
| 175 | 1 |
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