code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ = 100 ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = (n * (n + 1) // 2) ** 2
UpperCAmelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'{solution() = }')
| 130 | 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
_lowercase: int = logging.get_logger(__name__)
_lowercase: Union[str, Any] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
_lowercase: Dict = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
}
}
_lowercase: List[Any] = {
'''camembert-base''': 5_1_2,
}
_lowercase: Dict = '''▁'''
class lowerCamelCase__ ( UpperCAmelCase ):
UpperCamelCase__ =VOCAB_FILES_NAMES
UpperCamelCase__ =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ =["input_ids", "attention_mask"]
def __init__( self : str , lowercase__ : int , lowercase__ : Tuple="<s>" , lowercase__ : Optional[int]="</s>" , lowercase__ : Optional[Any]="</s>" , lowercase__ : Any="<s>" , lowercase__ : Union[str, Any]="<unk>" , lowercase__ : Union[str, Any]="<pad>" , lowercase__ : Optional[int]="<mask>" , lowercase__ : str=["<s>NOTUSED", "</s>NOTUSED"] , lowercase__ : Optional[Dict[str, Any]] = None , **lowercase__ : Union[str, Any] , ):
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else mask_token
_lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowercase__ , eos_token=lowercase__ , unk_token=lowercase__ , sep_token=lowercase__ , cls_token=lowercase__ , pad_token=lowercase__ , mask_token=lowercase__ , additional_special_tokens=lowercase__ , sp_model_kwargs=self.sp_model_kwargs , **lowercase__ , )
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowercase__ ) )
_lowerCAmelCase = 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>
_lowerCAmelCase = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
_lowerCAmelCase = len(self.fairseq_tokens_to_ids )
_lowerCAmelCase = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
_lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
_lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self : Any , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None , lowercase__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase__ , token_ids_a=lowercase__ , already_has_special_tokens=lowercase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase__ )) + [1]
return [1] + ([0] * len(lowercase__ )) + [1, 1] + ([0] * len(lowercase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self : int , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None ):
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
_lowerCAmelCase = {self.convert_ids_to_tokens(lowercase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , lowercase__ : str ):
return self.sp_model.encode(lowercase__ , out_type=lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , lowercase__ : Optional[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(lowercase__ ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , lowercase__ : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self : int , lowercase__ : Optional[int] ):
_lowerCAmelCase = []
_lowerCAmelCase = ''
_lowerCAmelCase = 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(lowercase__ ) + token
_lowerCAmelCase = True
_lowerCAmelCase = []
else:
current_sub_tokens.append(lowercase__ )
_lowerCAmelCase = False
out_string += self.sp_model.decode(lowercase__ )
return out_string.strip()
def __getstate__( self : Any ):
_lowerCAmelCase = self.__dict__.copy()
_lowerCAmelCase = None
return state
def __setstate__( self : Optional[Any] , lowercase__ : Union[str, Any] ):
_lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_lowerCAmelCase = {}
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , lowercase__ : str , lowercase__ : Optional[str] = None ):
if not os.path.isdir(lowercase__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
lowercase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase__ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase__ , 'wb' ) as fi:
_lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(lowercase__ )
return (out_vocab_file,)
| 192 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : str = "▁"
_lowerCAmelCase : str = {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}
_lowerCAmelCase : List[str] = {
"sentencepiece_model_file": "sentencepiece.bpe.model",
"vocab_file": "vocab.txt",
}
_lowerCAmelCase : Optional[Any] = {
"vocab_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
},
"sentencepiece_model_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
},
}
_lowerCAmelCase : Dict = {
"ernie-m-base": 5_1_4,
"ernie-m-large": 5_1_4,
}
_lowerCAmelCase : Dict = {
"ernie-m-base": {"do_lower_case": False},
"ernie-m-large": {"do_lower_case": False},
}
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = ["input_ids"]
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = RESOURCE_FILES_NAMES
def __init__( self ,a_ ,a_=None ,a_=False ,a_="utf8" ,a_="[UNK]" ,a_="[SEP]" ,a_="[PAD]" ,a_="[CLS]" ,a_="[MASK]" ,a_ = None ,**a_ ,):
"""simple docstring"""
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowerCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=a_ ,unk_token=a_ ,sep_token=a_ ,pad_token=a_ ,cls_token=a_ ,mask_token=a_ ,vocab_file=a_ ,encoding=a_ ,sp_model_kwargs=self.sp_model_kwargs ,**a_ ,)
lowerCAmelCase__ = do_lower_case
lowerCAmelCase__ = sentencepiece_model_ckpt
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a_ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowerCAmelCase__ = self.load_vocab(filepath=a_ )
else:
lowerCAmelCase__ = {self.sp_model.id_to_piece(a_ ): id for id in range(self.sp_model.get_piece_size() )}
lowerCAmelCase__ = {v: k for k, v in self.vocab.items()}
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if text is None:
return None
lowerCAmelCase__ = self.tokenize(a_ )
lowerCAmelCase__ , lowerCAmelCase__ = '', []
for i, ch in enumerate(a_ ):
if ch in self.SP_CHAR_MAPPING:
lowerCAmelCase__ = self.SP_CHAR_MAPPING.get(a_ )
else:
lowerCAmelCase__ = unicodedata.normalize('NFKC' ,a_ )
if self.is_whitespace(a_ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(a_ ) )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = normalized_text, [], 0
if self.do_lower_case:
lowerCAmelCase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowerCAmelCase__ = token[1:]
lowerCAmelCase__ = text[offset:].index(a_ ) + offset
lowerCAmelCase__ = start + len(a_ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowerCAmelCase__ = end
return token_mapping
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return len(self.vocab )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return dict(self.vocab ,**self.added_tokens_encoder )
def __getstate__( self ):
"""simple docstring"""
lowerCAmelCase__ = self.__dict__.copy()
lowerCAmelCase__ = None
return state
def __setstate__( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
lowerCAmelCase__ = {}
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
return "".join((self.SP_CHAR_MAPPING.get(a_ ,a_ ) for c in text) )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=False ,a_=64 ,a_=0.1 ):
"""simple docstring"""
if self.sp_model_kwargs.get('enable_sampling' ) is True:
lowerCAmelCase__ = True
if self.sp_model_kwargs.get('alpha' ) is not None:
lowerCAmelCase__ = self.sp_model_kwargs.get('alpha' )
if self.sp_model_kwargs.get('nbest_size' ) is not None:
lowerCAmelCase__ = self.sp_model_kwargs.get('nbest_size' )
if not enable_sampling:
lowerCAmelCase__ = self.sp_model.EncodeAsPieces(a_ )
else:
lowerCAmelCase__ = self.sp_model.SampleEncodeAsPieces(a_ ,a_ ,a_ )
lowerCAmelCase__ = []
for pi, piece in enumerate(a_ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(a_ ) and pi != 0:
new_pieces.append(a_ )
continue
else:
continue
lowerCAmelCase__ = 0
for i, chunk in enumerate(a_ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(a_ ) or self.is_punct(a_ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(a_ )
lowerCAmelCase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCAmelCase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCAmelCase__ = i
if len(a_ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = ''.join(a_ ).replace(a_ ,' ' ).strip()
return out_string
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = self.convert_ids_to_tokens(a_ )
lowerCAmelCase__ = ''.join(a_ ).replace(a_ ,' ' ).strip()
return out_string
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
return self.vocab.get(a_ ,self.vocab.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
return self.reverse_vocab.get(a_ ,self.unk_token )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
lowerCAmelCase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=None ):
"""simple docstring"""
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=None ,a_=False ):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(a_ )) + [1, 1] + ([0] * len(a_ )) + [1]
return [1] + ([0] * len(a_ )) + [1]
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ = None ):
"""simple docstring"""
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_a is None:
# [CLS] X [SEP]
return (len(a_ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(a_ ) + 1) + [1] * (len(a_ ) + 3)
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if "\u4e00" <= char <= "\u9fff":
return True
return False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(a_ ) == 1:
lowerCAmelCase__ = unicodedata.category(a_ )
if cat == "Zs":
return True
return False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = {}
with io.open(a_ ,'r' ,encoding='utf-8' ) as f:
for index, line in enumerate(a_ ):
lowerCAmelCase__ = line.rstrip('\n' )
lowerCAmelCase__ = int(a_ )
return token_to_idx
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ = None ):
"""simple docstring"""
lowerCAmelCase__ = 0
if os.path.isdir(a_ ):
lowerCAmelCase__ = os.path.join(
a_ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
else:
lowerCAmelCase__ = (filename_prefix + '-' if filename_prefix else '') + save_directory
with open(a_ ,'w' ,encoding='utf-8' ) as writer:
for token, token_index in sorted(self.vocab.items() ,key=lambda a_ : kv[1] ):
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!' )
lowerCAmelCase__ = token_index
writer.write(token + '\n' )
index += 1
lowerCAmelCase__ = os.path.join(a_ ,'sentencepiece.bpe.model' )
with open(a_ ,'wb' ) as fi:
lowerCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(a_ )
return (vocab_file,)
| 604 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowerCAmelCase : int = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
_lowerCAmelCase : str = [
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def UpperCAmelCase_ ( ) -> str:
"""simple docstring"""
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=['rouge2', 'rougeL'] )
assert isinstance(snake_case__ , snake_case__ )
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=['rouge2'] )
assert (
pd.DataFrame(no_aggregation['rouge2'] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['rouge2'] ).fmeasure.mean()
)
def UpperCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ = 'rougeLsum'
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k] )[k]
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k] )[k]
assert score > score_no_sep
def UpperCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = ['rouge1', 'rouge2', 'rougeL']
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__ )
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__ )
assert score_sep == score_no_sep
def UpperCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = [
'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
]
lowerCAmelCase__ = [
'Margot Frank, died in 1945, a month earlier than previously thought.',
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
' the final seconds on board Flight 9525.',
]
assert calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ ) == calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ )
def UpperCAmelCase_ ( ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ = [
'" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '
]
lowerCAmelCase__ = [
' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
]
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=['rougeLsum'] , newline_sep=snake_case__ )['rougeLsum']
lowerCAmelCase__ = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=['rougeLsum'] )['rougeLsum']
assert new_score > prev_score
def UpperCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = Path('examples/seq2seq/test_data/wmt_en_ro' )
lowerCAmelCase__ = calculate_rouge_path(data_dir.joinpath('test.source' ) , data_dir.joinpath('test.target' ) )
assert isinstance(snake_case__ , snake_case__ )
lowerCAmelCase__ = calculate_rouge_path(
data_dir.joinpath('test.source' ) , data_dir.joinpath('test.target' ) , bootstrap_aggregation=snake_case__ )
assert isinstance(snake_case__ , snake_case__ )
| 604 | 1 |
"""simple docstring"""
def __a ( a, a ):
"""simple docstring"""
if not (isinstance(a_, a_ ) and isinstance(a_, a_ )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
_a = len(a_ )
_a = len(a_ )
_a = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
_a = 0
_a = 0
for i in range(1, texta_length + 1 ):
for j in range(1, texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
_a = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
_a = i
_a = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 388 | '''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase :Optional[int] = logging.getLogger(__name__)
class UpperCAmelCase ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Any = "masked_bert"
def __init__( self , lowercase__=30_522 , lowercase__=768 , lowercase__=12 , lowercase__=12 , lowercase__=3_072 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=512 , lowercase__=2 , lowercase__=0.0_2 , lowercase__=1E-12 , lowercase__=0 , lowercase__="topK" , lowercase__="constant" , lowercase__=0.0 , **lowercase__ , ) -> Optional[int]:
super().__init__(pad_token_id=lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE : int = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Any = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = pruning_method
SCREAMING_SNAKE_CASE : Any = mask_init
SCREAMING_SNAKE_CASE : List[str] = mask_scale
| 251 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMForMultipleChoice',
'XLMForQuestionAnswering',
'XLMForQuestionAnsweringSimple',
'XLMForSequenceClassification',
'XLMForTokenClassification',
'XLMModel',
'XLMPreTrainedModel',
'XLMWithLMHeadModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMForMultipleChoice',
'TFXLMForQuestionAnsweringSimple',
'TFXLMForSequenceClassification',
'TFXLMForTokenClassification',
'TFXLMMainLayer',
'TFXLMModel',
'TFXLMPreTrainedModel',
'TFXLMWithLMHeadModel',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 706 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class snake_case_ ( __lowercase ,__lowercase ,__lowercase ,unittest.TestCase ):
A_ = StableDiffusionControlNetImgaImgPipeline
A_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
A_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
A_ = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
A_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase__ ( self : Dict )->str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
torch.manual_seed(0 )
__lowerCAmelCase : Any = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
__lowerCAmelCase : List[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
__lowerCAmelCase : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCAmelCase : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCAmelCase : List[Any] = CLIPTextModel(_snake_case )
__lowerCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase : Tuple = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCAmelCase__ ( self : int , _snake_case : str , _snake_case : int=0 )->str:
'''simple docstring'''
if str(_snake_case ).startswith("""mps""" ):
__lowerCAmelCase : int = torch.manual_seed(_snake_case )
else:
__lowerCAmelCase : Optional[int] = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__lowerCAmelCase : List[str] = 2
__lowerCAmelCase : Any = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , )
__lowerCAmelCase : Any = floats_tensor(control_image.shape , rng=random.Random(_snake_case ) ).to(_snake_case )
__lowerCAmelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase : Tuple = Image.fromarray(np.uinta(_snake_case ) ).convert("""RGB""" ).resize((64, 64) )
__lowerCAmelCase : List[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCAmelCase__ ( self : Any )->Tuple:
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCAmelCase__ ( self : Dict )->int:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCAmelCase__ ( self : Optional[int] )->List[Any]:
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class snake_case_ ( __lowercase ,__lowercase ,unittest.TestCase ):
A_ = StableDiffusionControlNetImgaImgPipeline
A_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
A_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
A_ = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def UpperCAmelCase__ ( self : Tuple )->Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(_snake_case : Optional[Any] ):
if isinstance(_snake_case , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
__lowerCAmelCase : Dict = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_snake_case )
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_snake_case )
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
__lowerCAmelCase : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCAmelCase : Optional[Any] = CLIPTextModel(_snake_case )
__lowerCAmelCase : Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase : List[Any] = MultiControlNetModel([controlneta, controlneta] )
__lowerCAmelCase : List[str] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCAmelCase__ ( self : List[Any] , _snake_case : Dict , _snake_case : List[Any]=0 )->int:
'''simple docstring'''
if str(_snake_case ).startswith("""mps""" ):
__lowerCAmelCase : int = torch.manual_seed(_snake_case )
else:
__lowerCAmelCase : Optional[int] = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__lowerCAmelCase : Union[str, Any] = 2
__lowerCAmelCase : Optional[int] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , ),
]
__lowerCAmelCase : int = floats_tensor(control_image[0].shape , rng=random.Random(_snake_case ) ).to(_snake_case )
__lowerCAmelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase : Optional[int] = Image.fromarray(np.uinta(_snake_case ) ).convert("""RGB""" ).resize((64, 64) )
__lowerCAmelCase : Union[str, Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCAmelCase__ ( self : Optional[int] )->str:
'''simple docstring'''
__lowerCAmelCase : int = self.get_dummy_components()
__lowerCAmelCase : Optional[int] = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
__lowerCAmelCase : Any = 10.0
__lowerCAmelCase : Tuple = 4
__lowerCAmelCase : List[Any] = self.get_dummy_inputs(_snake_case )
__lowerCAmelCase : int = steps
__lowerCAmelCase : Tuple = scale
__lowerCAmelCase : Optional[int] = pipe(**_snake_case )[0]
__lowerCAmelCase : str = self.get_dummy_inputs(_snake_case )
__lowerCAmelCase : List[Any] = steps
__lowerCAmelCase : Optional[Any] = scale
__lowerCAmelCase : Any = pipe(**_snake_case , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
__lowerCAmelCase : str = self.get_dummy_inputs(_snake_case )
__lowerCAmelCase : Optional[Any] = steps
__lowerCAmelCase : str = scale
__lowerCAmelCase : str = pipe(**_snake_case , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
__lowerCAmelCase : Tuple = self.get_dummy_inputs(_snake_case )
__lowerCAmelCase : Optional[int] = steps
__lowerCAmelCase : Union[str, Any] = scale
__lowerCAmelCase : List[Any] = pipe(**_snake_case , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def UpperCAmelCase__ ( self : Tuple )->Dict:
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCAmelCase__ ( self : Tuple )->int:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCAmelCase__ ( self : Union[str, Any] )->List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def UpperCAmelCase__ ( self : str )->Tuple:
'''simple docstring'''
__lowerCAmelCase : Dict = self.get_dummy_components()
__lowerCAmelCase : Optional[int] = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(_snake_case )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class snake_case_ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Union[str, Any] )->Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : Union[str, Any] )->Dict:
'''simple docstring'''
__lowerCAmelCase : str = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
__lowerCAmelCase : List[Any] = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=_snake_case , controlnet=_snake_case )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_snake_case )
__lowerCAmelCase : Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase : List[str] = """evil space-punk bird"""
__lowerCAmelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
__lowerCAmelCase : List[Any] = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
__lowerCAmelCase : int = pipe(
_snake_case , _snake_case , control_image=_snake_case , generator=_snake_case , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
__lowerCAmelCase : Optional[Any] = output.images[0]
assert image.shape == (512, 512, 3)
__lowerCAmelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2 | 240 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCAmelCase_ ( __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple =AutoConfig.from_pretrained(__a )
SCREAMING_SNAKE_CASE : Dict =FlaxAutoModelForSeqaSeqLM.from_config(config=__a )
SCREAMING_SNAKE_CASE : Dict =checkpoints.load_tax_checkpoint(__a )
SCREAMING_SNAKE_CASE : Union[str, Any] ='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
SCREAMING_SNAKE_CASE : Dict ='''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
SCREAMING_SNAKE_CASE : Optional[Any] ='''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
SCREAMING_SNAKE_CASE : int ='''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
SCREAMING_SNAKE_CASE : Any =f'layers_{str(__a )}'
# Self-Attention
SCREAMING_SNAKE_CASE : str =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
SCREAMING_SNAKE_CASE : int =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
SCREAMING_SNAKE_CASE : str =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
SCREAMING_SNAKE_CASE : List[str] =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
SCREAMING_SNAKE_CASE : Tuple =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
SCREAMING_SNAKE_CASE : Any =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
SCREAMING_SNAKE_CASE : Any =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
SCREAMING_SNAKE_CASE : int =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
SCREAMING_SNAKE_CASE : List[Any] =tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
SCREAMING_SNAKE_CASE : Any =flax_model.params['''encoder''']['''block'''][str(__a )]['''layer''']
SCREAMING_SNAKE_CASE : str =tax_attention_key
SCREAMING_SNAKE_CASE : List[Any] =tax_attention_out
SCREAMING_SNAKE_CASE : int =tax_attention_query
SCREAMING_SNAKE_CASE : Dict =tax_attention_value
SCREAMING_SNAKE_CASE : str =tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
SCREAMING_SNAKE_CASE : List[str] =tax_global_layer_norm
if split_mlp_wi:
SCREAMING_SNAKE_CASE : List[Any] =tax_mlp_wi_a
SCREAMING_SNAKE_CASE : Optional[Any] =tax_mlp_wi_a
else:
SCREAMING_SNAKE_CASE : Optional[int] =tax_mlp_wi
SCREAMING_SNAKE_CASE : Dict =tax_mlp_wo
SCREAMING_SNAKE_CASE : Dict =tax_mlp_layer_norm
SCREAMING_SNAKE_CASE : int =flax_model_encoder_layer_block
# Only for layer 0:
SCREAMING_SNAKE_CASE : str =tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
SCREAMING_SNAKE_CASE : Optional[int] =tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
SCREAMING_SNAKE_CASE : List[Any] =tax_encoder_global_rel_embedding
# Assigning
SCREAMING_SNAKE_CASE : str =tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
SCREAMING_SNAKE_CASE : List[str] =tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
SCREAMING_SNAKE_CASE : Any =f'layers_{str(__a )}'
# Self-Attention
SCREAMING_SNAKE_CASE : List[Any] =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
SCREAMING_SNAKE_CASE : int =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
SCREAMING_SNAKE_CASE : Any =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
SCREAMING_SNAKE_CASE : Optional[Any] =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
SCREAMING_SNAKE_CASE : int =tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
SCREAMING_SNAKE_CASE : Any =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
SCREAMING_SNAKE_CASE : Tuple =tax_enc_dec_attention_module['''key''']['''kernel''']
SCREAMING_SNAKE_CASE : Optional[int] =tax_enc_dec_attention_module['''out''']['''kernel''']
SCREAMING_SNAKE_CASE : Optional[Any] =tax_enc_dec_attention_module['''query''']['''kernel''']
SCREAMING_SNAKE_CASE : str =tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
SCREAMING_SNAKE_CASE : int =tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
SCREAMING_SNAKE_CASE : List[str] =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
SCREAMING_SNAKE_CASE : Tuple =tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
SCREAMING_SNAKE_CASE : Optional[Any] =flax_model.params['''decoder''']['''block'''][str(__a )]['''layer''']
SCREAMING_SNAKE_CASE : Dict =tax_attention_key
SCREAMING_SNAKE_CASE : Optional[Any] =tax_attention_out
SCREAMING_SNAKE_CASE : Optional[int] =tax_attention_query
SCREAMING_SNAKE_CASE : Optional[Any] =tax_attention_value
SCREAMING_SNAKE_CASE : Any =tax_pre_attention_layer_norm
SCREAMING_SNAKE_CASE : Union[str, Any] =tax_enc_dec_attention_key
SCREAMING_SNAKE_CASE : Any =tax_enc_dec_attention_out
SCREAMING_SNAKE_CASE : Union[str, Any] =tax_enc_dec_attention_query
SCREAMING_SNAKE_CASE : List[Any] =tax_enc_dec_attention_value
SCREAMING_SNAKE_CASE : Union[str, Any] =tax_cross_layer_norm
if split_mlp_wi:
SCREAMING_SNAKE_CASE : str =tax_mlp_wi_a
SCREAMING_SNAKE_CASE : str =tax_mlp_wi_a
else:
SCREAMING_SNAKE_CASE : Any =tax_mlp_wi
SCREAMING_SNAKE_CASE : Optional[Any] =tax_mlp_wo
SCREAMING_SNAKE_CASE : str =txa_mlp_layer_norm
SCREAMING_SNAKE_CASE : Optional[int] =flax_model_decoder_layer_block
# Decoder Normalization
SCREAMING_SNAKE_CASE : Optional[Any] =tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
SCREAMING_SNAKE_CASE : List[str] =txa_decoder_norm
# Only for layer 0:
SCREAMING_SNAKE_CASE : Optional[int] =tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
SCREAMING_SNAKE_CASE : List[str] =tax_decoder_rel_embedding
# Token Embeddings
SCREAMING_SNAKE_CASE : Any =tax_model['''target''']['''token_embedder''']['''embedding''']
SCREAMING_SNAKE_CASE : Optional[int] =txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
SCREAMING_SNAKE_CASE : List[Any] =tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(__a )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
)
parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
parser.add_argument(
"""--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
)
_A = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 258 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _lowerCAmelCase ( UpperCamelCase__ ):
def __init__( self , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=0 ) -> List[str]:
SCREAMING_SNAKE_CASE : Optional[int] =1.0 if scale is None else scale
SCREAMING_SNAKE_CASE : List[Any] =0.0 if loc is None else loc
super().__init__(snake_case_ , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=snake_case_ )] )
@property
def __a ( self ) -> Any:
return self.base_dist.mean * self.scale + self.loc
@property
def __a ( self ) -> str:
return self.base_dist.variance * self.scale**2
@property
def __a ( self ) -> Union[str, Any]:
return self.variance.sqrt()
class _lowerCAmelCase ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , **snake_case_ ) -> None:
super().__init__(**snake_case_ )
SCREAMING_SNAKE_CASE : List[Any] =args_dim
SCREAMING_SNAKE_CASE : Any =nn.ModuleList([nn.Linear(snake_case_ , snake_case_ ) for dim in args_dim.values()] )
SCREAMING_SNAKE_CASE : Dict =domain_map
def __a ( self , snake_case_ ) -> Tuple[torch.Tensor]:
SCREAMING_SNAKE_CASE : Dict =[proj(snake_case_ ) for proj in self.proj]
return self.domain_map(*snake_case_ )
class _lowerCAmelCase ( nn.Module ):
def __init__( self , snake_case_ ) -> List[str]:
super().__init__()
SCREAMING_SNAKE_CASE : Tuple =function
def __a ( self , snake_case_ , *snake_case_ ) -> Dict:
return self.function(snake_case_ , *snake_case_ )
class _lowerCAmelCase :
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 42
def __init__( self , snake_case_ = 1 ) -> None:
SCREAMING_SNAKE_CASE : Dict =dim
SCREAMING_SNAKE_CASE : Dict ={k: dim * self.args_dim[k] for k in self.args_dim}
def __a ( self , snake_case_ ) -> Optional[Any]:
if self.dim == 1:
return self.distribution_class(*snake_case_ )
else:
return Independent(self.distribution_class(*snake_case_ ) , 1 )
def __a ( self , snake_case_ , snake_case_ = None , snake_case_ = None , ) -> Distribution:
SCREAMING_SNAKE_CASE : Optional[int] =self._base_distribution(snake_case_ )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(snake_case_ , loc=snake_case_ , scale=snake_case_ , event_dim=self.event_dim )
@property
def __a ( self ) -> Tuple:
return () if self.dim == 1 else (self.dim,)
@property
def __a ( self ) -> int:
return len(self.event_shape )
@property
def __a ( self ) -> float:
return 0.0
def __a ( self , snake_case_ ) -> nn.Module:
return ParameterProjection(
in_features=snake_case_ , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def __a ( self , *snake_case_ ) -> int:
raise NotImplementedError()
@staticmethod
def __a ( snake_case_ ) -> torch.Tensor:
return (x + torch.sqrt(torch.square(snake_case_ ) + 4.0 )) / 2.0
class _lowerCAmelCase ( UpperCamelCase__ ):
lowerCamelCase__ = {"df": 1, "loc": 1, "scale": 1}
lowerCamelCase__ = StudentT
@classmethod
def __a ( cls , snake_case_ , snake_case_ , snake_case_ ) -> Tuple:
SCREAMING_SNAKE_CASE : Dict =cls.squareplus(snake_case_ ).clamp_min(torch.finfo(scale.dtype ).eps )
SCREAMING_SNAKE_CASE : int =2.0 + cls.squareplus(snake_case_ )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class _lowerCAmelCase ( UpperCamelCase__ ):
lowerCamelCase__ = {"loc": 1, "scale": 1}
lowerCamelCase__ = Normal
@classmethod
def __a ( cls , snake_case_ , snake_case_ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE : List[Any] =cls.squareplus(snake_case_ ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class _lowerCAmelCase ( UpperCamelCase__ ):
lowerCamelCase__ = {"total_count": 1, "logits": 1}
lowerCamelCase__ = NegativeBinomial
@classmethod
def __a ( cls , snake_case_ , snake_case_ ) -> int:
SCREAMING_SNAKE_CASE : Any =cls.squareplus(snake_case_ )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def __a ( self , snake_case_ ) -> Distribution:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] =distr_args
if self.dim == 1:
return self.distribution_class(total_count=snake_case_ , logits=snake_case_ )
else:
return Independent(self.distribution_class(total_count=snake_case_ , logits=snake_case_ ) , 1 )
def __a ( self , snake_case_ , snake_case_ = None , snake_case_ = None ) -> Distribution:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] =distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 258 | 1 |
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 UpperCamelCase_( _A :int=32 , _A :Dict=10 , _A :Optional[int]=1_00 , _A :Optional[Any]=10_26 , _A :Any=True , _A :Optional[Any]="data/tokenized_stories_train_wikitext103.jbl" , _A :Tuple="igf_context_pairs.jbl" , )-> Union[str, Any]:
set_seed(3 )
# generate train_data and objective_set
UpperCamelCase__, UpperCamelCase__ = generate_datasets(
_A , _A , number=_A , min_len=10_26 , trim=_A )
# 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?
UpperCamelCase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# load pretrained model
UpperCamelCase__ = load_gpta("gpt2" ).to(_A )
print("computing perplexity on objective set" )
UpperCamelCase__ = compute_perplexity(_A , _A , _A ).item()
print("perplexity on objective set:" , _A )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_A , _A , _A , _A , _A , _A , _A , _A )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def UpperCamelCase_( _A :Optional[Any] , _A :List[Any]=15 , _A :Union[str, Any]=1_28 , _A :int=1_00 , _A :Union[str, Any]="igf_model.pt" , )-> Optional[Any]:
set_seed(42 )
# Load pre-trained model
UpperCamelCase__ = GPTaLMHeadModel.from_pretrained("gpt2" )
# Initialize secondary learner to use embedding weights of model
UpperCamelCase__ = SecondaryLearner(_A )
# Train secondary learner
UpperCamelCase__ = train_secondary_learner(
_A , _A , max_epochs=_A , batch_size=_A , eval_freq=1_00 , igf_model_path=_A , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def UpperCamelCase_( _A :Any , _A :List[Any] , _A :Dict , _A :List[str]=32 , _A :int=10_00 , _A :Optional[int]=16 , _A :Dict=1.0 , _A :List[Any]=recopy_gpta , _A :List[str]=None , _A :Any=10 , _A :Optional[int]="gpt2_finetuned.pt" , )-> Dict:
UpperCamelCase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
UpperCamelCase__ = RandomSampler(_A )
UpperCamelCase__ = DataLoader(_A , sampler=_A )
UpperCamelCase__ = max_steps // (len(_A )) + 1
UpperCamelCase__ = 0
UpperCamelCase__ = torch.zeros((1, context_len) , dtype=torch.long , device=_A )
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ = recopy_model(_A , _A , _A )
model.train()
if secondary_learner is not None:
secondary_learner.to(_A )
secondary_learner.eval()
UpperCamelCase__ = []
UpperCamelCase__ = 0
UpperCamelCase__ = []
UpperCamelCase__ = []
# Compute the performance of the transformer model at the beginning
UpperCamelCase__ = compute_perplexity(_A , _A , _A )
test_perps.append(_A )
print("Test perplexity, step" , _A , ":" , _A )
for epoch in range(int(_A ) ):
for step, example in enumerate(_A ):
torch.cuda.empty_cache()
UpperCamelCase__ = random.randint(0 , example.size(2 ) - context_len - 1 )
UpperCamelCase__ = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
UpperCamelCase__ = model(_A , labels=_A )
UpperCamelCase__ = True
if secondary_learner is not None:
UpperCamelCase__ = secondary_learner.forward(
torch.tensor(_A , dtype=torch.long , device=_A ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_A ) )
# 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:
UpperCamelCase__ = -1
if predicted_q < threshold:
UpperCamelCase__ = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
UpperCamelCase__ = 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()
UpperCamelCase__ = 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:
UpperCamelCase__ = compute_perplexity(_A , _A , _A )
test_perps.append(_A )
print("Test perplexity, step" , _A , ":" , _A )
# 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() , _A )
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 UpperCamelCase_( )-> Optional[Any]:
UpperCamelCase__ = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )
# Required parameters
parser.add_argument(
"--data_dir" , default=_A , type=_A , required=_A , help="The input data dir. Should contain data files for WikiText." , )
parser.add_argument(
"--model_name_or_path" , default=_A , type=_A , required=_A , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--data_file" , type=_A , default=_A , 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=_A , default=_A , help="A jbl file containing the context and information gain pairs to train secondary learner." , )
parser.add_argument(
"--output_dir" , default=_A , type=_A , required=_A , help="The output directory where the final fine-tuned model is stored." , )
parser.add_argument(
"--tokenizer_name" , default=_A , type=_A , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument("--seed" , type=_A , default=_A , help="A seed for reproducible training." )
parser.add_argument(
"--context_len" , default=32 , type=_A , 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=1_00 , type=_A , help="number of articles that are long enough to be used as our objective set" , )
parser.add_argument(
"--eval_freq" , default=1_00 , type=_A , help="secondary model evaluation is triggered at eval_freq" )
parser.add_argument("--max_steps" , default=10_00 , type=_A , help="To calculate training epochs" )
parser.add_argument(
"--secondary_learner_batch_size" , default=1_28 , type=_A , help="batch size of training data for secondary learner" , )
parser.add_argument(
"--batch_size" , default=16 , type=_A , help="batch size of training data of language model(gpt2) " )
parser.add_argument(
"--eval_interval" , default=10 , type=_A , 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=1_00 , type=_A , help="The number of examples split to be used as objective_set/test_data" )
parser.add_argument(
"--min_len" , default=10_26 , type=_A , help="The minimum length of the article to be used as objective set" )
parser.add_argument(
"--secondary_learner_max_epochs" , default=15 , type=_A , help="number of epochs to train secondary learner" )
parser.add_argument("--trim" , default=_A , type=_A , help="truncate the example if it exceeds context length" )
parser.add_argument(
"--threshold" , default=1.0 , type=_A , 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=_A , help="finetuned_model_name" )
parser.add_argument(
"--recopy_model" , default=_A , type=_A , 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=1_00 , min_len=10_26 , trim=_A , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , )
# Load train data for secondary learner
UpperCamelCase__ = joblib.load("data/IGF_values.jbl" )
# Train secondary learner
UpperCamelCase__ = training_secondary_learner(
_A , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="igf_model.pt" , )
# load pretrained gpt2 model
UpperCamelCase__ = GPTaLMHeadModel.from_pretrained("gpt2" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
UpperCamelCase__, UpperCamelCase__ = generate_datasets(
context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=1_00 , min_len=10_26 , trim=_A )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_A , _A , _A , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=_A , secondary_learner=_A , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , )
if __name__ == "__main__":
main()
| 702 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
'kssteven/ibert-roberta-large-mnli': (
'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
),
}
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
_UpperCamelCase : Any = 'ibert'
def __init__( self , snake_case=30522 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-1_2 , snake_case=1 , snake_case=0 , snake_case=2 , snake_case="absolute" , snake_case=False , snake_case="none" , **snake_case , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_act
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = layer_norm_eps
UpperCamelCase__ = position_embedding_type
UpperCamelCase__ = quant_mode
UpperCamelCase__ = force_dequant
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCamelCase__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 185 | 0 |
from math import factorial, pi
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase = 30):
if not isinstance(_UpperCAmelCase , (int, float)):
raise ValueError('maclaurin_sin() requires either an int or float for theta')
if not isinstance(_UpperCAmelCase , _UpperCAmelCase) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy')
SCREAMING_SNAKE_CASE = float(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_UpperCAmelCase))
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase = 30):
if not isinstance(_UpperCAmelCase , (int, float)):
raise ValueError('maclaurin_cos() requires either an int or float for theta')
if not isinstance(_UpperCAmelCase , _UpperCAmelCase) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy')
SCREAMING_SNAKE_CASE = float(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_UpperCAmelCase))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 73 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
self.tool.setup()
SCREAMING_SNAKE_CASE = load_tool('text-classification' , remote=a)
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = self.tool('That\'s quite cool' , ['positive', 'negative'])
self.assertEqual(a , 'positive')
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.remote_tool('That\'s quite cool' , ['positive', 'negative'])
self.assertEqual(a , 'positive')
def SCREAMING_SNAKE_CASE__ ( self) -> int:
SCREAMING_SNAKE_CASE = self.tool(text='That\'s quite cool' , labels=['positive', 'negative'])
self.assertEqual(a , 'positive')
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = self.remote_tool(text='That\'s quite cool' , labels=['positive', 'negative'])
self.assertEqual(a , 'positive')
| 73 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
UpperCamelCase = parser.parse_args()
UpperCamelCase = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCamelCase = CLIPImageProcessor()
UpperCamelCase = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
UpperCamelCase = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 721 | from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCamelCase = TypeVar('T')
class _a ( Generic[T] ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
__A : Any | T = None
__A : int = len(__UpperCAmelCase )
__A : list[T] = [any_type for _ in range(self.N )] + arr
__A : Any = fnc
self.build()
def __UpperCAmelCase( self ):
for p in range(self.N - 1 , 0 , -1 ):
__A : Dict = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
p += self.N
__A : List[Any] = v
while p > 1:
__A : Optional[Any] = p // 2
__A : str = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ): # noqa: E741
__A , __A : Dict = l + self.N, r + self.N
__A : T | None = None
while l <= r:
if l % 2 == 1:
__A : Union[str, Any] = self.st[l] if res is None else self.fn(__UpperCAmelCase , self.st[l] )
if r % 2 == 0:
__A : Optional[Any] = self.st[r] if res is None else self.fn(__UpperCAmelCase , self.st[r] )
__A , __A : str = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
UpperCamelCase = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
UpperCamelCase = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
UpperCamelCase = SegmentTree(test_array, min)
UpperCamelCase = SegmentTree(test_array, max)
UpperCamelCase = SegmentTree(test_array, lambda a, b: a + b)
def lowerCamelCase_ ( ) -> None:
for i in range(len(_lowercase ) ):
for j in range(_lowercase , len(_lowercase ) ):
__A : Dict = reduce(_lowercase , test_array[i : j + 1] )
__A : int = reduce(_lowercase , test_array[i : j + 1] )
__A : Dict = reduce(lambda _lowercase , _lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(_lowercase , _lowercase )
assert max_range == max_segment_tree.query(_lowercase , _lowercase )
assert sum_range == sum_segment_tree.query(_lowercase , _lowercase )
test_all_segments()
for index, value in test_updates.items():
UpperCamelCase = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 387 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase__ ( _lowercase : int ) -> Any:
# A local function to see if a dot lands in the circle.
def is_in_circle(_lowercase : float , _lowercase : float ) -> bool:
__UpperCAmelCase: Union[str, Any] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__UpperCAmelCase: int = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(_lowercase ) )
# The ratio of the area for circle to square is pi/4.
__UpperCAmelCase: Any = proportion * 4
print(F'''The estimated value of pi is {pi_estimate}''' )
print(F'''The numpy value of pi is {pi}''' )
print(F'''The total error is {abs(pi - pi_estimate )}''' )
def UpperCamelCase__ ( _lowercase : int , _lowercase : Callable[[float], float] , _lowercase : float = 0.0 , _lowercase : float = 1.0 , ) -> float:
return mean(
function_to_integrate(uniform(_lowercase , _lowercase ) ) for _ in range(_lowercase ) ) * (max_value - min_value)
def UpperCamelCase__ ( _lowercase : int , _lowercase : float = 0.0 , _lowercase : float = 1.0 ) -> None:
def identity_function(_lowercase : float ) -> float:
return x
__UpperCAmelCase: List[Any] = area_under_curve_estimator(
_lowercase , _lowercase , _lowercase , _lowercase )
__UpperCAmelCase: Optional[Any] = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(F'''Estimated value is {estimated_value}''' )
print(F'''Expected value is {expected_value}''' )
print(F'''Total error is {abs(estimated_value - expected_value )}''' )
print("""******************""" )
def UpperCamelCase__ ( _lowercase : int ) -> None:
def function_to_integrate(_lowercase : float ) -> float:
return sqrt(4.0 - x * x )
__UpperCAmelCase: Dict = area_under_curve_estimator(
_lowercase , _lowercase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'''Estimated value is {estimated_value}''' )
print(F'''Expected value is {pi}''' )
print(F'''Total error is {abs(estimated_value - pi )}''' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE_ = 2_99_79_24_58
# Symbols
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = symbols('ct x y z')
def UpperCamelCase__ ( _lowercase : float ) -> float:
if velocity > c:
raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!""" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("""Speed must be greater than or equal to 1!""" )
return velocity / c
def UpperCamelCase__ ( _lowercase : float ) -> float:
return 1 / sqrt(1 - beta(_lowercase ) ** 2 )
def UpperCamelCase__ ( _lowercase : float ) -> np.ndarray:
return np.array(
[
[gamma(_lowercase ), -gamma(_lowercase ) * beta(_lowercase ), 0, 0],
[-gamma(_lowercase ) * beta(_lowercase ), gamma(_lowercase ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def UpperCamelCase__ ( _lowercase : float , _lowercase : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
__UpperCAmelCase: List[str] = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(_lowercase ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
SCREAMING_SNAKE_CASE_ = transform(29_97_92_45)
print('Example of four vector: ')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
SCREAMING_SNAKE_CASE_ = {ct: c, x: 1, y: 1, z: 1}
SCREAMING_SNAKE_CASE_ = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""") | 523 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
def _A ( snake_case__ : List[Any] ):
# initialize config
if "resnet-50" in model_name:
snake_case__ : Optional[int] = ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
elif "resnet-101" in model_name:
snake_case__ : Tuple = ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
else:
raise ValueError('''Model name should include either resnet50 or resnet101''' )
snake_case__ : int = DetrConfig(use_timm_backbone=snake_case__ , backbone_config=snake_case__ )
# set label attributes
snake_case__ : List[Any] = '''panoptic''' in model_name
if is_panoptic:
snake_case__ : Tuple = 2_50
else:
snake_case__ : Tuple = 91
snake_case__ : List[str] = '''huggingface/label-files'''
snake_case__ : List[Any] = '''coco-detection-id2label.json'''
snake_case__ : Union[str, Any] = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='''dataset''' ) , '''r''' ) )
snake_case__ : Tuple = {int(snake_case__ ): v for k, v in idalabel.items()}
snake_case__ : Dict = idalabel
snake_case__ : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def _A ( snake_case__ : int ):
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ : Any = []
# stem
# fmt: off
rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''') )
rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''') )
rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''') )
rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''') )
rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
f'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
f'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
] )
return rename_keys
def _A ( snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : List[str] ):
snake_case__ : int = state_dict.pop(snake_case__ )
snake_case__ : Optional[int] = val
def _A ( snake_case__ : Dict , snake_case__ : str=False ):
snake_case__ : str = ''''''
if is_panoptic:
snake_case__ : int = '''detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case__ : str = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case__ : List[str] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Any = in_proj_weight[:2_56, :]
snake_case__ : List[str] = in_proj_bias[:2_56]
snake_case__ : Union[str, Any] = in_proj_weight[2_56:5_12, :]
snake_case__ : Union[str, Any] = in_proj_bias[2_56:5_12]
snake_case__ : Optional[int] = in_proj_weight[-2_56:, :]
snake_case__ : Optional[Any] = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
snake_case__ : Dict = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case__ : str = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Optional[int] = in_proj_weight[:2_56, :]
snake_case__ : str = in_proj_bias[:2_56]
snake_case__ : Union[str, Any] = in_proj_weight[2_56:5_12, :]
snake_case__ : Union[str, Any] = in_proj_bias[2_56:5_12]
snake_case__ : List[str] = in_proj_weight[-2_56:, :]
snake_case__ : int = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
snake_case__ : Dict = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
snake_case__ : Optional[Any] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
snake_case__ : Tuple = in_proj_weight_cross_attn[:2_56, :]
snake_case__ : Any = in_proj_bias_cross_attn[:2_56]
snake_case__ : List[str] = in_proj_weight_cross_attn[2_56:5_12, :]
snake_case__ : List[str] = in_proj_bias_cross_attn[2_56:5_12]
snake_case__ : Optional[Any] = in_proj_weight_cross_attn[-2_56:, :]
snake_case__ : Tuple = in_proj_bias_cross_attn[-2_56:]
def _A ( ):
snake_case__ : Dict = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case__ : Optional[int] = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
@torch.no_grad()
def _A ( snake_case__ : str , snake_case__ : str=None , snake_case__ : int=False ):
snake_case__ : List[Any] = get_detr_config(snake_case__ )
# load original model from torch hub
snake_case__ : List[Any] = {
'''detr-resnet-50''': '''detr_resnet50''',
'''detr-resnet-101''': '''detr_resnet101''',
}
logger.info(f'''Converting model {model_name}...''' )
snake_case__ : Tuple = torch.hub.load('''facebookresearch/detr''' , model_name_to_original_name[model_name] , pretrained=snake_case__ ).eval()
snake_case__ : Optional[int] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(snake_case__ ):
if is_panoptic:
snake_case__ : Dict = '''detr.''' + src
rename_key(snake_case__ , snake_case__ , snake_case__ )
# query, key and value matrices need special treatment
read_in_q_k_v(snake_case__ , is_panoptic=snake_case__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case__ : str = '''detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
snake_case__ : str = state_dict.pop(snake_case__ )
snake_case__ : int = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case__ : List[Any] = state_dict.pop(snake_case__ )
snake_case__ : str = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
snake_case__ : Any = state_dict.pop(snake_case__ )
snake_case__ : List[Any] = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
snake_case__ : Union[str, Any] = state_dict.pop(snake_case__ )
snake_case__ : Dict = val
# finally, create HuggingFace model and load state dict
snake_case__ : Optional[Any] = DetrForSegmentation(snake_case__ ) if is_panoptic else DetrForObjectDetection(snake_case__ )
model.load_state_dict(snake_case__ )
model.eval()
# verify our conversion on an image
snake_case__ : str = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
snake_case__ : str = DetrImageProcessor(format=snake_case__ )
snake_case__ : Union[str, Any] = processor(images=prepare_img() , return_tensors='''pt''' )
snake_case__ : Optional[int] = encoding['''pixel_values''']
snake_case__ : Tuple = detr(snake_case__ )
snake_case__ : List[str] = model(snake_case__ )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
model.save_pretrained(snake_case__ )
processor.save_pretrained(snake_case__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('''Uploading PyTorch model and image processor to the hub...''' )
model.push_to_hub(f'''nielsr/{model_name}''' )
processor.push_to_hub(f'''nielsr/{model_name}''' )
if __name__ == "__main__":
_lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
_lowerCAmelCase : Any = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 712 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : Tuple = [float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCAmelCase : List[str] = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 694 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowercase_ : str = re.compile(R'\s+')
def A__ ( snake_case_ : List[str] ):
return {"hash": hashlib.mda(re.sub(snake_case_ , '''''' , example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def A__ ( snake_case_ : Optional[int] ):
SCREAMING_SNAKE_CASE__: Any= [len(snake_case_ ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(snake_case_ ), "line_max": max(snake_case_ )}
def A__ ( snake_case_ : List[Any] ):
SCREAMING_SNAKE_CASE__: Optional[int]= np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def A__ ( snake_case_ : List[str] , snake_case_ : str ):
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def A__ ( snake_case_ : Union[str, Any] , snake_case_ : Dict=5 ):
SCREAMING_SNAKE_CASE__: Tuple= ['''auto-generated''', '''autogenerated''', '''automatically generated''']
SCREAMING_SNAKE_CASE__: Any= example['''content'''].splitlines()
for _, line in zip(range(snake_case_ ) , snake_case_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def A__ ( snake_case_ : Dict , snake_case_ : int=5 , snake_case_ : Tuple=0.05 ):
SCREAMING_SNAKE_CASE__: List[Any]= ['''unit tests''', '''test file''', '''configuration file''']
SCREAMING_SNAKE_CASE__: Optional[int]= example['''content'''].splitlines()
SCREAMING_SNAKE_CASE__: Tuple= 0
SCREAMING_SNAKE_CASE__: Tuple= 0
# first test
for _, line in zip(range(snake_case_ ) , snake_case_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
SCREAMING_SNAKE_CASE__: Union[str, Any]= example['''content'''].count('''\n''' )
SCREAMING_SNAKE_CASE__: Tuple= int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def A__ ( snake_case_ : Dict ):
SCREAMING_SNAKE_CASE__: Dict= ['''def ''', '''class ''', '''for ''', '''while ''']
SCREAMING_SNAKE_CASE__: str= example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def A__ ( snake_case_ : Optional[Any] , snake_case_ : int=4 ):
SCREAMING_SNAKE_CASE__: Optional[int]= example['''content'''].splitlines()
SCREAMING_SNAKE_CASE__: List[Any]= 0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def A__ ( snake_case_ : Optional[Any] ):
SCREAMING_SNAKE_CASE__: Union[str, Any]= tokenizer(example['''content'''] , truncation=snake_case_ )['''input_ids''']
SCREAMING_SNAKE_CASE__: Dict= len(example['''content'''] ) / len(snake_case_ )
return {"ratio": ratio}
def A__ ( snake_case_ : str ):
SCREAMING_SNAKE_CASE__: Optional[Any]= {}
results.update(get_hash(snake_case_ ) )
results.update(line_stats(snake_case_ ) )
results.update(alpha_stats(snake_case_ ) )
results.update(char_token_ratio(snake_case_ ) )
results.update(is_autogenerated(snake_case_ ) )
results.update(is_config_or_test(snake_case_ ) )
results.update(has_no_keywords(snake_case_ ) )
results.update(has_few_assignments(snake_case_ ) )
return results
def A__ ( snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int ):
if not check_uniques(snake_case_ , snake_case_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def A__ ( snake_case_ : Union[str, Any] ):
with open(snake_case_ , '''rb''' ) as f_in:
with gzip.open(str(snake_case_ ) + '''.gz''' , '''wb''' , compresslevel=6 ) as f_out:
shutil.copyfileobj(snake_case_ , snake_case_ )
os.unlink(snake_case_ )
# Settings
lowercase_ : Dict = HfArgumentParser(PreprocessingArguments)
lowercase_ : Union[str, Any] = parser.parse_args()
if args.num_workers is None:
lowercase_ : List[str] = multiprocessing.cpu_count()
lowercase_ : List[str] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowercase_ : Optional[int] = time.time()
lowercase_ : Tuple = load_dataset(args.dataset_name, split='train')
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')
# Run preprocessing
lowercase_ : List[Any] = time.time()
lowercase_ : Dict = ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')
# Deduplicate hashes
lowercase_ : Optional[int] = set(ds.unique('hash'))
lowercase_ : Optional[int] = len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')
# Deduplicate data and apply heuristics
lowercase_ : List[Any] = time.time()
lowercase_ : List[Any] = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowercase_ : str = time.time()
lowercase_ , lowercase_ : Optional[int] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
print(f'''Size of deduplicate dataset: {len(ds_filter)}''')
# Save data in batches of samples_per_file
lowercase_ : List[Any] = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
lowercase_ : Optional[int] = output_dir / 'data'
data_dir.mkdir(exist_ok=True)
lowercase_ : List[str] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowercase_ : Union[str, Any] = str(data_dir / f'''file-{file_number+1:012}.json''')
lowercase_ : List[Any] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(f'''Time to save dataset: {time.time()-t_start:.2f}''')
| 64 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Optional[int] = "▁"
__A : Dict = {"vocab_file": "spiece.model"}
__A : Optional[Any] = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
__A : Optional[Any] = {
"google/reformer-crime-and-punishment": 524_288,
}
class A_ (a_ ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self , _A , _A="</s>" , _A="<unk>" , _A=[] , _A = None , **_A , ):
'''simple docstring'''
UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_A , unk_token=_A , additional_special_tokens=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
UpperCAmelCase = vocab_file
UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_A )
@property
def _lowercase ( self ):
'''simple docstring'''
return self.sp_model.get_piece_size()
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
'''simple docstring'''
UpperCAmelCase = self.__dict__.copy()
UpperCAmelCase = None
return state
def __setstate__( self , _A ):
'''simple docstring'''
UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCAmelCase = {}
UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowercase ( self , _A ):
'''simple docstring'''
return self.sp_model.encode(_A , out_type=_A )
def _lowercase ( self , _A ):
'''simple docstring'''
return self.sp_model.piece_to_id(_A )
def _lowercase ( self , _A ):
'''simple docstring'''
if index < self.sp_model.get_piece_size():
UpperCAmelCase = self.sp_model.IdToPiece(_A )
return token
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_A ) + token
UpperCAmelCase = []
else:
current_sub_tokens.append(_A )
out_string += self.sp_model.decode(_A )
return out_string.strip()
def _lowercase ( self , _A , _A = None ):
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , '''wb''' ) as fi:
UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 130 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Dict = 1
__UpperCamelCase :int = 3
__UpperCamelCase :Dict = (32, 32)
__UpperCamelCase :Any = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0)).to(__lowercase)
return image
@property
def UpperCamelCase__ ( self) -> int:
torch.manual_seed(0)
__UpperCamelCase :List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
return model
@property
def UpperCamelCase__ ( self) -> Any:
torch.manual_seed(0)
__UpperCamelCase :Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def UpperCamelCase__ ( self) -> int:
torch.manual_seed(0)
__UpperCamelCase :Optional[Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_006 , )
return RobertaSeriesModelWithTransformation(__lowercase)
@property
def UpperCamelCase__ ( self) -> Optional[int]:
def extract(*__lowercase , **__lowercase):
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self) -> Union[str, Any]:
__UpperCamelCase :Any = torch.ones([0])
def UpperCamelCase__ ( self , __lowercase) -> List[str]:
self.pixel_values.to(__lowercase)
return self
return Out()
return extract
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase :List[str] = self.dummy_cond_unet
__UpperCamelCase :Any = PNDMScheduler(skip_prk_steps=__lowercase)
__UpperCamelCase :str = self.dummy_vae
__UpperCamelCase :int = self.dummy_text_encoder
__UpperCamelCase :Optional[int] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
__UpperCamelCase :List[str] = 77
__UpperCamelCase :int = self.dummy_image.to(__lowercase)
__UpperCamelCase :Optional[int] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
__UpperCamelCase :Any = AltDiffusionImgaImgPipeline(
unet=__lowercase , scheduler=__lowercase , vae=__lowercase , text_encoder=__lowercase , tokenizer=__lowercase , safety_checker=__lowercase , feature_extractor=self.dummy_extractor , )
__UpperCamelCase :List[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=__lowercase)
__UpperCamelCase :int = alt_pipe.to(__lowercase)
alt_pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :Optional[Any] = '''A painting of a squirrel eating a burger'''
__UpperCamelCase :Any = torch.Generator(device=__lowercase).manual_seed(0)
__UpperCamelCase :Dict = alt_pipe(
[prompt] , generator=__lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=__lowercase , )
__UpperCamelCase :Tuple = output.images
__UpperCamelCase :List[Any] = torch.Generator(device=__lowercase).manual_seed(0)
__UpperCamelCase :List[Any] = alt_pipe(
[prompt] , generator=__lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=__lowercase , return_dict=__lowercase , )[0]
__UpperCamelCase :Tuple = image[0, -3:, -3:, -1]
__UpperCamelCase :List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__UpperCamelCase :str = np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''')
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Tuple = self.dummy_cond_unet
__UpperCamelCase :Optional[int] = PNDMScheduler(skip_prk_steps=__lowercase)
__UpperCamelCase :List[Any] = self.dummy_vae
__UpperCamelCase :Tuple = self.dummy_text_encoder
__UpperCamelCase :Dict = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
__UpperCamelCase :Optional[Any] = 77
__UpperCamelCase :Tuple = self.dummy_image.to(__lowercase)
# put models in fp16
__UpperCamelCase :Dict = unet.half()
__UpperCamelCase :Tuple = vae.half()
__UpperCamelCase :Optional[Any] = bert.half()
# make sure here that pndm scheduler skips prk
__UpperCamelCase :List[Any] = AltDiffusionImgaImgPipeline(
unet=__lowercase , scheduler=__lowercase , vae=__lowercase , text_encoder=__lowercase , tokenizer=__lowercase , safety_checker=__lowercase , feature_extractor=self.dummy_extractor , )
__UpperCamelCase :Optional[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=__lowercase)
__UpperCamelCase :Optional[Any] = alt_pipe.to(__lowercase)
alt_pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :List[Any] = '''A painting of a squirrel eating a burger'''
__UpperCamelCase :str = torch.manual_seed(0)
__UpperCamelCase :Dict = alt_pipe(
[prompt] , generator=__lowercase , num_inference_steps=2 , output_type='''np''' , image=__lowercase , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''')
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Optional[int] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''')
# resize to resolution that is divisible by 8 but not 16 or 32
__UpperCamelCase :str = init_image.resize((760, 504))
__UpperCamelCase :Optional[Any] = '''BAAI/AltDiffusion'''
__UpperCamelCase :Tuple = AltDiffusionImgaImgPipeline.from_pretrained(
__lowercase , safety_checker=__lowercase , )
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
pipe.enable_attention_slicing()
__UpperCamelCase :List[str] = '''A fantasy landscape, trending on artstation'''
__UpperCamelCase :List[str] = torch.manual_seed(0)
__UpperCamelCase :Optional[int] = pipe(
prompt=__lowercase , image=__lowercase , strength=0.75 , guidance_scale=7.5 , generator=__lowercase , output_type='''np''' , )
__UpperCamelCase :List[str] = output.images[0]
__UpperCamelCase :List[str] = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
__UpperCamelCase :str = np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''')
__UpperCamelCase :List[str] = init_image.resize((768, 512))
__UpperCamelCase :List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')
__UpperCamelCase :int = '''BAAI/AltDiffusion'''
__UpperCamelCase :List[str] = AltDiffusionImgaImgPipeline.from_pretrained(
__lowercase , safety_checker=__lowercase , )
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
pipe.enable_attention_slicing()
__UpperCamelCase :Tuple = '''A fantasy landscape, trending on artstation'''
__UpperCamelCase :Dict = torch.manual_seed(0)
__UpperCamelCase :Dict = pipe(
prompt=__lowercase , image=__lowercase , strength=0.75 , guidance_scale=7.5 , generator=__lowercase , output_type='''np''' , )
__UpperCamelCase :Tuple = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image).max() < 1E-2
| 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__lowercase = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ASTForAudioClassification''',
'''ASTModel''',
'''ASTPreTrainedModel''',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ['''ASTFeatureExtractor''']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 452 | 0 |
from __future__ import annotations
import time
import numpy as np
__a = [8, 5, 9, 7]
__a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : int = claim_vector
lowercase : Tuple = allocated_resources_table
lowercase : Dict = maximum_claim_table
def __lowerCamelCase ( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def __lowerCamelCase ( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def __lowerCamelCase ( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(SCREAMING_SNAKE_CASE__ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def __lowerCamelCase ( self ):
return {self.__need().index(SCREAMING_SNAKE_CASE__ ): i for i in self.__need()}
def __lowerCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = self.__need()
lowercase : Optional[Any] = self.__allocated_resources_table
lowercase : Optional[Any] = self.__available_resources()
lowercase : Any = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowercase : Tuple = False
for each_need in need_list:
lowercase : Dict = True
for index, need in enumerate(SCREAMING_SNAKE_CASE__ ):
if need > available_resources[index]:
lowercase : Tuple = False
break
if execution:
lowercase : int = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase : Union[str, Any] = original_need_index
print(f"""Process {process_number + 1} is executing.""" )
# remove the process run from stack
need_list.remove(SCREAMING_SNAKE_CASE__ )
# update available/freed resources stack
lowercase : List[Any] = np.array(SCREAMING_SNAKE_CASE__ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(SCREAMING_SNAKE_CASE__ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def __lowerCamelCase ( self ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
f"""P{self.__allocated_resources_table.index(SCREAMING_SNAKE_CASE__ ) + 1}"""
+ ''' '''.join(f"""{it:>8}""" for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
f"""P{self.__maximum_claim_table.index(SCREAMING_SNAKE_CASE__ ) + 1}"""
+ ''' '''.join(f"""{it:>8}""" for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(SCREAMING_SNAKE_CASE__ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(SCREAMING_SNAKE_CASE__ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __SCREAMING_SNAKE_CASE :
@property
def __lowerCamelCase ( self ):
return self.get_dummy_input()
@property
def __lowerCamelCase ( self ):
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , ):
lowercase : Optional[int] = 4
lowercase : Dict = 32
lowercase : List[str] = (32, 32)
lowercase : Optional[int] = torch.manual_seed(0 )
lowercase : Optional[int] = torch.device(SCREAMING_SNAKE_CASE__ )
lowercase : int = (batch_size, num_channels) + sizes
lowercase : str = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = {'''hidden_states''': hidden_states}
if include_temb:
lowercase : List[Any] = 128
lowercase : List[Any] = randn_tensor((batch_size, temb_channels) , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
if include_res_hidden_states_tuple:
lowercase : List[Any] = torch.manual_seed(1 )
lowercase : Optional[Any] = (randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ ),)
if include_encoder_hidden_states:
lowercase : Optional[Any] = floats_tensor((batch_size, 32, 32) ).to(SCREAMING_SNAKE_CASE__ )
if include_skip_sample:
lowercase : Dict = randn_tensor(((batch_size, 3) + sizes) , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
return dummy_input
def __lowerCamelCase ( self ):
lowercase : Optional[int] = {
'''in_channels''': 32,
'''out_channels''': 32,
'''temb_channels''': 128,
}
if self.block_type == "up":
lowercase : Optional[int] = 32
if self.block_type == "mid":
init_dict.pop('''out_channels''' )
lowercase : Union[str, Any] = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.prepare_init_args_and_inputs_for_common()
lowercase : List[str] = self.block_class(**SCREAMING_SNAKE_CASE__ )
unet_block.to(SCREAMING_SNAKE_CASE__ )
unet_block.eval()
with torch.no_grad():
lowercase : Tuple = unet_block(**SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = output[0]
self.assertEqual(output.shape , self.output_shape )
lowercase : Optional[Any] = output[0, -1, -3:, -3:]
lowercase : Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
assert torch_all_close(output_slice.flatten() , SCREAMING_SNAKE_CASE__ , atol=5E-3 )
@unittest.skipIf(torch_device == '''mps''' , '''Training is not supported in mps''' )
def __lowerCamelCase ( self ):
lowercase , lowercase : Dict = self.prepare_init_args_and_inputs_for_common()
lowercase : Optional[int] = self.block_class(**SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.train()
lowercase : Optional[Any] = model(**SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = output[0]
lowercase : int = torch.device(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = randn_tensor(output.shape , device=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
loss.backward()
| 319 | 1 |
def snake_case (UpperCamelCase : int , UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3317044064679887385961981 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
lowerCamelCase__ = [
2047,
1373653,
25326001,
3215031751,
2152302898747,
3474749660383,
341550071728321,
1,
3825123056546413051,
1,
1,
318665857834031151167461,
3317044064679887385961981,
]
lowerCamelCase__ = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(UpperCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
lowerCamelCase__ = primes[:idx]
break
lowerCamelCase__ , lowerCamelCase__ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowerCamelCase__ = False
for r in range(UpperCamelCase ):
lowerCamelCase__ = pow(UpperCamelCase , d * 2**r , UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowerCamelCase__ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def snake_case ():
'''simple docstring'''
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(838201 )
assert miller_rabin(838207 )
# 1_373_653
assert not miller_rabin(17316001 )
assert miller_rabin(17316017 )
# 25_326_001
assert not miller_rabin(3078386641 )
assert miller_rabin(3078386653 )
# 3_215_031_751
assert not miller_rabin(1713045574801 )
assert miller_rabin(1713045574819 )
# 2_152_302_898_747
assert not miller_rabin(2779799728307 )
assert miller_rabin(2779799728327 )
# 3_474_749_660_383
assert not miller_rabin(113850023909441 )
assert miller_rabin(113850023909527 )
# 341_550_071_728_321
assert not miller_rabin(1275041018848804351 )
assert miller_rabin(1275041018848804391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(79666464458507787791867 )
assert miller_rabin(79666464458507787791951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552840677446647897660333 )
assert miller_rabin(552840677446647897660359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 235 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowercase :
"""simple docstring"""
def __init__( self : Optional[int] , a_ : list[tuple[float, float]] ):
"""simple docstring"""
lowerCamelCase__ = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowerCamelCase__ = len(a_ ) - 1
def _UpperCamelCase ( self : Union[str, Any] , a_ : float ):
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase__ = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , a_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(a_ ) , 5 ) == 1
return output_values
def _UpperCamelCase ( self : int , a_ : float ):
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase__ = self.basis_function(a_ )
lowerCamelCase__ = 0.0
lowerCamelCase__ = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _UpperCamelCase ( self : str , a_ : float = 0.0_1 ):
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
lowerCamelCase__ = [] # x coordinates of points to plot
lowerCamelCase__ = [] # y coordinates of points to plot
lowerCamelCase__ = 0.0
while t <= 1:
lowerCamelCase__ = self.bezier_curve_function(a_ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowerCamelCase__ = [i[0] for i in self.list_of_points]
lowerCamelCase__ = [i[1] for i in self.list_of_points]
plt.plot(
a_ , a_ , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(a_ , a_ , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 235 | 1 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
a = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
a = re.compile(r'''([a-z\d])([A-Z])''')
a = re.compile(r'''(?<!_)_(?!_)''')
a = re.compile(r'''(_{2,})''')
a = r'''^\w+(\.\w+)*$'''
a = r'''<>:/\|?*'''
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = _uppercase_uppercase_re.sub(R'\1_\2' , _snake_case )
_A = _lowercase_uppercase_re.sub(R'\1_\2' , _snake_case )
return name.lower()
def _snake_case ( _snake_case : List[str] ) -> Optional[int]:
'''simple docstring'''
_A = _single_underscore_re.split(_snake_case )
_A = [_multiple_underscores_re.split(_snake_case ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(_snake_case ) if n != '' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
if os.path.basename(_snake_case ) != name:
raise ValueError(F'''Should be a dataset name, not a path: {name}''' )
return camelcase_to_snakecase(_snake_case )
def _snake_case ( _snake_case : int , _snake_case : Any ) -> Optional[int]:
'''simple docstring'''
if os.path.basename(_snake_case ) != name:
raise ValueError(F'''Should be a dataset name, not a path: {name}''' )
if not re.match(_split_re , _snake_case ):
raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.''' )
return F'''{filename_prefix_for_name(_snake_case )}-{split}'''
def _snake_case ( _snake_case : Optional[int] , _snake_case : str , _snake_case : int , _snake_case : Union[str, Any]=None ) -> Any:
'''simple docstring'''
_A = filename_prefix_for_split(_snake_case , _snake_case )
if filetype_suffix:
prefix += F'''.{filetype_suffix}'''
_A = os.path.join(_snake_case , _snake_case )
return F'''{filepath}*'''
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Union[str, Any] , _snake_case : str=None , _snake_case : List[Any]=None ) -> Dict:
'''simple docstring'''
_A = filename_prefix_for_split(_snake_case , _snake_case )
_A = os.path.join(_snake_case , _snake_case )
if shard_lengths:
_A = len(_snake_case )
_A = [F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(_snake_case )]
if filetype_suffix:
_A = [filename + F'''.{filetype_suffix}''' for filename in filenames]
return filenames
else:
_A = prefix
if filetype_suffix:
filename += F'''.{filetype_suffix}'''
return [filename]
| 7 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = '''gpt_bigcode'''
UpperCAmelCase : str = ['''past_key_values''']
UpperCAmelCase : Dict = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Tuple , _UpperCAmelCase : Dict=50_257 , _UpperCAmelCase : List[Any]=1_024 , _UpperCAmelCase : Any=768 , _UpperCAmelCase : int=12 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : str="gelu_pytorch_tanh" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Optional[Any]=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : List[Any]=50_256 , _UpperCAmelCase : Dict=50_256 , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Any=True , **_UpperCAmelCase : Any , ):
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = n_inner
_A = activation_function
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = scale_attn_weights
_A = use_cache
_A = attention_softmax_in_fpaa
_A = scale_attention_softmax_in_fpaa
_A = multi_query
_A = bos_token_id
_A = eos_token_id
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
| 7 | 1 |
"""simple docstring"""
def A( snake_case_ ):
"""simple docstring"""
return str(snake_case_ ) == str(snake_case_ )[::-1]
def A( snake_case_ ):
"""simple docstring"""
return int(snake_case_ ) + int(str(snake_case_ )[::-1] )
def A( snake_case_ = 10000 ):
"""simple docstring"""
lowercase__: Union[str, Any] = []
for num in range(1 , snake_case_ ):
lowercase__: Optional[Any] = 0
lowercase__: Dict = num
while iterations < 50:
lowercase__: Union[str, Any] = sum_reverse(snake_case_ )
iterations += 1
if is_palindrome(snake_case_ ):
break
else:
lychrel_nums.append(snake_case_ )
return len(snake_case_ )
if __name__ == "__main__":
print(F"{solution() = }")
| 707 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}
class _a ( lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = """ernie_m"""
UpperCamelCase__ = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , UpperCAmelCase_ = 250_002 , UpperCAmelCase_ = 768 , UpperCAmelCase_ = 12 , UpperCAmelCase_ = 12 , UpperCAmelCase_ = 3_072 , UpperCAmelCase_ = "gelu" , UpperCAmelCase_ = 0.1 , UpperCAmelCase_ = 0.1 , UpperCAmelCase_ = 514 , UpperCAmelCase_ = 0.02 , UpperCAmelCase_ = 1 , UpperCAmelCase_ = 1E-0_5 , UpperCAmelCase_=None , UpperCAmelCase_=False , UpperCAmelCase_=0.0 , **UpperCAmelCase_ , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase_ , **UpperCAmelCase_)
lowercase__: Union[str, Any] = vocab_size
lowercase__: List[Any] = hidden_size
lowercase__: List[Any] = num_hidden_layers
lowercase__: Tuple = num_attention_heads
lowercase__: Optional[int] = intermediate_size
lowercase__: List[Any] = hidden_act
lowercase__: Optional[Any] = hidden_dropout_prob
lowercase__: str = attention_probs_dropout_prob
lowercase__: Tuple = max_position_embeddings
lowercase__: str = initializer_range
lowercase__: List[Any] = layer_norm_eps
lowercase__: List[str] = classifier_dropout
lowercase__: Optional[Any] = is_decoder
lowercase__: Tuple = act_dropout
| 120 | 0 |
"""simple docstring"""
from math import factorial
class a :
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] ) -> int:
lowerCamelCase_ = real
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = [1] * rank
else:
lowerCamelCase_ = rank
def __repr__( self : Dict ) -> Union[str, Any]:
return (
F'''{self.real}+'''
F'''{"+".join(str(__SCREAMING_SNAKE_CASE )+"E"+str(n+1 )for n,dual in enumerate(self.duals ) )}'''
)
def UpperCamelCase ( self : List[str] ) -> Tuple:
lowerCamelCase_ = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , __SCREAMING_SNAKE_CASE )
def __add__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[Any]:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return Dual(self.real + other , self.duals )
lowerCamelCase_ = self.duals.copy()
lowerCamelCase_ = other.duals.copy()
if len(__SCREAMING_SNAKE_CASE ) > len(__SCREAMING_SNAKE_CASE ):
o_dual.extend([1] * (len(__SCREAMING_SNAKE_CASE ) - len(__SCREAMING_SNAKE_CASE )) )
elif len(__SCREAMING_SNAKE_CASE ) < len(__SCREAMING_SNAKE_CASE ):
s_dual.extend([1] * (len(__SCREAMING_SNAKE_CASE ) - len(__SCREAMING_SNAKE_CASE )) )
lowerCamelCase_ = []
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , __SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : List[Any] = __add__
def __sub__( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
return self + other * -1
def __mul__( self : Tuple , __SCREAMING_SNAKE_CASE : int ) -> Tuple:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , __SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : str = __mul__
def __truediv__( self : Tuple , __SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , __SCREAMING_SNAKE_CASE )
raise ValueError
def __floordiv__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> Any:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , __SCREAMING_SNAKE_CASE )
raise ValueError
def __pow__( self : List[str] , __SCREAMING_SNAKE_CASE : List[Any] ) -> str:
if n < 0 or isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError('power must be a positive integer' )
if n == 0:
return 1
if n == 1:
return self
lowerCamelCase_ = self
for _ in range(n - 1 ):
x *= self
return x
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ) -> Any:
if not callable(_lowerCamelCase ):
raise ValueError('differentiate() requires a function as input for func' )
if not isinstance(_lowerCamelCase , (float, int) ):
raise ValueError('differentiate() requires a float as input for position' )
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError('differentiate() requires an int as input for order' )
lowerCamelCase_ = Dual(_lowerCamelCase , 1 )
lowerCamelCase_ = func(_lowerCamelCase )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
def lowerCamelCase__ ( _lowerCamelCase : Union[str, Any] ) -> Dict:
return y**2 * y**4
print(differentiate(f, 9, 2))
| 549 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE : Optional[int] = DebertaTokenizer
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Any = DebertaTokenizerFast
def UpperCamelCase ( self : Optional[Any] ) -> Dict:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'[UNK]',
]
lowerCamelCase_ = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
lowerCamelCase_ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
lowerCamelCase_ = {'unk_token': '[UNK]'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCamelCase ( self : Tuple , **__SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Any ) -> Dict:
lowerCamelCase_ = 'lower newer'
lowerCamelCase_ = 'lower newer'
return input_text, output_text
def UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = 'lower newer'
lowerCamelCase_ = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
lowerCamelCase_ = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = tokenizer('Hello' , 'World' )
lowerCamelCase_ = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['token_type_ids'] , __SCREAMING_SNAKE_CASE )
@slow
def UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
lowerCamelCase_ = self.tokenizer_class.from_pretrained('microsoft/deberta-base' )
lowerCamelCase_ = tokenizer.encode('sequence builders' , add_special_tokens=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokenizer.encode('multi-sequence build' , add_special_tokens=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokenizer.encode(
'sequence builders' , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
lowerCamelCase_ = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
lowerCamelCase_ = tokenizer_class.from_pretrained('microsoft/deberta-base' )
lowerCamelCase_ = [
'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.',
]
lowerCamelCase_ = tokenizer(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = [tokenizer.decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE ) for seq in encoding['input_ids']]
# fmt: off
lowerCamelCase_ = {
'input_ids': [
[1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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
lowerCamelCase_ = [
'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 , __SCREAMING_SNAKE_CASE )
for expected, decoded in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 549 | 1 |
def UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ ) -> float:
__lowercase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCase__ ( ) -> List[str]:
print(sum_of_series(1 , 1 , 10 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 634 |
def UpperCAmelCase__ ( lowercase__ ) -> Optional[int]:
__lowercase = len(lowercase__ )
__lowercase = sum(lowercase__ )
__lowercase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowercase = True
for i in range(1 , s + 1 ):
__lowercase = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowercase = dp[i][j - 1]
if arr[i - 1] <= j:
__lowercase = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowercase = s - 2 * j
break
return diff
| 634 | 1 |
a : List[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
a : Optional[Any] = {value: key for key, value in encode_dict.items()}
def lowerCamelCase__ ( __lowerCamelCase : str ):
__UpperCAmelCase : Tuple = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCamelCase__ ( __lowerCamelCase : str ):
if set(__lowerCamelCase ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
__UpperCAmelCase : int = """"""
for word in coded.split():
while len(__lowerCamelCase ) != 0:
decoded += decode_dict[word[:5]]
__UpperCAmelCase : Any = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 63 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar('''T''')
class __magic_name__ ( Generic[T] ):
def __init__( self : int ,_UpperCAmelCase : bool = True ):
_a : dict[T, list[T]] = {} # dictionary of lists
_a : Tuple = directed
def __lowercase ( self : Union[str, Any] ,_UpperCAmelCase : T ,_UpperCAmelCase : T ):
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(_UpperCAmelCase )
self.adj_list[destination_vertex].append(_UpperCAmelCase )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(_UpperCAmelCase )
_a : Optional[Any] = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(_UpperCAmelCase )
_a : Union[str, Any] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
_a : Union[str, Any] = [destination_vertex]
_a : Optional[int] = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(_UpperCAmelCase )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(_UpperCAmelCase )
_a : int = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
_a : Tuple = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
_a : Tuple = [destination_vertex]
_a : str = []
return self
def __repr__( self : int ):
return pformat(self.adj_list )
| 358 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
__a: Optional[Any] = logging.get_logger(__name__)
__a: List[str] = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
__a: str = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : List[Any] = torch.load(UpperCAmelCase , map_location='''cpu''' )
return sd
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=rename_keys_prefix ):
lowercase__ : Optional[Any] = OrderedDict()
lowercase__ : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
lowercase__ : Optional[int] = key
for name_pair in rename_keys_prefix:
lowercase__ : Optional[Any] = new_key.replace(name_pair[0] , name_pair[1] )
lowercase__ : Tuple = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
lowercase__ : List[str] = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
lowercase__ : int = '''pretraining'''
if "vcr" in checkpoint_path:
lowercase__ : int = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
lowercase__ : Dict = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
lowercase__ : Dict = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
lowercase__ : Tuple = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
lowercase__ : Dict = {'''visual_embedding_dim''': 512}
lowercase__ : Union[str, Any] = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
lowercase__ : List[str] = {'''visual_embedding_dim''': 2048}
lowercase__ : int = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
lowercase__ : Union[str, Any] = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
lowercase__ : Optional[Any] = '''vqa'''
elif "nlvr" in checkpoint_path:
lowercase__ : Dict = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
lowercase__ : List[Any] = '''nlvr'''
lowercase__ : Optional[Any] = VisualBertConfig(**UpperCAmelCase )
# Load State Dict
lowercase__ : List[Any] = load_state_dict(UpperCAmelCase )
lowercase__ : Any = get_new_dict(UpperCAmelCase , UpperCAmelCase )
if model_type == "pretraining":
lowercase__ : List[str] = VisualBertForPreTraining(UpperCAmelCase )
elif model_type == "vqa":
lowercase__ : Optional[Any] = VisualBertForQuestionAnswering(UpperCAmelCase )
elif model_type == "nlvr":
lowercase__ : Any = VisualBertForVisualReasoning(UpperCAmelCase )
elif model_type == "multichoice":
lowercase__ : Tuple = VisualBertForMultipleChoice(UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
# Save Checkpoints
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__a: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
__a: Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 713 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=13 , __lowerCAmelCase=7 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=99 , __lowerCAmelCase=32 , __lowerCAmelCase=2 , __lowerCAmelCase=4 , __lowerCAmelCase=37 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=512 , __lowerCAmelCase=16 , __lowerCAmelCase=2 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=3 , __lowerCAmelCase=4 , __lowerCAmelCase=None , ) -> Dict:
lowercase__ : Union[str, Any] = parent
lowercase__ : Union[str, Any] = 13
lowercase__ : Dict = 7
lowercase__ : Optional[Any] = True
lowercase__ : List[Any] = True
lowercase__ : Optional[Any] = True
lowercase__ : Union[str, Any] = True
lowercase__ : Optional[Any] = 99
lowercase__ : Dict = 32
lowercase__ : Optional[int] = 2
lowercase__ : str = 4
lowercase__ : List[str] = 37
lowercase__ : Tuple = '''gelu'''
lowercase__ : Optional[int] = 0.1
lowercase__ : Optional[Any] = 0.1
lowercase__ : Dict = 512
lowercase__ : Optional[Any] = 16
lowercase__ : int = 2
lowercase__ : int = 0.0_2
lowercase__ : str = 3
lowercase__ : Optional[Any] = 4
lowercase__ : Optional[Any] = None
def _lowerCAmelCase( self ) -> str:
lowercase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : Tuple = None
if self.use_input_mask:
lowercase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Optional[Any] = None
if self.use_token_type_ids:
lowercase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ : Any = None
lowercase__ : Union[str, Any] = None
lowercase__ : Any = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : str = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__lowerCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
lowercase__ : Any = TFRoFormerModel(config=__lowerCAmelCase )
lowercase__ : Optional[int] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowercase__ : Union[str, Any] = [input_ids, input_mask]
lowercase__ : Union[str, Any] = model(__lowerCAmelCase )
lowercase__ : Union[str, Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
lowercase__ : Optional[Any] = True
lowercase__ : str = TFRoFormerForCausalLM(config=__lowerCAmelCase )
lowercase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : Dict = model(__lowerCAmelCase )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
lowercase__ : List[str] = TFRoFormerForMaskedLM(config=__lowerCAmelCase )
lowercase__ : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : int = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
lowercase__ : Optional[int] = self.num_labels
lowercase__ : Tuple = TFRoFormerForSequenceClassification(config=__lowerCAmelCase )
lowercase__ : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : Any = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
lowercase__ : Union[str, Any] = self.num_choices
lowercase__ : Dict = TFRoFormerForMultipleChoice(config=__lowerCAmelCase )
lowercase__ : List[str] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase__ : Optional[Any] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase__ : List[Any] = tf.tile(tf.expand_dims(__lowerCAmelCase , 1 ) , (1, self.num_choices, 1) )
lowercase__ : Dict = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowercase__ : str = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
lowercase__ : Optional[int] = self.num_labels
lowercase__ : List[str] = TFRoFormerForTokenClassification(config=__lowerCAmelCase )
lowercase__ : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : Tuple = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
lowercase__ : Dict = TFRoFormerForQuestionAnswering(config=__lowerCAmelCase )
lowercase__ : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : Tuple = model(__lowerCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = config_and_inputs
lowercase__ : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( a__ , a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
"feature-extraction": TFRoFormerModel,
"fill-mask": TFRoFormerForMaskedLM,
"question-answering": TFRoFormerForQuestionAnswering,
"text-classification": TFRoFormerForSequenceClassification,
"text-generation": TFRoFormerForCausalLM,
"token-classification": TFRoFormerForTokenClassification,
"zero-shot": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : List[str] = TFRoFormerModelTester(self )
lowercase__ : List[str] = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def _lowerCAmelCase( self ) -> Dict:
self.config_tester.run_common_tests()
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> str:
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> int:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> str:
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Dict:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Any:
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ : List[Any] = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(__lowerCAmelCase )
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : str = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
lowercase__ : Optional[int] = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase__ : str = model(__lowerCAmelCase )[0]
# TODO Replace vocab size
lowercase__ : str = 50000
lowercase__ : List[Any] = [1, 6, vocab_size]
self.assertEqual(output.shape , __lowerCAmelCase )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowercase__ : Union[str, Any] = tf.constant(
[
[
[-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
[-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
[-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 )
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1e-4
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Optional[Any] = tf.constant([[4, 10]] )
lowercase__ : Any = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
lowercase__ : Optional[int] = emba(input_ids.shape )
lowercase__ : Optional[Any] = tf.constant(
[[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]] )
tf.debugging.assert_near(__lowerCAmelCase , __lowerCAmelCase , atol=self.tolerance )
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : List[Any] = tf.constant(
[
[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
[0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
[0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
] )
lowercase__ : int = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
lowercase__ : List[Any] = emba.weight[:3, :5]
tf.debugging.assert_near(__lowerCAmelCase , __lowerCAmelCase , atol=self.tolerance )
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1e-4
def _lowerCAmelCase( self ) -> Tuple:
# 2,12,16,64
lowercase__ : Dict = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
lowercase__ : Tuple = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
lowercase__ : Dict = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
lowercase__ : Tuple = embed_positions([2, 16, 768] )[None, None, :, :]
lowercase__ , lowercase__ : Union[str, Any] = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase__ : int = tf.constant(
[
[0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
[-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
[-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
[-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
[0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
[3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
] )
lowercase__ : Tuple = tf.constant(
[
[0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
[0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
[1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
[2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
[-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
[-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __lowerCAmelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __lowerCAmelCase , atol=self.tolerance )
| 428 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
__magic_name__ : List[str] = logging.getLogger(__name__)
__magic_name__ : Optional[int] = 'pytorch_model.bin'
@dataclasses.dataclass
class __snake_case :
__a = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class __snake_case :
__a = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
__a = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''The name of the task to train on.'''} , )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class __snake_case :
__a = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
__a = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
__a = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
__a = dataclasses.field(
default=10 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
__a = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
__a = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
__a = dataclasses.field(
default=100 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
__a = dataclasses.field(
default=lowerCamelCase , metadata={'''help''': '''Random seed for initialization.'''} , )
def a_ ( lowercase__ :int, lowercase__ :Optional[int], lowercase__ :str, lowercase__ :Optional[int], lowercase__ :Optional[Any], lowercase__ :List[str] ):
__lowerCamelCase = datasets.concatenate_datasets([infer_input, infer_output], axis=1 )
if args.do_filter_by_confidence:
__lowerCamelCase = dataset.filter(lambda lowercase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
__lowerCamelCase = int(eval_result * len(lowercase__ ) )
print(lowercase__ )
__lowerCamelCase = dataset.sort("""probability""", reverse=lowercase__ )
__lowerCamelCase = dataset.select(range(lowercase__ ) )
__lowerCamelCase = dataset.remove_columns(["""label""", """probability"""] )
__lowerCamelCase = dataset.rename_column("""prediction""", """label""" )
__lowerCamelCase = dataset.map(lambda lowercase__ : {"label": idalabel[example["label"]]} )
__lowerCamelCase = dataset.shuffle(seed=args.seed )
__lowerCamelCase = os.path.join(lowercase__, f'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(lowercase__, index=lowercase__ )
else:
dataset.to_json(lowercase__ )
def a_ ( lowercase__ :Tuple, lowercase__ :Any, lowercase__ :Any, lowercase__ :List[str], **lowercase__ :List[str] ):
__lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO, )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
__lowerCamelCase = STModelArguments(model_name_or_path=lowercase__ )
__lowerCamelCase = STDataArguments(train_file=lowercase__, infer_file=lowercase__ )
__lowerCamelCase = STTrainingArguments(output_dir=lowercase__ )
__lowerCamelCase = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(lowercase__ ).items():
setattr(lowercase__, lowercase__, lowercase__ )
for key, value in kwargs.items():
if hasattr(lowercase__, lowercase__ ):
setattr(lowercase__, lowercase__, lowercase__ )
# Sanity checks
__lowerCamelCase = {}
__lowerCamelCase = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
__lowerCamelCase = args.train_file
__lowerCamelCase = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
__lowerCamelCase = args.eval_file
for key in data_files:
__lowerCamelCase = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
__lowerCamelCase = extension
else:
assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
__lowerCamelCase = f'{args.output_dir}/self-train_iter-{{}}'.format
__lowerCamelCase = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=lowercase__ )
os.makedirs(lowercase__, exist_ok=lowercase__ )
accelerator.wait_for_everyone()
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = 0
__lowerCamelCase = False
# Show the progress bar
__lowerCamelCase = tqdm(range(args.max_selftrain_iterations ), disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0, int(args.max_selftrain_iterations ) ):
__lowerCamelCase = data_dir_format(lowercase__ )
assert os.path.exists(lowercase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
__lowerCamelCase = os.path.join(lowercase__, """stage-1""" )
__lowerCamelCase = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(lowercase__, lowercase__ ):
arguments_dict.update({key: value} )
__lowerCamelCase = os.path.join(lowercase__, """best-checkpoint""", lowercase__ )
if os.path.exists(lowercase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""", lowercase__, lowercase__, )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""", lowercase__ )
finetune(**lowercase__ )
accelerator.wait_for_everyone()
assert os.path.exists(lowercase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""", lowercase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
__lowerCamelCase = os.path.join(lowercase__, """best-checkpoint""" )
__lowerCamelCase = os.path.join(lowercase__, """stage-2""" )
# Update arguments_dict
__lowerCamelCase = model_path
__lowerCamelCase = data_files["""train"""]
__lowerCamelCase = current_output_dir
__lowerCamelCase = os.path.join(lowercase__, """best-checkpoint""", lowercase__ )
if os.path.exists(lowercase__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""", lowercase__, lowercase__, )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""", lowercase__ )
finetune(**lowercase__ )
accelerator.wait_for_everyone()
assert os.path.exists(lowercase__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""", lowercase__ )
__lowerCamelCase = iteration
__lowerCamelCase = data_dir_format(iteration + 1 )
__lowerCamelCase = AutoConfig.from_pretrained(os.path.join(lowercase__, """best-checkpoint""" ) )
__lowerCamelCase = config.idalabel
__lowerCamelCase = os.path.join(lowercase__, """eval_results_best-checkpoint.json""" )
__lowerCamelCase = os.path.join(lowercase__, """test_results_best-checkpoint.json""" )
assert os.path.exists(lowercase__ )
with open(lowercase__, """r""" ) as f:
__lowerCamelCase = float(json.load(lowercase__ )[args.eval_metric] )
__lowerCamelCase = os.path.join(lowercase__, """infer_output_best-checkpoint.csv""" )
assert os.path.exists(lowercase__ )
# Loading the dataset from local csv or json files.
__lowerCamelCase = load_dataset(args.data_file_extension, data_files={"""data""": data_files["""infer"""]} )["""data"""]
__lowerCamelCase = load_dataset("""csv""", data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(lowercase__, exist_ok=lowercase__ )
shutil.copy(lowercase__, os.path.join(lowercase__, f'eval_results_iter-{iteration}.json' ) )
if os.path.exists(lowercase__ ):
shutil.copy(lowercase__, os.path.join(lowercase__, f'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(lowercase__, lowercase__, lowercase__, lowercase__, lowercase__, lowercase__ )
accelerator.wait_for_everyone()
__lowerCamelCase = os.path.join(lowercase__, f'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
__lowerCamelCase = eval_result
if best_iteration is None:
__lowerCamelCase = new_iteration
__lowerCamelCase = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
__lowerCamelCase = new_iteration
__lowerCamelCase = new_eval_result
__lowerCamelCase = 0
else:
if new_eval_result == best_eval_result:
__lowerCamelCase = new_iteration
__lowerCamelCase = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
__lowerCamelCase = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""", lowercase__ )
logger.info("""Best evaluation result: %s = %f""", args.eval_metric, lowercase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowercase__, f'eval_results_iter-{iteration}.json' ), os.path.join(lowercase__, """eval_results_best-iteration.json""" ), )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""", args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""", args.eval_metric, lowercase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowercase__, f'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ), os.path.join(lowercase__, """eval_results_best-iteration.json""" ), )
| 281 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Any = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ : Dict = ['GLPNFeatureExtractor']
__magic_name__ : Union[str, Any] = ['GLPNImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ : int = [
'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
'GLPNForDepthEstimation',
'GLPNLayer',
'GLPNModel',
'GLPNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
__magic_name__ : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 281 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase ="megatron-bert"
def __init__( self , snake_case=2_9_0_5_6 , snake_case=1_0_2_4 , snake_case=2_4 , snake_case=1_6 , snake_case=4_0_9_6 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=5_1_2 , snake_case=2 , snake_case=0.02 , snake_case=1E-1_2 , snake_case=0 , snake_case="absolute" , snake_case=True , **snake_case , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=snake_case , **snake_case)
_UpperCAmelCase : Optional[int] =vocab_size
_UpperCAmelCase : Any =hidden_size
_UpperCAmelCase : Dict =num_hidden_layers
_UpperCAmelCase : Any =num_attention_heads
_UpperCAmelCase : str =hidden_act
_UpperCAmelCase : Dict =intermediate_size
_UpperCAmelCase : Tuple =hidden_dropout_prob
_UpperCAmelCase : str =attention_probs_dropout_prob
_UpperCAmelCase : str =max_position_embeddings
_UpperCAmelCase : Optional[Any] =type_vocab_size
_UpperCAmelCase : Dict =initializer_range
_UpperCAmelCase : Optional[Any] =layer_norm_eps
_UpperCAmelCase : int =position_embedding_type
_UpperCAmelCase : Dict =use_cache
| 703 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowercase ='docs/source/en/_toctree.yml'
def lowerCamelCase__ ( __lowerCamelCase : List[Any] ):
'''simple docstring'''
_UpperCAmelCase : str =defaultdict(__lowerCamelCase )
_UpperCAmelCase : str =[]
_UpperCAmelCase : Optional[Any] =[]
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(__lowerCamelCase )
_UpperCAmelCase : List[Any] =new_doc_list
_UpperCAmelCase : Dict =[key for key, value in counts.items() if value > 1]
_UpperCAmelCase : Tuple =[]
for duplicate_key in duplicates:
_UpperCAmelCase : List[Any] =list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(__lowerCamelCase ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
_UpperCAmelCase : Any =sorted(__lowerCamelCase , key=lambda __lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__lowerCamelCase ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(__lowerCamelCase )
# Sort
return overview_doc
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any]=False ):
'''simple docstring'''
with open(__lowerCamelCase , encoding='utf-8' ) as f:
_UpperCAmelCase : Any =yaml.safe_load(f.read() )
# Get to the API doc
_UpperCAmelCase : Tuple =0
while content[api_idx]["title"] != "API":
api_idx += 1
_UpperCAmelCase : Optional[int] =content[api_idx]['sections']
# Then to the model doc
_UpperCAmelCase : Dict =0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_UpperCAmelCase : Any =api_doc[scheduler_idx]['sections']
_UpperCAmelCase : Any =clean_doc_toc(__lowerCamelCase )
_UpperCAmelCase : Dict =False
if new_scheduler_doc != scheduler_doc:
_UpperCAmelCase : Optional[Any] =True
if overwrite:
_UpperCAmelCase : List[Any] =new_scheduler_doc
if diff:
if overwrite:
_UpperCAmelCase : List[Any] =api_doc
with open(__lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__lowerCamelCase , allow_unicode=__lowerCamelCase ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any]=False ):
'''simple docstring'''
with open(__lowerCamelCase , encoding='utf-8' ) as f:
_UpperCAmelCase : Union[str, Any] =yaml.safe_load(f.read() )
# Get to the API doc
_UpperCAmelCase : Optional[int] =0
while content[api_idx]["title"] != "API":
api_idx += 1
_UpperCAmelCase : Dict =content[api_idx]['sections']
# Then to the model doc
_UpperCAmelCase : Optional[Any] =0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_UpperCAmelCase : Tuple =False
_UpperCAmelCase : int =api_doc[pipeline_idx]['sections']
_UpperCAmelCase : List[Any] =[]
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_UpperCAmelCase : Dict =pipeline_doc['section']
_UpperCAmelCase : Tuple =clean_doc_toc(__lowerCamelCase )
if overwrite:
_UpperCAmelCase : List[str] =new_sub_pipeline_doc
new_pipeline_docs.append(__lowerCamelCase )
# sort overall pipeline doc
_UpperCAmelCase : Union[str, Any] =clean_doc_toc(__lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
_UpperCAmelCase : Optional[Any] =True
if overwrite:
_UpperCAmelCase : Optional[int] =new_pipeline_docs
if diff:
if overwrite:
_UpperCAmelCase : Optional[Any] =api_doc
with open(__lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__lowerCamelCase , allow_unicode=__lowerCamelCase ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
lowercase =argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowercase =parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 331 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwiftFormerForImageClassification",
"SwiftFormerModel",
"SwiftFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 66 |
'''simple docstring'''
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 638 | 0 |
def __lowerCAmelCase ( UpperCamelCase = 1000000 ) -> int:
lowerCAmelCase__ : Optional[int] = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCamelCase ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 470 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[Any]:
lowerCAmelCase__ : List[str] = 10
lowerCAmelCase__ : Optional[Any] = datasets.Features(
{
'''tokens''': datasets.Sequence(datasets.Value('''string''' ) ),
'''labels''': datasets.Sequence(datasets.ClassLabel(names=['''negative''', '''positive'''] ) ),
'''answers''': datasets.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
'''id''': datasets.Value('''int64''' ),
} )
lowerCAmelCase__ : List[Any] = datasets.Dataset.from_dict(
{
'''tokens''': [['''foo'''] * 5] * n,
'''labels''': [[1] * 5] * n,
'''answers''': [{'''answer_start''': [97], '''text''': ['''1976''']}] * 10,
'''id''': list(range(UpperCamelCase ) ),
} , features=UpperCamelCase , )
return dataset
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : str = str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
dataset.map(cache_file_name=UpperCamelCase )
return filename
# FILE_CONTENT + files
lowerCAmelCase_ = """\
Text data.
Second line of data."""
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt'''
lowerCAmelCase__ : int = FILE_CONTENT
with open(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase )
return filename
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
import bza
lowerCAmelCase__ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.bz2'''
lowerCAmelCase__ : Optional[Any] = bytes(UpperCamelCase , '''utf-8''' )
with bza.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
import gzip
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''file.txt.gz''' )
lowerCAmelCase__ : Any = bytes(UpperCamelCase , '''utf-8''' )
with gzip.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Tuple:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.lz4'''
lowerCAmelCase__ : Tuple = bytes(UpperCamelCase , '''utf-8''' )
with lza.frame.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase__ : int = tmp_path_factory.mktemp('''data''' ) / '''file.txt.7z'''
with pyazr.SevenZipFile(UpperCamelCase , '''w''' ) as archive:
archive.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
import tarfile
lowerCAmelCase__ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
import lzma
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.xz'''
lowerCAmelCase__ : str = bytes(UpperCamelCase , '''utf-8''' )
with lzma.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Any:
import zipfile
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[str]:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zst'''
lowerCAmelCase__ : int = bytes(UpperCamelCase , '''utf-8''' )
with zstd.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''file.xml'''
lowerCAmelCase__ : Tuple = textwrap.dedent(
'''\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>''' )
with open(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase )
return filename
lowerCAmelCase_ = [
{"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
lowerCAmelCase_ = [
{"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
{"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
lowerCAmelCase_ = {
"""col_1""": ["""0""", """1""", """2""", """3"""],
"""col_2""": [0, 1, 2, 3],
"""col_3""": [0.0, 1.0, 2.0, 3.0],
}
lowerCAmelCase_ = [
{"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
{"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]
lowerCAmelCase_ = [
{"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Tuple:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : str = datasets.Dataset.from_dict(UpperCamelCase )
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
dataset.map(cache_file_name=UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
with contextlib.closing(sqlitea.connect(UpperCamelCase ) ) as con:
lowerCAmelCase__ : int = con.cursor()
cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)''' )
for item in DATA:
cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)''' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> int:
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
with open(UpperCamelCase , '''w''' , newline='''''' ) as f:
lowerCAmelCase__ : List[str] = csv.DictWriter(UpperCamelCase , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
with open(UpperCamelCase , '''w''' , newline='''''' ) as f:
lowerCAmelCase__ : Tuple = csv.DictWriter(UpperCamelCase , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[str]:
import bza
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.bz2'''
with open(UpperCamelCase , '''rb''' ) as f:
lowerCAmelCase__ : List[str] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(csv_path.replace('''.csv''' , '''.CSV''' ) ) )
f.write(UpperCamelCase , arcname=os.path.basename(csva_path.replace('''.csv''' , '''.CSV''' ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> str:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : str = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
lowerCAmelCase__ : List[Any] = pa.schema(
{
'''col_1''': pa.string(),
'''col_2''': pa.intaa(),
'''col_3''': pa.floataa(),
} )
with open(UpperCamelCase , '''wb''' ) as f:
lowerCAmelCase__ : str = pq.ParquetWriter(UpperCamelCase , schema=UpperCamelCase )
lowerCAmelCase__ : List[Any] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase ) )] for k in DATA[0]} , schema=UpperCamelCase )
writer.write_table(UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : List[Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
lowerCAmelCase__ : List[str] = {'''data''': DATA}
with open(UpperCamelCase , '''w''' ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Tuple = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
lowerCAmelCase__ : Optional[Any] = {'''data''': DATA_DICT_OF_LISTS}
with open(UpperCamelCase , '''w''' ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> int:
lowerCAmelCase__ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> str:
lowerCAmelCase__ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_312.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA_312:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset-str.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA_STR:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> int:
import gzip
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt.gz''' )
with open(UpperCamelCase , '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase , '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
import gzip
lowerCAmelCase__ : int = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.gz''' )
with open(UpperCamelCase , '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase , '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''nested''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCAmelCase__ : Optional[Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.join('''nested''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Dict = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : Any = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : int = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : int = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.txt''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Optional[Any] = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.abc'''
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.text.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Any = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.text.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.ext.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename('''unsupported.ext''' ) )
f.write(UpperCamelCase , arcname=os.path.basename('''unsupported_2.ext''' ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : List[Any] = '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
lowerCAmelCase__ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_with_unicode_new_lines.txt''' )
with open(UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[int]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_image_rgb.jpg''' )
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[Any]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_audio_44100.wav''' )
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset.img.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ).replace('''.jpg''' , '''2.jpg''' ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : Optional[Any] = tmp_path_factory.mktemp('''data_dir''' )
(data_dir / "subdir").mkdir()
with open(data_dir / '''subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden file
with open(data_dir / '''subdir''' / '''.test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '''.subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''.subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
return data_dir
| 470 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = AudioLDMPipeline
lowerCAmelCase_ : List[str] = TEXT_TO_AUDIO_PARAMS
lowerCAmelCase_ : List[Any] = TEXT_TO_AUDIO_BATCH_PARAMS
lowerCAmelCase_ : Any = frozenset(
[
"""num_inference_steps""",
"""num_waveforms_per_prompt""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_UpperCAmelCase , )
UpperCAmelCase__ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0 )
UpperCAmelCase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCAmelCase__ = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , projection_dim=32 , )
UpperCAmelCase__ = ClapTextModelWithProjection(_UpperCAmelCase )
UpperCAmelCase__ = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCAmelCase__ = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_60_00 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_UpperCAmelCase , )
UpperCAmelCase__ = SpeechTaHifiGan(_UpperCAmelCase )
UpperCAmelCase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple=0 ):
"""simple docstring"""
if str(_UpperCAmelCase ).startswith("""mps""" ):
UpperCAmelCase__ = torch.manual_seed(_UpperCAmelCase )
else:
UpperCAmelCase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
UpperCAmelCase__ = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) == 2_56
UpperCAmelCase__ = audio[:10]
UpperCAmelCase__ = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = 3 * [inputs["""prompt"""]]
# forward
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = 3 * [inputs.pop("""prompt""" )]
UpperCAmelCase__ = audioldm_pipe.tokenizer(
_UpperCAmelCase , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors="""pt""" , )
UpperCAmelCase__ = text_inputs["""input_ids"""].to(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.text_encoder(
_UpperCAmelCase , )
UpperCAmelCase__ = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCAmelCase__ = F.normalize(_UpperCAmelCase , dim=-1 )
UpperCAmelCase__ = prompt_embeds
# forward
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = 3 * ["""this is a negative prompt"""]
UpperCAmelCase__ = negative_prompt
UpperCAmelCase__ = 3 * [inputs["""prompt"""]]
# forward
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = 3 * [inputs.pop("""prompt""" )]
UpperCAmelCase__ = []
for p in [prompt, negative_prompt]:
UpperCAmelCase__ = audioldm_pipe.tokenizer(
_UpperCAmelCase , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors="""pt""" , )
UpperCAmelCase__ = text_inputs["""input_ids"""].to(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.text_encoder(
_UpperCAmelCase , )
UpperCAmelCase__ = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCAmelCase__ = F.normalize(_UpperCAmelCase , dim=-1 )
embeds.append(_UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = embeds
# forward
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = """egg cracking"""
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase , negative_prompt=_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) == 2_56
UpperCAmelCase__ = audio[:10]
UpperCAmelCase__ = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCAmelCase__ = audioldm_pipe(_UpperCAmelCase , num_inference_steps=2 ).audios
assert audios.shape == (1, 2_56)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCAmelCase__ = 2
UpperCAmelCase__ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 2_56)
# test num_waveforms_per_prompt for single prompt
UpperCAmelCase__ = 2
UpperCAmelCase__ = audioldm_pipe(_UpperCAmelCase , num_inference_steps=2 , num_waveforms_per_prompt=_UpperCAmelCase ).audios
assert audios.shape == (num_waveforms_per_prompt, 2_56)
# test num_waveforms_per_prompt for batch of prompts
UpperCAmelCase__ = 2
UpperCAmelCase__ = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_UpperCAmelCase ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.vocoder.config.sampling_rate
UpperCAmelCase__ = self.get_dummy_inputs(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe(audio_length_in_s=0.016 , **_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) / vocoder_sampling_rate == 0.016
UpperCAmelCase__ = audioldm_pipe(audio_length_in_s=0.032 , **_UpperCAmelCase )
UpperCAmelCase__ = output.audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) / vocoder_sampling_rate == 0.032
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = AudioLDMPipeline(**_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = ["""hey"""]
UpperCAmelCase__ = audioldm_pipe(_UpperCAmelCase , num_inference_steps=1 )
UpperCAmelCase__ = output.audios.shape
assert audio_shape == (1, 2_56)
UpperCAmelCase__ = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCAmelCase__ = SpeechTaHifiGan(_UpperCAmelCase ).to(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe(_UpperCAmelCase , num_inference_steps=1 )
UpperCAmelCase__ = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 2_56)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_UpperCAmelCase )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_UpperCAmelCase )
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : str="cpu" , _UpperCAmelCase : List[str]=torch.floataa , _UpperCAmelCase : Optional[int]=0 ):
"""simple docstring"""
UpperCAmelCase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
UpperCAmelCase__ = np.random.RandomState(_UpperCAmelCase ).standard_normal((1, 8, 1_28, 16) )
UpperCAmelCase__ = torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
UpperCAmelCase__ = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_inputs(_UpperCAmelCase )
UpperCAmelCase__ = 25
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase ).audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) == 8_19_20
UpperCAmelCase__ = audio[7_72_30:7_72_40]
UpperCAmelCase__ = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCAmelCase__ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCAmelCase__ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCAmelCase__ = audioldm_pipe.to(_UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase__ = self.get_inputs(_UpperCAmelCase )
UpperCAmelCase__ = audioldm_pipe(**_UpperCAmelCase ).audios[0]
assert audio.ndim == 1
assert len(_UpperCAmelCase ) == 8_19_20
UpperCAmelCase__ = audio[2_77_80:2_77_90]
UpperCAmelCase__ = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCAmelCase__ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 603 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=13 , _UpperCAmelCase : Optional[Any]=7 , _UpperCAmelCase : int=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : Optional[Any]=99 , _UpperCAmelCase : Tuple=32 , _UpperCAmelCase : Optional[int]=5 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : Dict=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : Union[str, Any]=1_28 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : List[Any]=16 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : Optional[Any]=3 , _UpperCAmelCase : int=4 , _UpperCAmelCase : List[Any]=None , ):
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_input_mask
UpperCAmelCase__ = use_token_type_ids
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = type_sequence_label_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = num_labels
UpperCAmelCase__ = num_choices
UpperCAmelCase__ = scope
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return NezhaConfig(
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=_UpperCAmelCase , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = self.prepare_config_and_inputs()
UpperCAmelCase__ = True
UpperCAmelCase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Any , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
UpperCAmelCase__ = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
UpperCAmelCase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , ):
"""simple docstring"""
UpperCAmelCase__ = True
UpperCAmelCase__ = NezhaModel(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaForNextSentencePrediction(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaForPreTraining(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , next_sentence_label=_UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any ):
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = NezhaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = NezhaForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
"""simple docstring"""
UpperCAmelCase__ = self.num_choices
UpperCAmelCase__ = NezhaForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Any = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ : Tuple = (
{
"""feature-extraction""": NezhaModel,
"""fill-mask""": NezhaForMaskedLM,
"""question-answering""": NezhaForQuestionAnswering,
"""text-classification""": NezhaForSequenceClassification,
"""token-classification""": NezhaForTokenClassification,
"""zero-shot""": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ : Union[str, Any] = True
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int]=False ):
"""simple docstring"""
UpperCAmelCase__ = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class in get_values(_UpperCAmelCase ):
UpperCAmelCase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_UpperCAmelCase )
UpperCAmelCase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = NezhaModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCAmelCase__ = None
self.model_tester.create_and_check_model_as_decoder(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = NezhaModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(config=_UpperCAmelCase )
UpperCAmelCase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = torch.jit.trace(
_UpperCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """bert.pt""" ) )
UpperCAmelCase__ = torch.jit.load(os.path.join(_UpperCAmelCase , """bert.pt""" ) , map_location=_UpperCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase ) , inputs_dict["""attention_mask"""].to(_UpperCAmelCase ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
UpperCAmelCase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
UpperCAmelCase__ = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , _UpperCAmelCase )
UpperCAmelCase__ = torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
UpperCAmelCase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
UpperCAmelCase__ = torch.Size((1, 6, 2_11_28) )
self.assertEqual(output.shape , _UpperCAmelCase )
UpperCAmelCase__ = torch.tensor(
[[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
| 603 | 1 |
from timeit import timeit
def __snake_case ( _UpperCamelCase ) -> int:
if number < 0:
raise ValueError('''the value of input must not be negative''' )
_a = 0
while number:
number &= number - 1
result += 1
return result
def __snake_case ( _UpperCamelCase ) -> int:
if number < 0:
raise ValueError('''the value of input must not be negative''' )
_a = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def __snake_case ( ) -> None:
def do_benchmark(_UpperCamelCase ) -> None:
_a = '''import __main__ as z'''
print(f"Benchmark when {number = }:" )
print(f"{get_set_bits_count_using_modulo_operator(_UpperCamelCase ) = }" )
_a = timeit('''z.get_set_bits_count_using_modulo_operator(25)''' , setup=_UpperCamelCase )
print(f"timeit() runs in {timing} seconds" )
print(f"{get_set_bits_count_using_brian_kernighans_algorithm(_UpperCamelCase ) = }" )
_a = timeit(
'''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''' , setup=_UpperCamelCase , )
print(f"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(_UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 346 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase :Tuple = data_utils.TransfoXLTokenizer
lowerCamelCase :Tuple = data_utils.TransfoXLCorpus
lowerCamelCase :List[str] = data_utils
lowerCamelCase :str = data_utils
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(_UpperCamelCase , '''rb''' ) as fp:
_a = pickle.load(_UpperCamelCase , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
_a = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(f"Save vocabulary to {pytorch_vocab_dump_path}" )
_a = corpus.vocab.__dict__
torch.save(_UpperCamelCase , _UpperCamelCase )
_a = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , _UpperCamelCase )
_a = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(f"Save dataset to {pytorch_dataset_dump_path}" )
torch.save(_UpperCamelCase , _UpperCamelCase )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
_a = os.path.abspath(_UpperCamelCase )
_a = os.path.abspath(_UpperCamelCase )
print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}." )
# Initialise PyTorch model
if transfo_xl_config_file == "":
_a = TransfoXLConfig()
else:
_a = TransfoXLConfig.from_json_file(_UpperCamelCase )
print(f"Building PyTorch model from configuration: {config}" )
_a = TransfoXLLMHeadModel(_UpperCamelCase )
_a = load_tf_weights_in_transfo_xl(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save pytorch-model
_a = os.path.join(_UpperCamelCase , _UpperCamelCase )
_a = os.path.join(_UpperCamelCase , _UpperCamelCase )
print(f"Save PyTorch model to {os.path.abspath(_UpperCamelCase )}" )
torch.save(model.state_dict() , _UpperCamelCase )
print(f"Save configuration file to {os.path.abspath(_UpperCamelCase )}" )
with open(_UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--tf_checkpoint_path',
default='',
type=str,
help='An optional path to a TensorFlow checkpoint path to be converted.',
)
parser.add_argument(
'--transfo_xl_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--transfo_xl_dataset_file',
default='',
type=str,
help='An optional dataset file to be converted in a vocabulary.',
)
lowerCamelCase :Optional[Any] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 346 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Dict = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.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",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
lowerCAmelCase_ : Optional[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def _lowerCamelCase (__lowerCamelCase : Optional[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : str ) -> List[Any]:
for attribute in key.split("." ):
a__ = getattr(_UpperCamelCase , _UpperCamelCase )
if weight_type is not None:
a__ = getattr(_UpperCamelCase , _UpperCamelCase ).shape
else:
a__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
a__ = value
elif weight_type == "weight_g":
a__ = value
elif weight_type == "weight_v":
a__ = value
elif weight_type == "bias":
a__ = value
else:
a__ = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowerCamelCase (__lowerCamelCase : int , __lowerCamelCase : List[str] ) -> Any:
a__ = []
a__ = fairseq_model.state_dict()
a__ = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
a__ = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , hf_model.config.feat_extract_norm == "group" , )
a__ = True
else:
for key, mapped_key in MAPPING.items():
a__ = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split("." )[:-1] ) != key):
# special case since naming is very similar
continue
a__ = True
if "*" in mapped_key:
a__ = name.split(_UpperCamelCase )[0].split("." )[-2]
a__ = mapped_key.replace("*" , _UpperCamelCase )
if "weight_g" in name:
a__ = "weight_g"
elif "weight_v" in name:
a__ = "weight_v"
elif "bias" in name:
a__ = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
a__ = "weight"
else:
a__ = None
set_recursively(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCamelCase (__lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : List[str] ) -> Optional[int]:
a__ = full_name.split("conv_layers." )[-1]
a__ = name.split("." )
a__ = int(items[0] )
a__ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
a__ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
a__ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.''' )
a__ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' )
a__ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def _lowerCamelCase (__lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : int=None , __lowerCamelCase : Any=None , __lowerCamelCase : Dict=True ) -> int:
if config_path is not None:
a__ = UniSpeechSatConfig.from_pretrained(_UpperCamelCase )
else:
a__ = UniSpeechSatConfig()
a__ = ""
if is_finetuned:
a__ = UniSpeechSatForCTC(_UpperCamelCase )
else:
a__ = UniSpeechSatForPreTraining(_UpperCamelCase )
a__ , a__ , a__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
a__ = model[0].eval()
recursively_load_weights(_UpperCamelCase , _UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ : Any = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
lowerCAmelCase_ : Optional[Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 489 |
from __future__ import annotations
def a__ ( _UpperCamelCase : list[float] ):
if len(_UpperCamelCase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be greater than 0''' )
__lowerCamelCase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class __lowercase ( A ):
'''simple docstring'''
_A : int = '''data2vec-audio'''
def __init__( self : str , _a : List[Any]=32 , _a : str=768 , _a : Any=12 , _a : int=12 , _a : Dict=3_072 , _a : Tuple="gelu" , _a : Optional[Any]=0.1 , _a : str=0.1 , _a : Optional[Any]=0.1 , _a : int=0.0 , _a : Dict=0.1 , _a : Dict=0.1 , _a : Union[str, Any]=0.02 , _a : Any=1E-5 , _a : Tuple="gelu" , _a : str=(512, 512, 512, 512, 512, 512, 512) , _a : Any=(5, 2, 2, 2, 2, 2, 2) , _a : int=(10, 3, 3, 3, 3, 2, 2) , _a : Tuple=False , _a : Optional[Any]=16 , _a : Optional[int]=19 , _a : Dict=5 , _a : List[str]=0.05 , _a : Dict=10 , _a : Dict=2 , _a : List[Any]=0.0 , _a : Optional[Any]=10 , _a : Optional[Any]=0 , _a : Optional[Any]="sum" , _a : int=False , _a : Union[str, Any]=False , _a : Union[str, Any]=256 , _a : Union[str, Any]=(512, 512, 512, 512, 1_500) , _a : List[str]=(5, 3, 3, 1, 1) , _a : Optional[int]=(1, 2, 3, 1, 1) , _a : Tuple=512 , _a : Optional[Any]=0 , _a : Optional[int]=1 , _a : str=2 , _a : int=False , _a : Tuple=3 , _a : Union[str, Any]=2 , _a : List[Any]=3 , _a : str=None , **_a : Optional[int] , ):
super().__init__(**_a , pad_token_id=_a , bos_token_id=_a , eos_token_id=_a )
UpperCamelCase__ = hidden_size
UpperCamelCase__ = feat_extract_activation
UpperCamelCase__ = list(_a )
UpperCamelCase__ = list(_a )
UpperCamelCase__ = list(_a )
UpperCamelCase__ = conv_bias
UpperCamelCase__ = num_conv_pos_embeddings
UpperCamelCase__ = num_conv_pos_embedding_groups
UpperCamelCase__ = conv_pos_kernel_size
UpperCamelCase__ = len(self.conv_dim )
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_dropout
UpperCamelCase__ = attention_dropout
UpperCamelCase__ = activation_dropout
UpperCamelCase__ = feat_proj_dropout
UpperCamelCase__ = final_dropout
UpperCamelCase__ = layerdrop
UpperCamelCase__ = layer_norm_eps
UpperCamelCase__ = initializer_range
UpperCamelCase__ = vocab_size
UpperCamelCase__ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCamelCase__ = mask_time_prob
UpperCamelCase__ = mask_time_length
UpperCamelCase__ = mask_time_min_masks
UpperCamelCase__ = mask_feature_prob
UpperCamelCase__ = mask_feature_length
UpperCamelCase__ = mask_feature_min_masks
# ctc loss
UpperCamelCase__ = ctc_loss_reduction
UpperCamelCase__ = ctc_zero_infinity
# adapter
UpperCamelCase__ = add_adapter
UpperCamelCase__ = adapter_kernel_size
UpperCamelCase__ = adapter_stride
UpperCamelCase__ = num_adapter_layers
UpperCamelCase__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCamelCase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCamelCase__ = list(_a )
UpperCamelCase__ = list(_a )
UpperCamelCase__ = list(_a )
UpperCamelCase__ = xvector_output_dim
@property
def A_ ( self : Tuple ):
return math.prod(self.conv_stride )
| 591 | def lowerCamelCase_ ( UpperCamelCase__ : int = 100 ):
'''simple docstring'''
UpperCamelCase__ = (n * (n + 1) // 2) ** 2
UpperCamelCase__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 591 | 1 |
"""simple docstring"""
import re
def A_ (__a ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ (__a ):
'''simple docstring'''
A_ = split_input(str_ )
return "".join(
["".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def A_ (__a , __a , __a ):
'''simple docstring'''
try:
A_ = split_input(__a )
if upper:
A_ = "".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
A_ = "".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def A_ (__a ):
'''simple docstring'''
return to_simple_case(__a )
def A_ (__a ):
'''simple docstring'''
try:
A_ = to_simple_case(__a )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def A_ (__a , __a ):
'''simple docstring'''
return to_complex_case(__a , __a , "_" )
def A_ (__a , __a ):
'''simple docstring'''
return to_complex_case(__a , __a , "-" )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 115 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple docstring'''
with open(__a ) as metadata_file:
A_ = json.load(__a )
A_ = LukeConfig(use_entity_aware_attention=__a , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
A_ = torch.load(__a , map_location="cpu" )
# Load the entity vocab file
A_ = load_entity_vocab(__a )
A_ = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
A_ = AddedToken("<ent>" , lstrip=__a , rstrip=__a )
A_ = AddedToken("<ent2>" , lstrip=__a , rstrip=__a )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a , LukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(__a , __a )
A_ = LukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
A_ = state_dict["embeddings.word_embeddings.weight"]
A_ = word_emb[tokenizer.convert_tokens_to_ids(["@"] )[0]].unsqueeze(0 )
A_ = word_emb[tokenizer.convert_tokens_to_ids(["#"] )[0]].unsqueeze(0 )
A_ = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
A_ = f'encoder.layer.{layer_index}.attention.self.'
A_ = state_dict[prefix + matrix_name]
A_ = state_dict[prefix + matrix_name]
A_ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
A_ = state_dict["entity_embeddings.entity_embeddings.weight"]
A_ = entity_emb[entity_vocab["[MASK]"]]
A_ = LukeModel(config=__a ).eval()
A_ , A_ = model.load_state_dict(__a , strict=__a )
if not (len(__a ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'Missing keys {", ".join(__a )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith("entity_predictions" ) or key.startswith("lm_head" ) for key in unexpected_keys )):
raise ValueError(
"Unexpected keys"
f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
A_ = LukeTokenizer.from_pretrained(__a , task="entity_classification" )
A_ = (
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
" new world number one avoid a humiliating second- round exit at Wimbledon ."
)
A_ = (39, 42)
A_ = tokenizer(__a , entity_spans=[span] , add_prefix_space=__a , return_tensors="pt" )
A_ = model(**__a )
# Verify word hidden states
if model_size == "large":
A_ = torch.Size((1, 42, 1024) )
A_ = torch.tensor(
[[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
else: # base
A_ = torch.Size((1, 42, 768) )
A_ = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __a , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
A_ = torch.Size((1, 1, 1024) )
A_ = torch.tensor([[0.0466, -0.0106, -0.0179]] )
else: # base
A_ = torch.Size((1, 1, 768) )
A_ = torch.tensor([[0.1457, 0.1044, 0.0174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __a , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(__a ) )
model.save_pretrained(__a )
def A_ (__a ):
'''simple docstring'''
A_ = {}
with open(__a , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(__a ):
A_ , A_ = line.rstrip().split("\t" )
A_ = index
return entity_vocab
if __name__ == "__main__":
UpperCamelCase_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
UpperCamelCase_ : List[str] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 115 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCamelCase = '''scheduler_config.json'''
class lowerCamelCase__ ( __A ):
lowerCamelCase_ : Union[str, Any] = 1
lowerCamelCase_ : Dict = 2
lowerCamelCase_ : List[Any] = 3
lowerCamelCase_ : Any = 4
lowerCamelCase_ : int = 5
@dataclass
class lowerCamelCase__ ( __A ):
lowerCamelCase_ : jnp.ndarray
class lowerCamelCase__ :
lowerCamelCase_ : Union[str, Any] = SCHEDULER_CONFIG_NAME
lowerCamelCase_ : List[Any] = ["""dtype"""]
lowerCamelCase_ : Optional[Any] = []
lowerCamelCase_ : int = True
@classmethod
def UpperCAmelCase_ (cls : str , _snake_case : int = None , _snake_case : List[Any] = None , _snake_case : Optional[int]=False , **_snake_case : List[Any] , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCamelCase__ , subfolder=UpperCamelCase__ , return_unused_kwargs=UpperCamelCase__ , **UpperCamelCase__ , )
lowerCamelCase_ : Optional[Any] = cls.from_config(UpperCamelCase__ , return_unused_kwargs=UpperCamelCase__ , **UpperCamelCase__ )
if hasattr(UpperCamelCase__ , 'create_state' ) and getattr(UpperCamelCase__ , 'has_state' , UpperCamelCase__ ):
lowerCamelCase_ : Optional[Any] = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase_ (self : Tuple , _snake_case : List[Any] , _snake_case : int = False , **_snake_case : Any ) -> Dict:
"""simple docstring"""
self.save_config(save_directory=UpperCamelCase__ , push_to_hub=UpperCamelCase__ , **UpperCamelCase__ )
@property
def UpperCAmelCase_ (self : List[str] ) -> List[Any]:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def UpperCAmelCase_ (cls : Union[str, Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ : List[str] = list(set([cls.__name__] + cls._compatibles ) )
lowerCamelCase_ : Optional[Any] = importlib.import_module(__name__.split('.' )[0] )
lowerCamelCase_ : str = [
getattr(UpperCamelCase__ , UpperCamelCase__ ) for c in compatible_classes_str if hasattr(UpperCamelCase__ , UpperCamelCase__ )
]
return compatible_classes
def _a ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
assert len(lowerCAmelCase__ ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__ ) - x.ndim) ) , lowerCAmelCase__ )
def _a ( lowerCamelCase__ , lowerCamelCase__=0.999 , lowerCamelCase__=jnp.floataa ) -> Any:
def alpha_bar(lowerCamelCase__ ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
lowerCamelCase_ : str = []
for i in range(lowerCAmelCase__ ):
lowerCamelCase_ : List[Any] = i / num_diffusion_timesteps
lowerCamelCase_ : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(lowerCAmelCase__ ) / alpha_bar(lowerCAmelCase__ ) , lowerCAmelCase__ ) )
return jnp.array(lowerCAmelCase__ , dtype=lowerCAmelCase__ )
@flax.struct.dataclass
class lowerCamelCase__ :
lowerCamelCase_ : jnp.ndarray
lowerCamelCase_ : jnp.ndarray
lowerCamelCase_ : jnp.ndarray
@classmethod
def UpperCAmelCase_ (cls : List[str] , _snake_case : Tuple ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ : List[Any] = scheduler.config
if config.trained_betas is not None:
lowerCamelCase_ : Dict = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
lowerCamelCase_ : str = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase_ : Optional[int] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase_ : str = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
lowerCamelCase_ : Dict = 1.0 - betas
lowerCamelCase_ : Optional[Any] = jnp.cumprod(UpperCamelCase__ , axis=0 )
return cls(
alphas=UpperCamelCase__ , betas=UpperCamelCase__ , alphas_cumprod=UpperCamelCase__ , )
def _a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
lowerCamelCase_ : Any = state.alphas_cumprod
lowerCamelCase_ : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
lowerCamelCase_ : List[str] = sqrt_alpha_prod.flatten()
lowerCamelCase_ : Dict = broadcast_to_shape_from_left(lowerCAmelCase__ , original_samples.shape )
lowerCamelCase_ : str = (1 - alphas_cumprod[timesteps]) ** 0.5
lowerCamelCase_ : Union[str, Any] = sqrt_one_minus_alpha_prod.flatten()
lowerCamelCase_ : Optional[int] = broadcast_to_shape_from_left(lowerCAmelCase__ , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
lowerCamelCase_ : Any = get_sqrt_alpha_prod(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
lowerCamelCase_ : Dict = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
lowerCamelCase_ : str = get_sqrt_alpha_prod(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
lowerCamelCase_ : Dict = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 709 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowerCamelCase__ ( UpperCAmelCase ):
lowerCamelCase_ : Tuple = 'umt5'
lowerCamelCase_ : Any = ['past_key_values']
def __init__(self : Tuple , _snake_case : Optional[int]=25_0112 , _snake_case : str=512 , _snake_case : Optional[int]=64 , _snake_case : Dict=1024 , _snake_case : Tuple=8 , _snake_case : Dict=None , _snake_case : Dict=6 , _snake_case : int=32 , _snake_case : Optional[int]=128 , _snake_case : Tuple=0.1 , _snake_case : List[Any]=1e-6 , _snake_case : List[Any]=1.0 , _snake_case : Optional[int]="gated-gelu" , _snake_case : Tuple=True , _snake_case : Tuple=True , _snake_case : List[str]="T5Tokenizer" , _snake_case : int=True , _snake_case : Any=0 , _snake_case : Optional[Any]=1 , _snake_case : str=0 , **_snake_case : Optional[int] , ) -> int:
"""simple docstring"""
super().__init__(
is_encoder_decoder=_snake_case , tokenizer_class=_snake_case , tie_word_embeddings=_snake_case , pad_token_id=_snake_case , eos_token_id=_snake_case , decoder_start_token_id=_snake_case , **_snake_case , )
lowerCamelCase_ : int = vocab_size
lowerCamelCase_ : List[str] = d_model
lowerCamelCase_ : Tuple = d_kv
lowerCamelCase_ : Tuple = d_ff
lowerCamelCase_ : List[Any] = num_layers
lowerCamelCase_ : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCamelCase_ : int = num_heads
lowerCamelCase_ : str = relative_attention_num_buckets
lowerCamelCase_ : List[Any] = relative_attention_max_distance
lowerCamelCase_ : str = dropout_rate
lowerCamelCase_ : List[str] = layer_norm_epsilon
lowerCamelCase_ : Optional[Any] = initializer_factor
lowerCamelCase_ : Optional[Any] = feed_forward_proj
lowerCamelCase_ : List[Any] = use_cache
lowerCamelCase_ : int = self.feed_forward_proj.split('-' )
lowerCamelCase_ : Optional[int] = act_info[-1]
lowerCamelCase_ : int = act_info[0] == 'gated'
if len(_snake_case ) > 1 and act_info[0] != "gated" or len(_snake_case ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
if feed_forward_proj == "gated-gelu":
lowerCamelCase_ : Dict = 'gelu_new'
@property
def UpperCAmelCase_ (self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return self.d_model
@property
def UpperCAmelCase_ (self : Optional[int] ) -> int:
"""simple docstring"""
return self.num_heads
@property
def UpperCAmelCase_ (self : int ) -> str:
"""simple docstring"""
return self.num_layers
class lowerCamelCase__ ( UpperCAmelCase ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def UpperCAmelCase_ (self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
lowerCamelCase_ : Optional[Any] = 'past_encoder_sequence + sequence'
lowerCamelCase_ : List[str] = {0: 'batch'}
lowerCamelCase_ : Any = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
lowerCamelCase_ : List[Any] = {0: 'batch', 1: 'decoder_sequence'}
lowerCamelCase_ : Union[str, Any] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(_snake_case , direction='inputs' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def UpperCAmelCase_ (self : Union[str, Any] ) -> int:
"""simple docstring"""
return 13
@property
def UpperCAmelCase_ (self : List[str] ) -> float:
"""simple docstring"""
return 5e-4
| 144 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __magic_name__ ( _a , unittest.TestCase):
_UpperCAmelCase : List[Any] = DanceDiffusionPipeline
_UpperCAmelCase : List[Any] = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
_UpperCAmelCase : Any = PipelineTesterMixin.required_optional_params - {
'callback',
'latents',
'callback_steps',
'output_type',
'num_images_per_prompt',
}
_UpperCAmelCase : Optional[int] = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
_UpperCAmelCase : Optional[Any] = False
_UpperCAmelCase : int = False
def _UpperCAmelCase ( self : List[Any] ):
torch.manual_seed(0 )
UpperCAmelCase = UNetaDModel(
block_out_channels=(3_2, 3_2, 6_4) ,extra_in_channels=1_6 ,sample_size=5_1_2 ,sample_rate=1_6_0_0_0 ,in_channels=2 ,out_channels=2 ,flip_sin_to_cos=__SCREAMING_SNAKE_CASE ,use_timestep_embedding=__SCREAMING_SNAKE_CASE ,time_embedding_type="fourier" ,mid_block_type="UNetMidBlock1D" ,down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,)
UpperCAmelCase = IPNDMScheduler()
UpperCAmelCase = {
"unet": unet,
"scheduler": scheduler,
}
return components
def _UpperCAmelCase ( self : Dict ,__SCREAMING_SNAKE_CASE : Any ,__SCREAMING_SNAKE_CASE : Dict=0 ):
if str(__SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
UpperCAmelCase = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def _UpperCAmelCase ( self : List[Any] ):
UpperCAmelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = DanceDiffusionPipeline(**__SCREAMING_SNAKE_CASE )
UpperCAmelCase = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
UpperCAmelCase = pipe(**__SCREAMING_SNAKE_CASE )
UpperCAmelCase = output.audios
UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def _UpperCAmelCase ( self : Tuple ):
return super().test_save_load_local()
@skip_mps
def _UpperCAmelCase ( self : Optional[int] ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def _UpperCAmelCase ( self : Optional[Any] ):
return super().test_save_load_optional_components()
@skip_mps
def _UpperCAmelCase ( self : str ):
return super().test_attention_slicing_forward_pass()
def _UpperCAmelCase ( self : Optional[int] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
def _UpperCAmelCase ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase = torch_device
UpperCAmelCase = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" )
UpperCAmelCase = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(generator=__SCREAMING_SNAKE_CASE ,num_inference_steps=1_0_0 ,audio_length_in_s=4.096 )
UpperCAmelCase = output.audios
UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self : str ):
UpperCAmelCase = torch_device
UpperCAmelCase = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" ,torch_dtype=torch.floataa )
UpperCAmelCase = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(generator=__SCREAMING_SNAKE_CASE ,num_inference_steps=1_0_0 ,audio_length_in_s=4.096 )
UpperCAmelCase = output.audios
UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
| 333 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__lowerCAmelCase =models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
__lowerCAmelCase =tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
__lowerCAmelCase =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
__lowerCAmelCase =train_datagen.flow_from_directory(
"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
__lowerCAmelCase =test_datagen.flow_from_directory(
"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
__lowerCAmelCase =tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(64, 64)
)
__lowerCAmelCase =tf.keras.preprocessing.image.img_to_array(test_image)
__lowerCAmelCase =np.expand_dims(test_image, axis=0)
__lowerCAmelCase =classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
__lowerCAmelCase ="Normal"
if result[0][0] == 1:
__lowerCAmelCase ="Abnormality detected"
| 333 | 1 |
"""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
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Tuple = {
'facebook/data2vec-vision-base-ft': (
'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
),
}
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'data2vec-vision'
def __init__( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int]=768 , _SCREAMING_SNAKE_CASE: List[Any]=12 , _SCREAMING_SNAKE_CASE: str=12 , _SCREAMING_SNAKE_CASE: Dict=3072 , _SCREAMING_SNAKE_CASE: int="gelu" , _SCREAMING_SNAKE_CASE: Optional[int]=0.0 , _SCREAMING_SNAKE_CASE: Dict=0.0 , _SCREAMING_SNAKE_CASE: Union[str, Any]=0.02 , _SCREAMING_SNAKE_CASE: Dict=1e-12 , _SCREAMING_SNAKE_CASE: str=224 , _SCREAMING_SNAKE_CASE: Optional[int]=16 , _SCREAMING_SNAKE_CASE: Tuple=3 , _SCREAMING_SNAKE_CASE: List[Any]=False , _SCREAMING_SNAKE_CASE: Tuple=False , _SCREAMING_SNAKE_CASE: Union[str, Any]=False , _SCREAMING_SNAKE_CASE: List[str]=False , _SCREAMING_SNAKE_CASE: Dict=0.1 , _SCREAMING_SNAKE_CASE: int=0.1 , _SCREAMING_SNAKE_CASE: List[Any]=True , _SCREAMING_SNAKE_CASE: Optional[int]=[3, 5, 7, 11] , _SCREAMING_SNAKE_CASE: Tuple=[1, 2, 3, 6] , _SCREAMING_SNAKE_CASE: Tuple=True , _SCREAMING_SNAKE_CASE: Optional[int]=0.4 , _SCREAMING_SNAKE_CASE: Dict=256 , _SCREAMING_SNAKE_CASE: int=1 , _SCREAMING_SNAKE_CASE: List[str]=False , _SCREAMING_SNAKE_CASE: Union[str, Any]=255 , **_SCREAMING_SNAKE_CASE: str , ) -> int:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = hidden_size
__lowerCAmelCase : Optional[Any] = num_hidden_layers
__lowerCAmelCase : Union[str, Any] = num_attention_heads
__lowerCAmelCase : Any = intermediate_size
__lowerCAmelCase : Dict = hidden_act
__lowerCAmelCase : List[str] = hidden_dropout_prob
__lowerCAmelCase : Optional[int] = attention_probs_dropout_prob
__lowerCAmelCase : Optional[Any] = initializer_range
__lowerCAmelCase : Tuple = layer_norm_eps
__lowerCAmelCase : List[str] = image_size
__lowerCAmelCase : Dict = patch_size
__lowerCAmelCase : List[Any] = num_channels
__lowerCAmelCase : Any = use_mask_token
__lowerCAmelCase : str = use_absolute_position_embeddings
__lowerCAmelCase : int = use_relative_position_bias
__lowerCAmelCase : Any = use_shared_relative_position_bias
__lowerCAmelCase : Optional[Any] = layer_scale_init_value
__lowerCAmelCase : Any = drop_path_rate
__lowerCAmelCase : List[str] = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase : str = out_indices
__lowerCAmelCase : str = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase : Union[str, Any] = use_auxiliary_head
__lowerCAmelCase : Any = auxiliary_loss_weight
__lowerCAmelCase : Tuple = auxiliary_channels
__lowerCAmelCase : Dict = auxiliary_num_convs
__lowerCAmelCase : str = auxiliary_concat_input
__lowerCAmelCase : Dict = semantic_loss_ignore_index
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = version.parse('1.11' )
@property
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> float:
"""simple docstring"""
return 1e-4 | 716 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: Any=0) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : str = np.random.RandomState(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = self.get_dummy_inputs()
__lowerCAmelCase : Optional[Any] = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : List[str] = np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Any:
"""simple docstring"""
__lowerCAmelCase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
__lowerCAmelCase : Dict = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = self.get_dummy_inputs()
__lowerCAmelCase : str = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : str = np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: str) -> Any:
"""simple docstring"""
__lowerCAmelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
__lowerCAmelCase : Optional[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = self.get_dummy_inputs()
__lowerCAmelCase : Tuple = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : Any = np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
__lowerCAmelCase : Dict = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = self.get_dummy_inputs()
__lowerCAmelCase : Tuple = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : Tuple = np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
__lowerCAmelCase : int = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = self.get_dummy_inputs()
__lowerCAmelCase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : List[Any] = np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
__lowerCAmelCase : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = self.get_dummy_inputs()
__lowerCAmelCase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE).images
__lowerCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCAmelCase : Optional[Any] = np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _SCREAMING_SNAKE_CASE ( self: Any) -> str:
"""simple docstring"""
__lowerCAmelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = self.get_dummy_inputs()
__lowerCAmelCase : List[str] = 3 * [inputs["prompt"]]
# forward
__lowerCAmelCase : Optional[Any] = pipe(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = output.images[0, -3:, -3:, -1]
__lowerCAmelCase : Union[str, Any] = self.get_dummy_inputs()
__lowerCAmelCase : Union[str, Any] = 3 * [inputs.pop("prompt")]
__lowerCAmelCase : Union[str, Any] = pipe.tokenizer(
_SCREAMING_SNAKE_CASE , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=_SCREAMING_SNAKE_CASE , return_tensors="np" , )
__lowerCAmelCase : Dict = text_inputs["input_ids"]
__lowerCAmelCase : str = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0]
__lowerCAmelCase : Union[str, Any] = prompt_embeds
# forward
__lowerCAmelCase : Tuple = pipe(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4
def _SCREAMING_SNAKE_CASE ( self: Any) -> int:
"""simple docstring"""
__lowerCAmelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = self.get_dummy_inputs()
__lowerCAmelCase : Optional[int] = 3 * ["this is a negative prompt"]
__lowerCAmelCase : Union[str, Any] = negative_prompt
__lowerCAmelCase : Union[str, Any] = 3 * [inputs["prompt"]]
# forward
__lowerCAmelCase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = output.images[0, -3:, -3:, -1]
__lowerCAmelCase : Any = self.get_dummy_inputs()
__lowerCAmelCase : List[Any] = 3 * [inputs.pop("prompt")]
__lowerCAmelCase : Dict = []
for p in [prompt, negative_prompt]:
__lowerCAmelCase : Optional[Any] = pipe.tokenizer(
_SCREAMING_SNAKE_CASE , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=_SCREAMING_SNAKE_CASE , return_tensors="np" , )
__lowerCAmelCase : Any = text_inputs["input_ids"]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0])
__lowerCAmelCase , __lowerCAmelCase : List[str] = embeds
# forward
__lowerCAmelCase : int = pipe(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class A__ ( unittest.TestCase ):
'''simple docstring'''
@property
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> int:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : str = ort.SessionOptions()
__lowerCAmelCase : List[str] = False
return options
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Any = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = "A painting of a squirrel eating a burger"
np.random.seed(0)
__lowerCAmelCase : str = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="np")
__lowerCAmelCase : Union[str, Any] = output.images
__lowerCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCAmelCase : Dict = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self: str) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = DDIMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx")
__lowerCAmelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = "open neural network exchange"
__lowerCAmelCase : Union[str, Any] = np.random.RandomState(0)
__lowerCAmelCase : List[str] = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_SCREAMING_SNAKE_CASE , output_type="np")
__lowerCAmelCase : Tuple = output.images
__lowerCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCAmelCase : Optional[Any] = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx")
__lowerCAmelCase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = "open neural network exchange"
__lowerCAmelCase : Any = np.random.RandomState(0)
__lowerCAmelCase : int = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_SCREAMING_SNAKE_CASE , output_type="np")
__lowerCAmelCase : Optional[Any] = output.images
__lowerCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCAmelCase : List[Any] = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self: str) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : str = 0
def test_callback_fn(_SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: np.ndarray) -> None:
__lowerCAmelCase : Optional[int] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowerCAmelCase : Optional[int] = latents[0, -3:, -3:, -1]
__lowerCAmelCase : List[str] = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowerCAmelCase : Tuple = latents[0, -3:, -3:, -1]
__lowerCAmelCase : Any = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
__lowerCAmelCase : Dict = False
__lowerCAmelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = "Andromeda galaxy in a bottle"
__lowerCAmelCase : Any = np.random.RandomState(0)
pipe(
prompt=_SCREAMING_SNAKE_CASE , num_inference_steps=5 , guidance_scale=7.5 , generator=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _SCREAMING_SNAKE_CASE ( self: str) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
assert pipe.safety_checker is None
__lowerCAmelCase : Optional[Any] = pipe("example prompt" , num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowerCAmelCase : Optional[Any] = pipe("example prompt" , num_inference_steps=2).images[0]
assert image is not None | 615 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__= GPTaTokenizer
A__= GPTaTokenizerFast
A__= True
A__= {'add_prefix_space': True}
A__= False
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
UpperCAmelCase__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
UpperCAmelCase__ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
UpperCAmelCase__ = {"unk_token": "<unk>"}
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(_lowercase ) )
def _UpperCAmelCase ( self : int , **_lowercase : List[Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def _UpperCAmelCase ( self : Optional[Any] , **_lowercase : str ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **_lowercase )
def _UpperCAmelCase ( self : int , _lowercase : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = "lower newer"
UpperCAmelCase__ = "lower newer"
return input_text, output_text
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase__ = "lower newer"
UpperCAmelCase__ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
UpperCAmelCase__ = tokenizer.tokenize(_lowercase , add_prefix_space=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
UpperCAmelCase__ = tokens + [tokenizer.unk_token]
UpperCAmelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_rust_tokenizer(add_prefix_space=_lowercase )
UpperCAmelCase__ = "lower newer"
# Testing tokenization
UpperCAmelCase__ = tokenizer.tokenize(_lowercase , add_prefix_space=_lowercase )
UpperCAmelCase__ = rust_tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
# Testing conversion to ids without special tokens
UpperCAmelCase__ = tokenizer.encode(_lowercase , add_special_tokens=_lowercase , add_prefix_space=_lowercase )
UpperCAmelCase__ = rust_tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
# Testing conversion to ids with special tokens
UpperCAmelCase__ = self.get_rust_tokenizer(add_prefix_space=_lowercase )
UpperCAmelCase__ = tokenizer.encode(_lowercase , add_prefix_space=_lowercase )
UpperCAmelCase__ = rust_tokenizer.encode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
# Testing the unknown token
UpperCAmelCase__ = tokens + [rust_tokenizer.unk_token]
UpperCAmelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def _UpperCAmelCase ( self : List[Any] , *_lowercase : Union[str, Any] , **_lowercase : Optional[Any] ):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : Union[str, Any] , _lowercase : Optional[Any]=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
# Simple input
UpperCAmelCase__ = "This is a simple input"
UpperCAmelCase__ = ["This is a simple input 1", "This is a simple input 2"]
UpperCAmelCase__ = ("This is a simple input", "This is a pair")
UpperCAmelCase__ = [
("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
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding="max_length" )
# Simple input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding="max_length" )
# Simple input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding="max_length" , )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding="max_length" )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding="max_length" )
# Pair input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding="max_length" , )
def _UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
UpperCAmelCase__ = "This is a simple input"
UpperCAmelCase__ = ["This is a simple input looooooooong", "This is a simple input"]
UpperCAmelCase__ = ("This is a simple input", "This is a pair")
UpperCAmelCase__ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
UpperCAmelCase__ = tokenizer.pad_token_id
UpperCAmelCase__ = tokenizer(_lowercase , padding="max_length" , max_length=30 , return_tensors="np" )
UpperCAmelCase__ = tokenizer(_lowercase , padding=_lowercase , truncate=_lowercase , return_tensors="np" )
UpperCAmelCase__ = tokenizer(*_lowercase , padding="max_length" , max_length=60 , return_tensors="np" )
UpperCAmelCase__ = tokenizer(_lowercase , padding=_lowercase , truncate=_lowercase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def _UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = "$$$"
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=_lowercase , add_bos_token=_lowercase )
UpperCAmelCase__ = "This is a simple input"
UpperCAmelCase__ = ["This is a simple input 1", "This is a simple input 2"]
UpperCAmelCase__ = tokenizer.bos_token_id
UpperCAmelCase__ = tokenizer(_lowercase )
UpperCAmelCase__ = tokenizer(_lowercase )
self.assertEqual(out_s.input_ids[0] , _lowercase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
UpperCAmelCase__ = tokenizer.decode(out_s.input_ids )
UpperCAmelCase__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _lowercase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
pass
def _UpperCAmelCase ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = [self.get_tokenizer(do_lower_case=_lowercase , add_bos_token=_lowercase )]
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase__ = "Encode this."
UpperCAmelCase__ = "This one too please."
UpperCAmelCase__ = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
encoded_sequence += tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
UpperCAmelCase__ = tokenizer.encode_plus(
_lowercase , _lowercase , add_special_tokens=_lowercase , return_special_tokens_mask=_lowercase , )
UpperCAmelCase__ = encoded_sequence_dict["input_ids"]
UpperCAmelCase__ = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
UpperCAmelCase__ = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(_lowercase )
]
UpperCAmelCase__ = [x for x in filtered_sequence if x is not None]
self.assertEqual(_lowercase , _lowercase )
@require_tokenizers
class lowercase__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=_lowercase )
UpperCAmelCase__ = "A photo of a cat"
UpperCAmelCase__ = tokenizer.encode(
_lowercase , )
self.assertEqual(_lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
tokenizer.save_pretrained("test_opt" )
UpperCAmelCase__ = AutoTokenizer.from_pretrained("./test_opt" )
UpperCAmelCase__ = tokenizer.encode(
_lowercase , )
self.assertEqual(_lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , use_slow=_lowercase )
UpperCAmelCase__ = "A photo of a cat"
UpperCAmelCase__ = tokenizer.encode(
_lowercase , )
# Same as above
self.assertEqual(_lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
@unittest.skip("This test is failing because of a bug in the fast tokenizer" )
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=_lowercase )
UpperCAmelCase__ = "bos"
UpperCAmelCase__ = tokenizer.get_vocab()["bos"]
UpperCAmelCase__ = "A photo of a cat"
UpperCAmelCase__ = tokenizer.encode(
_lowercase , )
# We changed the bos token
self.assertEqual(_lowercase , [3_19_57, 2_50, 13_45, 9, 10, 47_58] )
tokenizer.save_pretrained("./tok" )
UpperCAmelCase__ = AutoTokenizer.from_pretrained("./tok" )
self.assertTrue(tokenizer.is_fast )
UpperCAmelCase__ = tokenizer.encode(
_lowercase , )
self.assertEqual(_lowercase , [3_19_57, 2_50, 13_45, 9, 10, 47_58] )
| 475 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= 42
A__= 42
def __init__( self : Tuple , _lowercase : UNetaDModel , _lowercase : ScoreSdeVeScheduler ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=_lowercase , scheduler=_lowercase )
@torch.no_grad()
def __call__( self : Dict , _lowercase : int = 1 , _lowercase : int = 20_00 , _lowercase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase : Optional[str] = "pil" , _lowercase : bool = True , **_lowercase : Any , ):
"""simple docstring"""
UpperCAmelCase__ = self.unet.config.sample_size
UpperCAmelCase__ = (batch_size, 3, img_size, img_size)
UpperCAmelCase__ = self.unet
UpperCAmelCase__ = randn_tensor(_lowercase , generator=_lowercase ) * self.scheduler.init_noise_sigma
UpperCAmelCase__ = sample.to(self.device )
self.scheduler.set_timesteps(_lowercase )
self.scheduler.set_sigmas(_lowercase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCAmelCase__ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
UpperCAmelCase__ = self.unet(_lowercase , _lowercase ).sample
UpperCAmelCase__ = self.scheduler.step_correct(_lowercase , _lowercase , generator=_lowercase ).prev_sample
# prediction step
UpperCAmelCase__ = model(_lowercase , _lowercase ).sample
UpperCAmelCase__ = self.scheduler.step_pred(_lowercase , _lowercase , _lowercase , generator=_lowercase )
UpperCAmelCase__ , UpperCAmelCase__ = output.prev_sample, output.prev_sample_mean
UpperCAmelCase__ = sample_mean.clamp(0 , 1 )
UpperCAmelCase__ = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_lowercase )
| 475 | 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 : Union[str, Any] = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
"""tokenization_roberta""": ["""RobertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : str = ["""RobertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : str = [
"""ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaForCausalLM""",
"""RobertaForMaskedLM""",
"""RobertaForMultipleChoice""",
"""RobertaForQuestionAnswering""",
"""RobertaForSequenceClassification""",
"""RobertaForTokenClassification""",
"""RobertaModel""",
"""RobertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : int = [
"""TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaForCausalLM""",
"""TFRobertaForMaskedLM""",
"""TFRobertaForMultipleChoice""",
"""TFRobertaForQuestionAnswering""",
"""TFRobertaForSequenceClassification""",
"""TFRobertaForTokenClassification""",
"""TFRobertaMainLayer""",
"""TFRobertaModel""",
"""TFRobertaPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : List[Any] = [
"""FlaxRobertaForCausalLM""",
"""FlaxRobertaForMaskedLM""",
"""FlaxRobertaForMultipleChoice""",
"""FlaxRobertaForQuestionAnswering""",
"""FlaxRobertaForSequenceClassification""",
"""FlaxRobertaForTokenClassification""",
"""FlaxRobertaModel""",
"""FlaxRobertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
_UpperCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 720 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : int = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_blip_2': ['Blip2Processor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase : Any = [
'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Blip2Model',
'Blip2QFormerModel',
'Blip2PreTrainedModel',
'Blip2ForConditionalGeneration',
'Blip2VisionModel',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
_UpperCamelCase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 134 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : Any = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : int = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : List[str] = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : Any = (1 - _cos) / 2
SCREAMING_SNAKE_CASE_ : Dict = 1 - _cos
SCREAMING_SNAKE_CASE_ : Dict = 1 + alpha
SCREAMING_SNAKE_CASE_ : int = -2 * _cos
SCREAMING_SNAKE_CASE_ : Dict = 1 - alpha
SCREAMING_SNAKE_CASE_ : str = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : int = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Any = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (1 + _cos) / 2
SCREAMING_SNAKE_CASE_ : List[Any] = -1 - _cos
SCREAMING_SNAKE_CASE_ : Optional[int] = 1 + alpha
SCREAMING_SNAKE_CASE_ : Optional[Any] = -2 * _cos
SCREAMING_SNAKE_CASE_ : Optional[int] = 1 - alpha
SCREAMING_SNAKE_CASE_ : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : Any = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : List[str] = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : List[Any] = _sin / 2
SCREAMING_SNAKE_CASE_ : Dict = 0
SCREAMING_SNAKE_CASE_ : Tuple = -ba
SCREAMING_SNAKE_CASE_ : int = 1 + alpha
SCREAMING_SNAKE_CASE_ : List[str] = -2 * _cos
SCREAMING_SNAKE_CASE_ : str = 1 - alpha
SCREAMING_SNAKE_CASE_ : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : int = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : List[Any] = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : List[Any] = 1 - alpha
SCREAMING_SNAKE_CASE_ : Tuple = -2 * _cos
SCREAMING_SNAKE_CASE_ : Dict = 1 + alpha
SCREAMING_SNAKE_CASE_ : str = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float , lowerCamelCase_ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : Optional[Any] = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : Any = 10 ** (gain_db / 40)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 1 + alpha * big_a
SCREAMING_SNAKE_CASE_ : List[str] = -2 * _cos
SCREAMING_SNAKE_CASE_ : List[Any] = 1 - alpha * big_a
SCREAMING_SNAKE_CASE_ : Dict = 1 + alpha / big_a
SCREAMING_SNAKE_CASE_ : List[Any] = -2 * _cos
SCREAMING_SNAKE_CASE_ : int = 1 - alpha / big_a
SCREAMING_SNAKE_CASE_ : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float , lowerCamelCase_ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : Any = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : str = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : str = 10 ** (gain_db / 40)
SCREAMING_SNAKE_CASE_ : List[str] = (big_a + 1) - (big_a - 1) * _cos
SCREAMING_SNAKE_CASE_ : str = (big_a + 1) + (big_a - 1) * _cos
SCREAMING_SNAKE_CASE_ : int = (big_a - 1) - (big_a + 1) * _cos
SCREAMING_SNAKE_CASE_ : Dict = (big_a - 1) + (big_a + 1) * _cos
SCREAMING_SNAKE_CASE_ : int = 2 * sqrt(lowerCamelCase_ ) * alpha
SCREAMING_SNAKE_CASE_ : str = big_a * (pmc + aaa)
SCREAMING_SNAKE_CASE_ : List[str] = 2 * big_a * mpc
SCREAMING_SNAKE_CASE_ : Union[str, Any] = big_a * (pmc - aaa)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ppmc + aaa
SCREAMING_SNAKE_CASE_ : Dict = -2 * pmpc
SCREAMING_SNAKE_CASE_ : int = ppmc - aaa
SCREAMING_SNAKE_CASE_ : Optional[int] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float , lowerCamelCase_ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : Tuple = sin(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Dict = cos(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Any = _sin / (2 * q_factor)
SCREAMING_SNAKE_CASE_ : Dict = 10 ** (gain_db / 40)
SCREAMING_SNAKE_CASE_ : Tuple = (big_a + 1) - (big_a - 1) * _cos
SCREAMING_SNAKE_CASE_ : List[Any] = (big_a + 1) + (big_a - 1) * _cos
SCREAMING_SNAKE_CASE_ : Optional[Any] = (big_a - 1) - (big_a + 1) * _cos
SCREAMING_SNAKE_CASE_ : Dict = (big_a - 1) + (big_a + 1) * _cos
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 2 * sqrt(lowerCamelCase_ ) * alpha
SCREAMING_SNAKE_CASE_ : Tuple = big_a * (ppmc + aaa)
SCREAMING_SNAKE_CASE_ : Optional[int] = -2 * big_a * pmpc
SCREAMING_SNAKE_CASE_ : Union[str, Any] = big_a * (ppmc - aaa)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = pmc + aaa
SCREAMING_SNAKE_CASE_ : str = 2 * mpc
SCREAMING_SNAKE_CASE_ : str = pmc - aaa
SCREAMING_SNAKE_CASE_ : Tuple = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_case__ ):
warnings.warn(
'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PoolFormerImageProcessor instead.' ,snake_case__ ,)
super().__init__(*snake_case__ ,**snake_case__ )
| 105 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[int] = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__):
'''simple docstring'''
__UpperCamelCase : Any = "blenderbot-small"
__UpperCamelCase : int = ["past_key_values"]
__UpperCamelCase : Union[str, Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , __SCREAMING_SNAKE_CASE=50_265 , __SCREAMING_SNAKE_CASE=512 , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=2_048 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=2_048 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=512 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=2 , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Union[str, Any] = d_model
UpperCamelCase : List[str] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : Optional[Any] = encoder_attention_heads
UpperCamelCase : int = decoder_ffn_dim
UpperCamelCase : List[str] = decoder_layers
UpperCamelCase : str = decoder_attention_heads
UpperCamelCase : str = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : Any = activation_dropout
UpperCamelCase : Optional[int] = activation_function
UpperCamelCase : int = init_std
UpperCamelCase : List[str] = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : List[Any] = use_cache
UpperCamelCase : Optional[Any] = encoder_layers
UpperCamelCase : Any = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , is_encoder_decoder=_lowercase , decoder_start_token_id=_lowercase , forced_eos_token_id=_lowercase , **_lowercase , )
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__):
'''simple docstring'''
@property
def _lowercase ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Optional[Any] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCamelCase : int = {0: """batch"""}
UpperCamelCase : str = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
UpperCamelCase : Union[str, Any] = {0: """batch""", 1: """decoder_sequence"""}
UpperCamelCase : Optional[Any] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCamelCase : Dict = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCamelCase : int = self.num_layers
for i in range(_lowercase ):
UpperCamelCase : Any = {0: """batch""", 2: """past_sequence + sequence"""}
UpperCamelCase : Optional[int] = {0: """batch""", 2: """past_sequence + sequence"""}
else:
UpperCamelCase : Any = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def _lowercase ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Any = super().outputs
else:
UpperCamelCase : Optional[int] = super(_lowercase , self ).outputs
if self.use_past:
UpperCamelCase : str = self.num_layers
for i in range(_lowercase ):
UpperCamelCase : str = {0: """batch""", 2: """past_sequence + sequence"""}
UpperCamelCase : Dict = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ):
"""simple docstring"""
UpperCamelCase : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# Generate decoder inputs
UpperCamelCase : List[str] = seq_length if not self.use_past else 1
UpperCamelCase : str = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
UpperCamelCase : Optional[Any] = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
UpperCamelCase : Any = dict(**_lowercase , **_lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCamelCase : Optional[int] = common_inputs["""input_ids"""].shape
UpperCamelCase : Optional[int] = common_inputs["""decoder_input_ids"""].shape[1]
UpperCamelCase : Optional[Any] = self.num_attention_heads
UpperCamelCase : Optional[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase : Tuple = decoder_seq_length + 3
UpperCamelCase : int = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCamelCase : Optional[Any] = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(_lowercase , _lowercase )] , dim=1 )
UpperCamelCase : Optional[Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCamelCase : List[str] = self.num_layers
UpperCamelCase : List[str] = min(_lowercase , _lowercase )
UpperCamelCase : Optional[Any] = max(_lowercase , _lowercase ) - min_num_layers
UpperCamelCase : Optional[Any] = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(_lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
) )
# TODO: test this.
UpperCamelCase : Optional[Any] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(_lowercase , _lowercase ):
common_inputs["past_key_values"].append((torch.zeros(_lowercase ), torch.zeros(_lowercase )) )
return common_inputs
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCamelCase : Any = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
UpperCamelCase : List[Any] = seqlen + 2
UpperCamelCase : Any = self.num_layers
UpperCamelCase : Any = self.num_attention_heads
UpperCamelCase : List[str] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase : str = common_inputs["""attention_mask"""].dtype
UpperCamelCase : Tuple = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 )
UpperCamelCase : int = [
(torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(_lowercase )
]
return common_inputs
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ):
"""simple docstring"""
UpperCamelCase : Any = compute_effective_axis_dimension(
_lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCamelCase : Any = tokenizer.num_special_tokens_to_add(_lowercase )
UpperCamelCase : Dict = compute_effective_axis_dimension(
_lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCamelCase : Dict = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCamelCase : List[Any] = dict(tokenizer(_lowercase , return_tensors=_lowercase ) )
return common_inputs
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
elif self.task == "causal-lm":
UpperCamelCase : int = self._generate_dummy_inputs_for_causal_lm(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
else:
UpperCamelCase : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
return common_inputs
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Union[str, Any] = super()._flatten_past_key_values_(_lowercase , _lowercase , _lowercase , _lowercase )
else:
UpperCamelCase : int = super(_lowercase , self )._flatten_past_key_values_(
_lowercase , _lowercase , _lowercase , _lowercase )
| 721 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : str = cva.getAffineTransform(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return cva.warpAffine(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , (rows, cols) )
if __name__ == "__main__":
# read original image
__UpperCAmelCase : Tuple = cva.imread(
str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
)
# turn image in gray scale value
__UpperCAmelCase : int = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
__UpperCAmelCase , __UpperCAmelCase : Tuple = gray_img.shape
# set different points to rotate image
__UpperCAmelCase : Optional[int] = np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
__UpperCAmelCase : Optional[int] = np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
__UpperCAmelCase : Any = np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
__UpperCAmelCase : int = np.array([[10, 100], [80, 50], [180, 250]], np.floataa)
# add all rotated images in a list
__UpperCAmelCase : Union[str, Any] = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
__UpperCAmelCase : List[str] = plt.figure(1)
__UpperCAmelCase : Dict = ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
plt.title(titles[i])
plt.axis("off")
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 643 | 0 |
def UpperCamelCase ( _UpperCAmelCase : int = 6008_5147_5143 ) -> int:
'''simple docstring'''
try:
_lowercase : str = int(_UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
_lowercase : str = 1
_lowercase : List[str] = 2
while i * i <= n:
while n % i == 0:
_lowercase : Tuple = i
n //= i
i += 1
if n > 1:
_lowercase : int = n
return int(_UpperCAmelCase )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 461 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __lowercase ( __snake_case ):
def __init__(self : List[Any] , snake_case : Optional[int] , snake_case : str , snake_case : str ) -> Dict:
_lowercase : Tuple = dataset
_lowercase : List[str] = process
_lowercase : Any = params
def __len__(self : Optional[Any] ) -> Any:
return len(self.dataset )
def __getitem__(self : int , snake_case : Any ) -> int:
_lowercase : Optional[Any] = self.dataset[i]
_lowercase : Union[str, Any] = self.process(snake_case , **self.params )
return processed
class __lowercase ( __snake_case ):
def __init__(self : int , snake_case : List[Any] , snake_case : Tuple , snake_case : Tuple , snake_case : List[str]=None ) -> Optional[int]:
_lowercase : List[str] = loader
_lowercase : Optional[Any] = infer
_lowercase : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_lowercase : str = None
_lowercase : Optional[int] = loader_batch_size
# Internal bookkeeping
_lowercase : Dict = None
_lowercase : Union[str, Any] = None
def __len__(self : Any ) -> Any:
return len(self.loader )
def __iter__(self : int ) -> Optional[Any]:
_lowercase : List[str] = iter(self.loader )
return self
def _a(self : int ) -> List[str]:
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_lowercase : Any = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_lowercase : List[str] = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case , snake_case ):
# Convert ModelOutput to tuple first
_lowercase : Dict = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_lowercase : Optional[int] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_lowercase : Optional[Any] = 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(snake_case , snake_case ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_lowercase : Optional[int] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_lowercase : List[str] = 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
_lowercase : Optional[Any] = 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
_lowercase : int = 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
_lowercase : Tuple = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_lowercase : Union[str, Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_lowercase : int = self._loader_batch_data.__class__(snake_case )
self._loader_batch_index += 1
return result
def _a(self : Any ) -> str:
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
_lowercase : str = next(self.iterator )
_lowercase : Any = self.infer(snake_case , **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(snake_case , torch.Tensor ):
_lowercase : int = processed
else:
_lowercase : List[str] = list(processed.keys() )[0]
_lowercase : List[str] = processed[key]
if isinstance(snake_case , snake_case ):
_lowercase : int = len(snake_case )
else:
_lowercase : Optional[int] = 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.
_lowercase : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_lowercase : Any = processed
_lowercase : Dict = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class __lowercase ( __snake_case ):
def __init__(self : Union[str, Any] , snake_case : str , snake_case : Dict , snake_case : Optional[Any] , snake_case : List[str]=None ) -> List[str]:
super().__init__(snake_case , snake_case , snake_case )
def __iter__(self : List[Any] ) -> str:
_lowercase : int = iter(self.loader )
_lowercase : Optional[int] = None
return self
def _a(self : Optional[Any] ) -> Optional[int]:
if self.subiterator is None:
_lowercase : List[str] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_lowercase : str = 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
_lowercase : Any = self.infer(next(self.iterator ) , **self.params )
_lowercase : Optional[int] = next(self.subiterator )
return processed
class __lowercase ( __snake_case ):
def __iter__(self : List[str] ) -> List[str]:
_lowercase : Any = iter(self.loader )
return self
def _a(self : List[Any] ) -> Union[str, Any]:
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
_lowercase : Dict = False
_lowercase : Optional[int] = []
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:
_lowercase : Tuple = self.loader_batch_item()
_lowercase : List[Any] = item.pop("is_last" )
accumulator.append(snake_case )
if is_last:
return accumulator
while not is_last:
_lowercase : List[Any] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case , torch.Tensor ):
_lowercase : Optional[Any] = processed
else:
_lowercase : Tuple = list(processed.keys() )[0]
_lowercase : int = processed[key]
if isinstance(snake_case , snake_case ):
_lowercase : str = len(snake_case )
else:
_lowercase : List[str] = 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.
_lowercase : Tuple = observed_batch_size
_lowercase : Any = processed
_lowercase : Dict = 0
while self._loader_batch_index < self.loader_batch_size:
_lowercase : str = self.loader_batch_item()
_lowercase : int = item.pop("is_last" )
accumulator.append(snake_case )
if is_last:
return accumulator
else:
_lowercase : str = processed
_lowercase : int = item.pop("is_last" )
accumulator.append(snake_case )
return accumulator
class __lowercase ( __snake_case ):
def __init__(self : int , snake_case : Dataset , snake_case : str ) -> List[Any]:
_lowercase : Optional[Any] = dataset
_lowercase : Any = key
def __len__(self : Any ) -> Union[str, Any]:
return len(self.dataset )
def __getitem__(self : int , snake_case : Any ) -> Any:
return self.dataset[i][self.key]
class __lowercase ( __snake_case ):
def __init__(self : int , snake_case : Dataset , snake_case : str , snake_case : str ) -> Dict:
_lowercase : int = dataset
_lowercase : Optional[Any] = keya
_lowercase : Tuple = keya
def __len__(self : List[str] ) -> Union[str, Any]:
return len(self.dataset )
def __getitem__(self : Optional[Any] , snake_case : Dict ) -> int:
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 461 | 1 |
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class _SCREAMING_SNAKE_CASE (unittest.TestCase ):
def __init__( self : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any]=7 , __UpperCamelCase : Optional[Any]=3 , __UpperCamelCase : Optional[int]=18 , __UpperCamelCase : Union[str, Any]=30 , __UpperCamelCase : Optional[int]=400 , __UpperCamelCase : int=True , __UpperCamelCase : Dict=None , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : List[Any]=[0.5, 0.5, 0.5] , __UpperCamelCase : Union[str, Any]=[0.5, 0.5, 0.5] , __UpperCamelCase : str=False , ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Tuple = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case__ : Any = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case__ : Union[str, Any] = parent
snake_case__ : Dict = batch_size
snake_case__ : Dict = num_channels
snake_case__ : Optional[int] = image_size
snake_case__ : Optional[int] = min_resolution
snake_case__ : str = max_resolution
snake_case__ : int = do_resize
snake_case__ : Any = size
snake_case__ : Optional[Any] = do_center_crop
snake_case__ : Union[str, Any] = crop_size
snake_case__ : str = do_normalize
snake_case__ : Tuple = image_mean
snake_case__ : Any = image_std
snake_case__ : Any = do_reduce_labels
def lowerCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def __UpperCAmelCase ( ) -> int:
snake_case__ : int = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case__ : Any = Image.open(dataset[0]['''file'''] )
snake_case__ : Dict = Image.open(dataset[1]['''file'''] )
return image, map
def __UpperCAmelCase ( ) -> List[Any]:
snake_case__ : List[Any] = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case__ : List[str] = Image.open(ds[0]['''file'''] )
snake_case__ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case__ : Tuple = Image.open(ds[2]['''file'''] )
snake_case__ : int = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE (a__, unittest.TestCase ):
A__ = BeitImageProcessor if is_vision_available() else None
def lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
snake_case__ : Optional[int] = BeitImageProcessingTester(self )
@property
def lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
snake_case__ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase_ , '''do_resize''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''size''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''do_center_crop''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''center_crop''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''image_mean''' ) )
self.assertTrue(hasattr(lowerCamelCase_ , '''image_std''' ) )
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , lowerCamelCase_ )
snake_case__ : int = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=lowerCamelCase_ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , lowerCamelCase_ )
def lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , Image.Image )
# Test not batched input
snake_case__ : Optional[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
snake_case__ : Optional[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'''],
) , )
def lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ : List[str] = 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
snake_case__ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case__ : Optional[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'''],
) , )
def lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
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
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
snake_case__ : Dict = 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 lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase_ , torchify=lowerCamelCase_ )
snake_case__ : List[Any] = []
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case__ : List[Any] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case__ : List[Any] = image_processing(lowerCamelCase_ , lowerCamelCase_ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].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'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case__ , snake_case__ : Tuple = prepare_semantic_single_inputs()
snake_case__ : Union[str, Any] = image_processing(lowerCamelCase_ , lowerCamelCase_ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case__ , snake_case__ : Tuple = prepare_semantic_batch_inputs()
snake_case__ : Any = image_processing(lowerCamelCase_ , lowerCamelCase_ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
snake_case__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case__ , snake_case__ : Union[str, Any] = prepare_semantic_single_inputs()
snake_case__ : Optional[Any] = image_processing(lowerCamelCase_ , lowerCamelCase_ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case__ : Union[str, Any] = True
snake_case__ : Any = image_processing(lowerCamelCase_ , lowerCamelCase_ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 710 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_lowercase : Tuple ={
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
logging.set_verbosity_info()
def __UpperCAmelCase ( UpperCamelCase__ :List[str] , UpperCamelCase__ :Any , UpperCamelCase__ :int , UpperCamelCase__ :Dict=None ) -> int:
# Initialise PyTorch model
snake_case__ : List[Any] = XLNetConfig.from_json_file(UpperCamelCase__ )
snake_case__ : Optional[Any] = finetuning_task.lower() if finetuning_task is not None else ''''''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''' )
snake_case__ : Union[str, Any] = finetuning_task
snake_case__ : str = GLUE_TASKS_NUM_LABELS[finetuning_task]
snake_case__ : List[Any] = XLNetForSequenceClassification(UpperCamelCase__ )
elif "squad" in finetuning_task:
snake_case__ : str = finetuning_task
snake_case__ : List[str] = XLNetForQuestionAnswering(UpperCamelCase__ )
else:
snake_case__ : Tuple = XLNetLMHeadModel(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Save pytorch-model
snake_case__ : str = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
snake_case__ : Tuple = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
print(F'''Save PyTorch model to {os.path.abspath(UpperCamelCase__ )}''' )
torch.save(model.state_dict() , UpperCamelCase__ )
print(F'''Save configuration file to {os.path.abspath(UpperCamelCase__ )}''' )
with open(UpperCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_lowercase : Tuple =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--xlnet_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained XLNet model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--finetuning_task",
default=None,
type=str,
help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
)
_lowercase : Optional[Any] =parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 574 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase_ (__A , unittest.TestCase ):
__magic_name__ = ConsistencyModelPipeline
__magic_name__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__magic_name__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
__magic_name__ = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
UpperCAmelCase_ : Union[str, Any] = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet" , )
return unet
@property
def _SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
UpperCAmelCase_ : Optional[Any] = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet_class_cond" , )
return unet
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : Optional[Any]=False ) -> List[Any]:
if class_cond:
UpperCAmelCase_ : Union[str, Any] = self.dummy_cond_unet
else:
UpperCAmelCase_ : Any = self.dummy_uncond_unet
# Default to CM multistep sampler
UpperCAmelCase_ : int = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
UpperCAmelCase_ : Any = {
"unet": unet,
"scheduler": scheduler,
}
return components
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any=0 ) -> List[Any]:
if str(lowerCAmelCase_ ).startswith("mps" ):
UpperCAmelCase_ : Any = torch.manual_seed(lowerCAmelCase_ )
else:
UpperCAmelCase_ : List[str] = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = {
"batch_size": 1,
"num_inference_steps": None,
"timesteps": [22, 0],
"generator": generator,
"output_type": "np",
}
return inputs
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase_ : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ : str = self.get_dummy_components()
UpperCAmelCase_ : List[str] = ConsistencyModelPipeline(**lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = self.get_dummy_inputs(lowerCAmelCase_ )
UpperCAmelCase_ : Any = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ : List[str] = image[0, -3:, -3:, -1]
UpperCAmelCase_ : List[Any] = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
UpperCAmelCase_ : List[str] = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ : Optional[int] = self.get_dummy_components(class_cond=lowerCAmelCase_ )
UpperCAmelCase_ : str = ConsistencyModelPipeline(**lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = self.get_dummy_inputs(lowerCAmelCase_ )
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : int = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Dict = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
UpperCAmelCase_ : Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ : Dict = self.get_dummy_components()
UpperCAmelCase_ : Tuple = ConsistencyModelPipeline(**lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = self.get_dummy_inputs(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = 1
UpperCAmelCase_ : List[Any] = None
UpperCAmelCase_ : Dict = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ : Dict = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Optional[int] = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
UpperCAmelCase_ : int = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ : Tuple = self.get_dummy_components(class_cond=lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = ConsistencyModelPipeline(**lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : int = self.get_dummy_inputs(lowerCAmelCase_ )
UpperCAmelCase_ : str = 1
UpperCAmelCase_ : List[Any] = None
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : int = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ : List[str] = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Optional[int] = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class UpperCamelCase_ (unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : Any=0 , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Optional[Any]="cpu" , lowerCAmelCase_ : Dict=torch.floataa , lowerCAmelCase_ : Optional[Any]=(1, 3, 64, 64) ) -> str:
UpperCAmelCase_ : str = torch.manual_seed(lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = {
"num_inference_steps": None,
"timesteps": [22, 0],
"class_labels": 0,
"generator": generator,
"output_type": "np",
}
if get_fixed_latents:
UpperCAmelCase_ : str = self.get_fixed_latents(seed=lowerCAmelCase_ , device=lowerCAmelCase_ , dtype=lowerCAmelCase_ , shape=lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = latents
return inputs
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : List[Any]=0 , lowerCAmelCase_ : Any="cpu" , lowerCAmelCase_ : Optional[Any]=torch.floataa , lowerCAmelCase_ : int=(1, 3, 64, 64) ) -> Optional[Any]:
if type(lowerCAmelCase_ ) == str:
UpperCAmelCase_ : Tuple = torch.device(lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
return latents
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
UpperCAmelCase_ : List[str] = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
UpperCAmelCase_ : str = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
UpperCAmelCase_ : int = ConsistencyModelPipeline(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
pipe.to(torch_device=lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = self.get_inputs()
UpperCAmelCase_ : int = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_ : Any = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Union[str, Any] = np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
UpperCAmelCase_ : List[str] = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
UpperCAmelCase_ : Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
UpperCAmelCase_ : str = ConsistencyModelPipeline(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
pipe.to(torch_device=lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : str = self.get_inputs()
UpperCAmelCase_ : Union[str, Any] = 1
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : List[str] = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_ : Dict = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Union[str, Any] = np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
@require_torch_a
def _SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
UpperCAmelCase_ : Optional[int] = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
UpperCAmelCase_ : int = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
UpperCAmelCase_ : Optional[Any] = ConsistencyModelPipeline(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
pipe.to(torch_device=lowerCAmelCase_ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = self.get_inputs(get_fixed_latents=lowerCAmelCase_ , device=lowerCAmelCase_ )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCAmelCase_ , enable_math=lowerCAmelCase_ , enable_mem_efficient=lowerCAmelCase_ ):
UpperCAmelCase_ : Optional[Any] = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_ : Dict = image[0, -3:, -3:, -1]
UpperCAmelCase_ : int = np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@require_torch_a
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
UpperCAmelCase_ : Tuple = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
UpperCAmelCase_ : Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
UpperCAmelCase_ : Optional[Any] = ConsistencyModelPipeline(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
pipe.to(torch_device=lowerCAmelCase_ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = self.get_inputs(get_fixed_latents=lowerCAmelCase_ , device=lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = 1
UpperCAmelCase_ : Optional[int] = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCAmelCase_ , enable_math=lowerCAmelCase_ , enable_mem_efficient=lowerCAmelCase_ ):
UpperCAmelCase_ : Optional[int] = pipe(**lowerCAmelCase_ ).images
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_ : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase_ : Dict = np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 95 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = Dict[str, Any]
__magic_name__ = List[Prediction]
@add_end_docstrings(lowerCamelCase )
class _lowerCAmelCase ( lowerCamelCase ):
def __init__( self , *a_ , **a_ ) -> Optional[int]:
super().__init__(*a_ , **a_ )
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
requires_backends(self , "vision" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def _a ( self , **a_ ) -> List[str]:
_UpperCAmelCase = {}
if "threshold" in kwargs:
_UpperCAmelCase = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__( self , *a_ , **a_ ) -> Union[Predictions, List[Prediction]]:
return super().__call__(*a_ , **a_ )
def _a ( self , a_ ) -> Optional[Any]:
_UpperCAmelCase = load_image(a_ )
_UpperCAmelCase = torch.IntTensor([[image.height, image.width]] )
_UpperCAmelCase = self.image_processor(images=[image] , return_tensors="pt" )
if self.tokenizer is not None:
_UpperCAmelCase = self.tokenizer(text=inputs["words"] , boxes=inputs["boxes"] , return_tensors="pt" )
_UpperCAmelCase = target_size
return inputs
def _a ( self , a_ ) -> Optional[Any]:
_UpperCAmelCase = model_inputs.pop("target_size" )
_UpperCAmelCase = self.model(**a_ )
_UpperCAmelCase = outputs.__class__({"target_size": target_size, **outputs} )
if self.tokenizer is not None:
_UpperCAmelCase = model_inputs["bbox"]
return model_outputs
def _a ( self , a_ , a_=0.9 ) -> int:
_UpperCAmelCase = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
_UpperCAmelCase , _UpperCAmelCase = target_size[0].tolist()
def unnormalize(a_ ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
_UpperCAmelCase , _UpperCAmelCase = model_outputs["logits"].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
_UpperCAmelCase = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
_UpperCAmelCase = [unnormalize(a_ ) for bbox in model_outputs["bbox"].squeeze(0 )]
_UpperCAmelCase = ["score", "label", "box"]
_UpperCAmelCase = [dict(zip(a_ , a_ ) ) for vals in zip(scores.tolist() , a_ , a_ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
_UpperCAmelCase = self.image_processor.post_process_object_detection(a_ , a_ , a_ )
_UpperCAmelCase = raw_annotations[0]
_UpperCAmelCase = raw_annotation["scores"]
_UpperCAmelCase = raw_annotation["labels"]
_UpperCAmelCase = raw_annotation["boxes"]
_UpperCAmelCase = scores.tolist()
_UpperCAmelCase = [self.model.config.idalabel[label.item()] for label in labels]
_UpperCAmelCase = [self._get_bounding_box(a_ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
_UpperCAmelCase = ["score", "label", "box"]
_UpperCAmelCase = [
dict(zip(a_ , a_ ) )
for vals in zip(raw_annotation["scores"] , raw_annotation["labels"] , raw_annotation["boxes"] )
]
return annotation
def _a ( self , a_ ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch." )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = box.int().tolist()
_UpperCAmelCase = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 657 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase : Dict = mock.Mock()
UpperCAmelCase : List[str] = 500
UpperCAmelCase : List[str] = {}
UpperCAmelCase : Union[str, Any] = HTTPError
UpperCAmelCase : Any = {}
# Download this model to make sure it's in the cache.
UpperCAmelCase : int = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=lowercase_ ) as mock_head:
UpperCAmelCase : Any = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
UpperCAmelCase : Any = mock.Mock()
UpperCAmelCase : Dict = 500
UpperCAmelCase : str = {}
UpperCAmelCase : Tuple = HTTPError
UpperCAmelCase : Optional[int] = {}
# Download this model to make sure it's in the cache.
UpperCAmelCase : int = GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=lowercase_ ) as mock_head:
UpperCAmelCase : Union[str, Any] = GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
try:
UpperCAmelCase : Dict = tempfile.mktemp()
with open(lowercase_ , 'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' , lowercase_ )
UpperCAmelCase : Any = AlbertTokenizer.from_pretrained(lowercase_ )
finally:
os.remove(lowercase_ )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' , 'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' , lowercase_ )
UpperCAmelCase : int = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def UpperCAmelCase_ ( self : int ) -> str:
UpperCAmelCase : Optional[int] = AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class A_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def UpperCAmelCase_ ( cls : str ) -> List[Any]:
UpperCAmelCase : str = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def UpperCAmelCase_ ( cls : List[str] ) -> Dict:
try:
delete_repo(token=cls._token , repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : str = os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCAmelCase : List[Any] = BertTokenizer(lowercase_ )
tokenizer.push_to_hub('test-tokenizer' , use_auth_token=self._token )
UpperCAmelCase : Optional[Any] = BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowercase_ , repo_id='test-tokenizer' , push_to_hub=lowercase_ , use_auth_token=self._token )
UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def UpperCAmelCase_ ( self : int ) -> Any:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Union[str, Any] = os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCAmelCase : Optional[Any] = BertTokenizer(lowercase_ )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' , use_auth_token=self._token )
UpperCAmelCase : Union[str, Any] = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
lowercase_ , repo_id='valid_org/test-tokenizer-org' , push_to_hub=lowercase_ , use_auth_token=self._token )
UpperCAmelCase : Dict = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Dict = os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCAmelCase : List[str] = CustomTokenizer(lowercase_ )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
UpperCAmelCase : Any = AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer""" , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : int = os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCAmelCase : str = BertTokenizerFast.from_pretrained(lowercase_ )
bert_tokenizer.save_pretrained(lowercase_ )
UpperCAmelCase : Union[str, Any] = CustomTokenizerFast.from_pretrained(lowercase_ )
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer""" , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizerFast' )
UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(
f"""{USER}/test-dynamic-tokenizer""" , use_fast=lowercase_ , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
UpperCAmelCase : Optional[Any] = Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS]', ' This is a ', 'extra_id_100'] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) , ['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) , ['BC', 'A'] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
UpperCAmelCase : int = Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
UpperCAmelCase : Any = Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
UpperCAmelCase : str = Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) , ['AB', 'C'] )
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
UpperCAmelCase : Any = Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) , ['ABC', 'D'] )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
UpperCAmelCase : Any = Trie()
UpperCAmelCase : str = trie.cut_text('ABC' , [0, 0, 2, 1, 2, 3] )
self.assertEqual(lowercase_ , ['AB', 'C'] )
| 703 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowercase__ = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
lowercase__ = {
"gpt-neox-20b": 2048,
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = VOCAB_FILES_NAMES
UpperCAmelCase_ : str = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowercase_ : Any=None , lowercase_ : Dict=None , lowercase_ : List[str]=None , lowercase_ : List[Any]="<|endoftext|>" , lowercase_ : List[str]="<|endoftext|>" , lowercase_ : Any="<|endoftext|>" , lowercase_ : List[str]=False , **lowercase_ : Union[str, Any] , ) -> str:
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
UpperCAmelCase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowercase_ ) != add_prefix_space:
UpperCAmelCase : Tuple = getattr(lowercase_ , pre_tok_state.pop('type' ) )
UpperCAmelCase : Optional[Any] = add_prefix_space
UpperCAmelCase : Tuple = pre_tok_class(**lowercase_ )
UpperCAmelCase : Any = add_prefix_space
def UpperCAmelCase_ ( self : Tuple , lowercase_ : str , lowercase_ : Optional[str] = None ) -> Tuple[str]:
UpperCAmelCase : Optional[int] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : "Conversation" ) -> List[int]:
UpperCAmelCase : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase_ , add_special_tokens=lowercase_ ) + [self.eos_token_id] )
if len(lowercase_ ) > self.model_max_length:
UpperCAmelCase : int = input_ids[-self.model_max_length :]
return input_ids
| 695 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : str ) -> Dict:
"""simple docstring"""
__lowercase = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
__lowercase = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 1_6000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
__lowercase = tempfile.mkdtemp()
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(self.tmpdirname , _lowerCAmelCase )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
with open(self.feature_extraction_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
# load decoder from hub
__lowercase = """hf-internal-testing/ngram-beam-search-decoder"""
def _a ( self : List[Any] , **_lowerCAmelCase : int ) -> int:
"""simple docstring"""
__lowercase = self.add_kwargs_tokens_map.copy()
kwargs.update(_lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self : str , **_lowerCAmelCase : List[Any] ) -> Dict:
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self : int , **_lowerCAmelCase : List[Any] ) -> int:
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **_lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.get_tokenizer()
__lowercase = self.get_feature_extractor()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
__lowercase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , _lowerCAmelCase )
def _a ( self : str ) -> Dict:
"""simple docstring"""
__lowercase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
__lowercase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def _a ( self : int ) -> Any:
"""simple docstring"""
__lowercase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(_lowerCAmelCase , """include""" ):
WavaVecaProcessorWithLM(
tokenizer=_lowerCAmelCase , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = floats_list((3, 1000) )
__lowercase = feature_extractor(_lowerCAmelCase , return_tensors="""np""" )
__lowercase = processor(_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 _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = """This is a test string"""
__lowercase = processor(text=_lowerCAmelCase )
__lowercase = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : Optional[int] , _lowerCAmelCase : Tuple=(2, 10, 16) , _lowerCAmelCase : Any=77 ) -> Dict:
"""simple docstring"""
np.random.seed(_lowerCAmelCase )
return np.random.rand(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
__lowercase = processor.decode(_lowerCAmelCase )
__lowercase = decoder.decode_beams(_lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def _a ( self : List[Any] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
__lowercase = processor.batch_decode(_lowerCAmelCase )
else:
with get_context(_lowerCAmelCase ).Pool() as pool:
__lowercase = processor.batch_decode(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as p:
__lowercase = decoder.decode_beams_batch(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(_lowerCAmelCase , decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] , decoded_processor.text )
self.assertListEqual(_lowerCAmelCase , decoded_processor.logit_score )
self.assertListEqual(_lowerCAmelCase , decoded_processor.lm_score )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = self._get_dummy_logits()
__lowercase = 15
__lowercase = -20.0
__lowercase = -4.0
__lowercase = processor.batch_decode(
_lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
__lowercase = decoded_processor_out.text
__lowercase = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as pool:
__lowercase = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
__lowercase = [d[0][0] for d in decoded_decoder_out]
__lowercase = [d[0][2] for d in decoded_decoder_out]
__lowercase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] , _lowerCAmelCase )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , _lowerCAmelCase , atol=1e-3 ) )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9_474] , _lowerCAmelCase , atol=1e-3 ) )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
__lowercase = self._get_dummy_logits()
__lowercase = 2.0
__lowercase = 5.0
__lowercase = -20.0
__lowercase = True
__lowercase = processor.batch_decode(
_lowerCAmelCase , alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
__lowercase = decoded_processor_out.text
__lowercase = list(_lowerCAmelCase )
decoder.reset_params(
alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
with get_context("""fork""" ).Pool() as pool:
__lowercase = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , )
__lowercase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] , _lowerCAmelCase )
__lowercase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , _lowerCAmelCase )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__lowercase = processor.decoder.model_container[processor.decoder._model_key]
__lowercase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
__lowercase = os.listdir(_lowerCAmelCase )
__lowercase = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = snapshot_download("""hf-internal-testing/processor_with_lm""" )
__lowercase = WavaVecaProcessorWithLM.from_pretrained(_lowerCAmelCase )
__lowercase = processor.decoder.model_container[processor.decoder._model_key]
__lowercase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
__lowercase = os.listdir(_lowerCAmelCase )
__lowercase = os.listdir(_lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__lowercase = floats_list((3, 1000) )
__lowercase = processor_wavaveca(_lowerCAmelCase , return_tensors="""np""" )
__lowercase = processor_auto(_lowerCAmelCase , return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1e-2 )
__lowercase = self._get_dummy_logits()
__lowercase = processor_wavaveca.batch_decode(_lowerCAmelCase )
__lowercase = processor_auto.batch_decode(_lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.get_feature_extractor()
__lowercase = self.get_tokenizer()
__lowercase = self.get_decoder()
__lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
@staticmethod
def _a ( _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = [d[key] for d in offsets]
return retrieved_list
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
__lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__lowercase = self._get_dummy_logits()[0]
__lowercase = processor.decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """end_offset""" ) , [1, 3, 5] )
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
__lowercase = self._get_dummy_logits()
__lowercase = processor.batch_decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) for o in outputs["""word_offsets"""]] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """end_offset""" ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
import torch
__lowercase = load_dataset("""common_voice""" , """en""" , split="""train""" , streaming=_lowerCAmelCase )
__lowercase = ds.cast_column("""audio""" , datasets.Audio(sampling_rate=1_6000 ) )
__lowercase = iter(_lowerCAmelCase )
__lowercase = next(_lowerCAmelCase )
__lowercase = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
__lowercase = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
__lowercase = processor(sample["""audio"""]["""array"""] , return_tensors="""pt""" ).input_values
with torch.no_grad():
__lowercase = model(_lowerCAmelCase ).logits.cpu().numpy()
__lowercase = processor.decode(logits[0] , output_word_offsets=_lowerCAmelCase )
__lowercase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__lowercase = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
__lowercase = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , _lowerCAmelCase )
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , output.text )
# output times
__lowercase = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """start_time""" ) )
__lowercase = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """end_time""" ) )
# fmt: off
__lowercase = torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599] )
__lowercase = torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
| 80 |
def __UpperCamelCase ( lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
while b:
lowerCAmelCase_ , lowerCAmelCase_ : int = b, a % b
return a
def __UpperCamelCase ( lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive(lowercase__ , a % b )
def __UpperCamelCase ( ) -> Dict:
'''simple docstring'''
print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}' )
print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}' )
print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}' )
print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}' )
print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}' )
print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}' )
print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}' )
print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}' )
if __name__ == "__main__":
main()
| 600 | 0 |
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 UpperCAmelCase_ ( snake_case__="" ) -> str:
"""simple docstring"""
lowerCAmelCase__ = tempfile.mkdtemp()
return os.path.join(snake_case__ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class __snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = torch.rand(12 ,dtype=torch.floataa ) - 0.5
lowerCAmelCase__ = AgentAudio(a_ )
lowerCAmelCase__ = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(a_ ,agent_type.to_raw() ,atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(a_ ) )
# Ensure that the file contains the same value as the original tensor
lowerCAmelCase__ , lowerCAmelCase__ = sf.read(a_ )
self.assertTrue(torch.allclose(a_ ,torch.tensor(a_ ) ,atol=1e-4 ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = torch.rand(12 ,dtype=torch.floataa ) - 0.5
lowerCAmelCase__ = get_new_path(suffix='.wav' )
sf.write(a_ ,a_ ,1_6000 )
lowerCAmelCase__ = AgentAudio(a_ )
self.assertTrue(torch.allclose(a_ ,agent_type.to_raw() ,atol=1e-4 ) )
self.assertEqual(agent_type.to_string() ,a_ )
@require_vision
@require_torch
class __snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = torch.randint(0 ,256 ,(64, 64, 3) )
lowerCAmelCase__ = AgentImage(a_ )
lowerCAmelCase__ = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(a_ ,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(a_ ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png'
lowerCAmelCase__ = Image.open(a_ )
lowerCAmelCase__ = AgentImage(a_ )
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(a_ ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png'
lowerCAmelCase__ = Image.open(a_ )
lowerCAmelCase__ = AgentImage(a_ )
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(a_ ) )
class __snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = 'Hey!'
lowerCAmelCase__ = AgentText(a_ )
self.assertEqual(a_ ,agent_type.to_string() )
self.assertEqual(a_ ,agent_type.to_raw() )
self.assertEqual(a_ ,a_ )
| 712 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
_lowerCAmelCase : Optional[Any] = {
"vocab_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
},
"merges_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
},
"tokenizer_config_file": {
"facebook/blenderbot_small-90M": (
"https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
)
},
}
_lowerCAmelCase : str = {"facebook/blenderbot_small-90M": 5_1_2}
def UpperCAmelCase_ ( snake_case__ ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ = set()
lowerCAmelCase__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ = char
lowerCAmelCase__ = set(snake_case__ )
return pairs
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['input_ids', 'attention_mask']
def __init__( self ,a_ ,a_ ,a_="__start__" ,a_="__end__" ,a_="__unk__" ,a_="__null__" ,**a_ ,):
"""simple docstring"""
super().__init__(unk_token=a_ ,bos_token=a_ ,eos_token=a_ ,pad_token=a_ ,**a_ )
with open(a_ ,encoding='utf-8' ) as vocab_handle:
lowerCAmelCase__ = json.load(a_ )
lowerCAmelCase__ = {v: k for k, v in self.encoder.items()}
with open(a_ ,encoding='utf-8' ) as merges_handle:
lowerCAmelCase__ = merges_handle.read().split('\n' )[1:-1]
lowerCAmelCase__ = [tuple(merge.split() ) for merge in merges]
lowerCAmelCase__ = dict(zip(a_ ,range(len(a_ ) ) ) )
lowerCAmelCase__ = {}
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return len(self.encoder )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return dict(self.encoder ,**self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ = re.sub('([.,!?()])' ,r' \1' ,a_ )
lowerCAmelCase__ = re.sub('(\')' ,r' \1 ' ,a_ )
lowerCAmelCase__ = re.sub(r'\s{2,}' ,' ' ,a_ )
if "\n" in token:
lowerCAmelCase__ = token.replace('\n' ,' __newln__' )
lowerCAmelCase__ = token.split(' ' )
lowerCAmelCase__ = []
for token in tokens:
if not len(a_ ):
continue
lowerCAmelCase__ = token.lower()
lowerCAmelCase__ = tuple(a_ )
lowerCAmelCase__ = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
lowerCAmelCase__ = get_pairs(a_ )
if not pairs:
words.append(a_ )
continue
while True:
lowerCAmelCase__ = min(a_ ,key=lambda a_ : self.bpe_ranks.get(a_ ,float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ , lowerCAmelCase__ = bigram
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
while i < len(a_ ):
try:
lowerCAmelCase__ = word.index(a_ ,a_ )
new_word.extend(word[i:j] )
lowerCAmelCase__ = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(a_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ = tuple(a_ )
lowerCAmelCase__ = new_word
if len(a_ ) == 1:
break
else:
lowerCAmelCase__ = get_pairs(a_ )
lowerCAmelCase__ = '@@ '.join(a_ )
lowerCAmelCase__ = word[:-4]
lowerCAmelCase__ = word
words.append(a_ )
return " ".join(a_ )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = re.findall(r'\S+\n?' ,a_ )
for token in words:
split_tokens.extend(list(self.bpe(a_ ).split(' ' ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = token.lower()
return self.encoder.get(a_ ,self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
return self.decoder.get(a_ ,self.unk_token )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = ' '.join(a_ ).replace('@@ ' ,'' ).strip()
return out_string
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ = None ):
"""simple docstring"""
if not os.path.isdir(a_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ = os.path.join(
a_ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase__ = os.path.join(
a_ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(a_ ,'w' ,encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=a_ ,ensure_ascii=a_ ) + '\n' )
lowerCAmelCase__ = 0
with open(a_ ,'w' ,encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda a_ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
' Please check that the tokenizer is not corrupted!' )
lowerCAmelCase__ = token_index
writer.write(' '.join(a_ ) + '\n' )
index += 1
return vocab_file, merge_file
| 604 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Tuple , a_ : Any , a_ : Dict=13 , a_ : Optional[Any]=[30, 30] , a_ : List[Any]=2 , a_ : int=3 , a_ : Union[str, Any]=True , a_ : Optional[Any]=True , a_ : Optional[Any]=32 , a_ : Union[str, Any]=5 , a_ : Union[str, Any]=4 , a_ : Tuple=37 , a_ : str="gelu" , a_ : Optional[Any]=0.1 , a_ : List[Any]=0.1 , a_ : List[Any]=10 , a_ : List[str]=0.02 , a_ : Tuple=3 , a_ : Any=None , a_ : Optional[Any]=8 , a_ : Union[str, Any]=10 , ):
lowerCAmelCase_ : Tuple = parent
lowerCAmelCase_ : List[str] = batch_size
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : Tuple = patch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : int = is_training
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Tuple = num_attention_heads
lowerCAmelCase_ : List[str] = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : Tuple = hidden_dropout_prob
lowerCAmelCase_ : Union[str, Any] = attention_probs_dropout_prob
lowerCAmelCase_ : Dict = type_sequence_label_size
lowerCAmelCase_ : Optional[Any] = initializer_range
lowerCAmelCase_ : List[str] = num_labels
lowerCAmelCase_ : int = scope
lowerCAmelCase_ : Tuple = n_targets
lowerCAmelCase_ : Optional[int] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowerCAmelCase_ : Tuple = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowerCAmelCase_ : int = num_patches + 1 + self.num_detection_tokens
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowerCAmelCase_ : Any = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowerCAmelCase_ : Dict = []
for i in range(self.batch_size ):
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[Any] = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=_UpperCAmelCase )
lowerCAmelCase_ : List[Any] = torch.rand(self.n_targets , 4 , device=_UpperCAmelCase )
labels.append(_UpperCAmelCase )
lowerCAmelCase_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self : Optional[int] ):
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def lowerCamelCase ( self : Optional[int] , a_ : List[Any] , a_ : Dict , a_ : Optional[int] ):
lowerCAmelCase_ : int = YolosModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowerCAmelCase_ : Dict = model(_UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def lowerCamelCase ( self : Optional[int] , a_ : Tuple , a_ : Union[str, Any] , a_ : Optional[Any] ):
lowerCAmelCase_ : Union[str, Any] = YolosForObjectDetection(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowerCAmelCase_ : int = model(pixel_values=_UpperCAmelCase )
lowerCAmelCase_ : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowerCAmelCase_ : List[str] = model(pixel_values=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ : int = config_and_inputs
lowerCAmelCase_ : int = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( _a , _a , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[int] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
a_ : Any = (
{"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
)
a_ : Tuple = False
a_ : Optional[Any] = False
a_ : List[str] = False
a_ : Union[str, Any] = False
def lowerCamelCase ( self : List[Any] , a_ : Optional[int] , a_ : Optional[int] , a_ : List[str]=False ):
lowerCAmelCase_ : Dict = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowerCAmelCase_ : List[Any] = []
for i in range(self.model_tester.batch_size ):
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : str = torch.ones(
size=(self.model_tester.n_targets,) , device=_UpperCAmelCase , dtype=torch.long )
lowerCAmelCase_ : List[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=_UpperCAmelCase , dtype=torch.float )
labels.append(_UpperCAmelCase )
lowerCAmelCase_ : int = labels
return inputs_dict
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : str = YolosModelTester(self )
lowerCAmelCase_ : int = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self : Tuple ):
self.config_tester.run_common_tests()
def lowerCamelCase ( self : Optional[int] ):
pass
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : str = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Any = model_class(_UpperCAmelCase )
lowerCAmelCase_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : int = [*signature.parameters.keys()]
lowerCAmelCase_ : Optional[int] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : List[Any] = True
# in YOLOS, the seq_len is different
lowerCAmelCase_ : List[Any] = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[Any] = False
lowerCAmelCase_ : int = True
lowerCAmelCase_ : List[str] = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowerCAmelCase_ : Any = 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"]
lowerCAmelCase_ : str = True
lowerCAmelCase_ : int = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : int = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowerCAmelCase_ : Tuple = 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] , )
lowerCAmelCase_ : List[str] = len(_UpperCAmelCase )
# Check attention is always last and order is fine
lowerCAmelCase_ : Any = True
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Tuple = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : int = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowerCAmelCase_ : str = 1
self.assertEqual(out_len + added_hidden_states , len(_UpperCAmelCase ) )
lowerCAmelCase_ : List[Any] = 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 lowerCamelCase ( self : int ):
def check_hidden_states_output(a_ : Optional[int] , a_ : Tuple , a_ : Optional[int] ):
lowerCAmelCase_ : Optional[int] = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowerCAmelCase_ : Dict = outputs.hidden_states
lowerCAmelCase_ : Tuple = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
# YOLOS has a different seq_length
lowerCAmelCase_ : Dict = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : int = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase )
@slow
def lowerCamelCase ( self : Dict ):
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Any = YolosModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase ( self : Optional[Any] ):
return AutoImageProcessor.from_pretrained("hustvl/yolos-small" ) if is_vision_available() else None
@slow
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : List[Any] = YolosForObjectDetection.from_pretrained("hustvl/yolos-small" ).to(_UpperCAmelCase )
lowerCAmelCase_ : List[Any] = self.default_image_processor
lowerCAmelCase_ : List[str] = prepare_img()
lowerCAmelCase_ : Optional[Any] = image_processor(images=_UpperCAmelCase , return_tensors="pt" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : Any = model(inputs.pixel_values )
# verify outputs
lowerCAmelCase_ : Union[str, Any] = torch.Size((1, 1_00, 92) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowerCAmelCase_ : str = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=_UpperCAmelCase , )
lowerCAmelCase_ : Tuple = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , _UpperCAmelCase , atol=1e-4 ) )
# verify postprocessing
lowerCAmelCase_ : Tuple = image_processor.post_process_object_detection(
_UpperCAmelCase , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowerCAmelCase_ : int = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(_UpperCAmelCase )
lowerCAmelCase_ : List[Any] = [75, 75, 17, 63, 17]
lowerCAmelCase_ : Dict = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(_UpperCAmelCase )
self.assertEqual(len(results["scores"] ) , 5 )
self.assertTrue(torch.allclose(results["scores"] , _UpperCAmelCase , atol=1e-4 ) )
self.assertSequenceEqual(results["labels"].tolist() , _UpperCAmelCase )
self.assertTrue(torch.allclose(results["boxes"][0, :] , _UpperCAmelCase ) )
| 610 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = prime_factors(UpperCamelCase_ )
if is_square_free(UpperCamelCase_ ):
return -1 if len(UpperCamelCase_ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 429 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class lowercase :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=6 , lowercase=17 , lowercase=23 , lowercase=11 , lowercase=True , ) -> List[str]:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = act_dim
lowerCAmelCase = state_dim
lowerCAmelCase = hidden_size
lowerCAmelCase = max_length
lowerCAmelCase = is_training
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowerCAmelCase = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowerCAmelCase = floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCAmelCase = floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCAmelCase = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
lowerCAmelCase = random_attention_mask((self.batch_size, self.seq_length) )
lowerCAmelCase = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _snake_case ( self ) -> Optional[int]:
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Any:
lowerCAmelCase = DecisionTransformerModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCAmelCase = model(lowercase , lowercase , lowercase , lowercase , lowercase , lowercase )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def _snake_case ( self ) -> str:
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
"""states""": states,
"""actions""": actions,
"""rewards""": rewards,
"""returns_to_go""": returns_to_go,
"""timesteps""": timesteps,
"""attention_mask""": attention_mask,
}
return config, inputs_dict
@require_torch
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = (DecisionTransformerModel,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE = ()
_SCREAMING_SNAKE_CASE = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
_SCREAMING_SNAKE_CASE = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def _snake_case ( self ) -> str:
lowerCAmelCase = DecisionTransformerModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def _snake_case ( self ) -> List[str]:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
@slow
def _snake_case ( self ) -> Dict:
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = DecisionTransformerModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(lowercase )
lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = [
"""states""",
"""actions""",
"""rewards""",
"""returns_to_go""",
"""timesteps""",
"""attention_mask""",
]
self.assertListEqual(arg_names[: len(lowercase )] , lowercase )
@require_torch
class lowercase ( unittest.TestCase ):
@slow
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = 2 # number of steps of autoregressive prediction we will perform
lowerCAmelCase = 10 # defined by the RL environment, may be normalized
lowerCAmelCase = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" )
lowerCAmelCase = model.to(lowercase )
lowerCAmelCase = model.config
torch.manual_seed(0 )
lowerCAmelCase = torch.randn(1 , 1 , config.state_dim ).to(device=lowercase , dtype=torch.floataa ) # env.reset()
lowerCAmelCase = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] , device=lowercase )
lowerCAmelCase = torch.tensor(lowercase , device=lowercase , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowerCAmelCase = state
lowerCAmelCase = torch.zeros(1 , 0 , config.act_dim , device=lowercase , dtype=torch.floataa )
lowerCAmelCase = torch.zeros(1 , 0 , device=lowercase , dtype=torch.floataa )
lowerCAmelCase = torch.tensor(0 , device=lowercase , dtype=torch.long ).reshape(1 , 1 )
for step in range(lowercase ):
lowerCAmelCase = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=lowercase )] , dim=1 )
lowerCAmelCase = torch.cat([rewards, torch.zeros(1 , 1 , device=lowercase )] , dim=1 )
lowerCAmelCase = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = model(
states=lowercase , actions=lowercase , rewards=lowercase , returns_to_go=lowercase , timesteps=lowercase , attention_mask=lowercase , return_dict=lowercase , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=lowercase , dtype=torch.floataa ),
1.0,
False,
{},
)
lowerCAmelCase = action_pred[0, -1]
lowerCAmelCase = torch.cat([states, state] , dim=1 )
lowerCAmelCase = returns_to_go[0, -1] - reward
lowerCAmelCase = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowerCAmelCase = torch.cat(
[timesteps, torch.ones((1, 1) , device=lowercase , dtype=torch.long ) * (step + 1)] , dim=1 )
| 393 |
"""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,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase ( unittest.TestCase ):
def _snake_case ( self ) -> int:
lowerCAmelCase = tempfile.mkdtemp()
lowerCAmelCase = BlipImageProcessor()
lowerCAmelCase = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
lowerCAmelCase = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
lowerCAmelCase = InstructBlipProcessor(lowercase , lowercase , lowercase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self , **lowercase ) -> Dict:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).tokenizer
def _snake_case ( self , **lowercase ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).image_processor
def _snake_case ( self , **lowercase ) -> Any:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).qformer_tokenizer
def _snake_case ( self ) -> str:
shutil.rmtree(self.tmpdirname )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase = [Image.fromarray(np.moveaxis(lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self ) -> Any:
lowerCAmelCase = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase = self.get_image_processor(do_normalize=lowercase , padding_value=1.0 )
lowerCAmelCase = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowercase )
self.assertIsInstance(processor.qformer_tokenizer , lowercase )
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_qformer_tokenizer()
lowerCAmelCase = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = image_processor(lowercase , return_tensors="""np""" )
lowerCAmelCase = processor(images=lowercase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self ) -> Any:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_qformer_tokenizer()
lowerCAmelCase = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = processor(text=lowercase )
lowerCAmelCase = tokenizer(lowercase , return_token_type_ids=lowercase )
lowerCAmelCase = qformer_tokenizer(lowercase , return_token_type_ids=lowercase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def _snake_case ( self ) -> Any:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_qformer_tokenizer()
lowerCAmelCase = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = processor(text=lowercase , images=lowercase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(lowercase ):
processor()
def _snake_case ( self ) -> str:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_qformer_tokenizer()
lowerCAmelCase = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase = processor.batch_decode(lowercase )
lowerCAmelCase = tokenizer.batch_decode(lowercase )
self.assertListEqual(lowercase , lowercase )
def _snake_case ( self ) -> int:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_qformer_tokenizer()
lowerCAmelCase = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = processor(text=lowercase , images=lowercase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 393 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}
class _UpperCamelCase (a_ ):
snake_case_ = """xlnet"""
snake_case_ = ["""mems"""]
snake_case_ = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __UpperCamelCase=3_2_0_0_0 , __UpperCamelCase=1_0_2_4 , __UpperCamelCase=2_4 , __UpperCamelCase=1_6 , __UpperCamelCase=4_0_9_6 , __UpperCamelCase="gelu" , __UpperCamelCase=True , __UpperCamelCase="bi" , __UpperCamelCase=0.0_2 , __UpperCamelCase=1e-12 , __UpperCamelCase=0.1 , __UpperCamelCase=5_1_2 , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=-1 , __UpperCamelCase=False , __UpperCamelCase="last" , __UpperCamelCase=True , __UpperCamelCase="tanh" , __UpperCamelCase=0.1 , __UpperCamelCase=5 , __UpperCamelCase=5 , __UpperCamelCase=5 , __UpperCamelCase=1 , __UpperCamelCase=2 , **__UpperCamelCase , )-> Any:
__lowerCAmelCase = vocab_size
__lowerCAmelCase = d_model
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
if d_model % n_head != 0:
raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})""" )
__lowerCAmelCase = d_model // n_head
__lowerCAmelCase = ff_activation
__lowerCAmelCase = d_inner
__lowerCAmelCase = untie_r
__lowerCAmelCase = attn_type
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = dropout
__lowerCAmelCase = mem_len
__lowerCAmelCase = reuse_len
__lowerCAmelCase = bi_data
__lowerCAmelCase = clamp_len
__lowerCAmelCase = same_length
__lowerCAmelCase = summary_type
__lowerCAmelCase = summary_use_proj
__lowerCAmelCase = summary_activation
__lowerCAmelCase = summary_last_dropout
__lowerCAmelCase = start_n_top
__lowerCAmelCase = end_n_top
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = pad_token_id
__lowerCAmelCase = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __UpperCamelCase , )
__lowerCAmelCase = kwargs["use_cache"]
__lowerCAmelCase = use_mems_eval
__lowerCAmelCase = use_mems_train
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
@property
def __UpperCAmelCase ( self )-> List[Any]:
logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def __UpperCAmelCase ( self , __UpperCamelCase )-> List[Any]:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 367 |
from __future__ import annotations
import os
from typing import Any
import requests
lowerCamelCase : Tuple = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCamelCase : int = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowerCamelCase : Any = os.environ.get('''USER_TOKEN''', '''''')
def __lowerCAmelCase ( __snake_case ):
__lowerCAmelCase = {
"Authorization": F"""token {auth_token}""",
"Accept": "application/vnd.github.v3+json",
}
return requests.get(__snake_case , headers=__snake_case ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(F'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 367 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a_ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'
a_ = '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'
a_ = R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/hendrycks/math''' , codebase_urls=['''https://github.com/hendrycks/math'''] , )
def __magic_name__ ( self : int , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =0.0
for i, j in zip(__lowercase , __lowercase ):
n_correct += 1.0 if math_equivalence.is_equiv(__lowercase , __lowercase ) else 0.0
SCREAMING_SNAKE_CASE__ : str =n_correct / len(__lowercase )
return {
"accuracy": accuracy,
} | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = SpeechTaTokenizer
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : int ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[Any] =SpeechTaTokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken('''<mask>''' , lstrip=__lowercase , rstrip=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''this is a test'''
SCREAMING_SNAKE_CASE__ : int ='''this is a test'''
return input_text, output_text
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Optional[Any]=False , __lowercase : Union[str, Any]=20 , __lowercase : Any=5 ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.get_input_output_texts(__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def __magic_name__ ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''<pad>'''
SCREAMING_SNAKE_CASE__ : Optional[int] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__lowercase ) , 81 )
def __magic_name__ ( self : Dict ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.get_tokenizers(do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Any =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : int =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.add_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : str ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE__ : int =tokenizer.add_special_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size_a + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __magic_name__ ( self : Optional[Any] ) -> Any:
pass
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__lowercase , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__lowercase )
# fmt: off
self.assertListEqual(__lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def __magic_name__ ( self : List[str] ) -> List[str]:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__lowercase , ) | 665 | 0 |
"""simple docstring"""
def _lowerCamelCase( a , a ):
__a = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__a = n - k
# Calculate C(n,k)
for i in range(a ):
result *= n - i
result //= i + 1
return result
def _lowerCamelCase( a ):
return binomial_coefficient(2 * node_count , a ) // (node_count + 1)
def _lowerCamelCase( a ):
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__a = 1
for i in range(1 , n + 1 ):
result *= i
return result
def _lowerCamelCase( a ):
return catalan_number(a ) * factorial(a )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:Any = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 528 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE__:Tuple = logging.get_logger(__name__)
def _lowerCamelCase( a ):
if isinstance(a , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(a , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(a ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class snake_case__ ( snake_case_ ):
_snake_case : List[Any] = ["""pixel_values"""]
def __init__( self , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = PILImageResampling.BILINEAR , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = True , lowerCamelCase = 1 / 255 , lowerCamelCase = True , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = None , **lowerCamelCase , ):
super().__init__(**lowerCamelCase )
__a = size if size is not None else {"shortest_edge": 256}
__a = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
__a = crop_size if crop_size is not None else {"height": 224, "width": 224}
__a = get_size_dict(lowerCamelCase , param_name="crop_size" )
__a = do_resize
__a = size
__a = do_center_crop
__a = crop_size
__a = resample
__a = do_rescale
__a = rescale_factor
__a = offset
__a = do_normalize
__a = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__a = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = PILImageResampling.BILINEAR , lowerCamelCase = None , **lowerCamelCase , ):
__a = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
if "shortest_edge" in size:
__a = get_resize_output_image_size(lowerCamelCase , size["shortest_edge"] , default_to_square=lowerCamelCase )
elif "height" in size and "width" in size:
__a = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , **lowerCamelCase , ):
__a = get_size_dict(lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(lowerCamelCase , size=(size["height"], size["width"]) , data_format=lowerCamelCase , **lowerCamelCase )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = True , lowerCamelCase = None , **lowerCamelCase , ):
__a = image.astype(np.floataa )
if offset:
__a = image - (scale / 2)
return rescale(lowerCamelCase , scale=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , **lowerCamelCase , ):
return normalize(lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def a__ ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
__a = to_numpy_array(lowerCamelCase )
if do_resize:
__a = self.resize(image=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase )
if do_center_crop:
__a = self.center_crop(lowerCamelCase , size=lowerCamelCase )
if do_rescale:
__a = self.rescale(image=lowerCamelCase , scale=lowerCamelCase , offset=lowerCamelCase )
if do_normalize:
__a = self.normalize(image=lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase )
__a = to_channel_dimension_format(lowerCamelCase , lowerCamelCase )
return image
def a__ ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = ChannelDimension.FIRST , **lowerCamelCase , ):
__a = do_resize if do_resize is not None else self.do_resize
__a = resample if resample is not None else self.resample
__a = do_center_crop if do_center_crop is not None else self.do_center_crop
__a = do_rescale if do_rescale is not None else self.do_rescale
__a = rescale_factor if rescale_factor is not None else self.rescale_factor
__a = offset if offset is not None else self.offset
__a = do_normalize if do_normalize is not None else self.do_normalize
__a = image_mean if image_mean is not None else self.image_mean
__a = image_std if image_std is not None else self.image_std
__a = size if size is not None else self.size
__a = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
__a = crop_size if crop_size is not None else self.crop_size
__a = get_size_dict(lowerCamelCase , param_name="crop_size" )
if not valid_images(lowerCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
__a = make_batched(lowerCamelCase )
__a = [
[
self._preprocess_image(
image=lowerCamelCase , do_resize=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , do_center_crop=lowerCamelCase , crop_size=lowerCamelCase , do_rescale=lowerCamelCase , rescale_factor=lowerCamelCase , offset=lowerCamelCase , do_normalize=lowerCamelCase , image_mean=lowerCamelCase , image_std=lowerCamelCase , data_format=lowerCamelCase , )
for img in video
]
for video in videos
]
__a = {"pixel_values": videos}
return BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
| 528 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase__ ( a , a , a , a = None , ):
__snake_case = np.shape(a )
__snake_case = np.shape(a )
__snake_case = np.shape(a )
if shape_a[0] != shape_b[0]:
__snake_case = (
'Expected the same number of rows for A and B. '
f'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(a )
if shape_b[1] != shape_c[1]:
__snake_case = (
'Expected the same number of columns for B and C. '
f'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(a )
__snake_case = pseudo_inv
if a_inv is None:
try:
__snake_case = np.linalg.inv(a )
except np.linalg.LinAlgError:
raise ValueError(
'Input matrix A is not invertible. Cannot compute Schur complement.' )
return mat_c - mat_b.T @ a_inv @ mat_b
class a_ ( unittest.TestCase ):
def lowercase__ ( self : int ):
__snake_case = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__snake_case = np.array([[0, 3], [3, 0], [2, 3]] )
__snake_case = np.array([[2, 1], [6, 3]] )
__snake_case = schur_complement(_lowercase , _lowercase , _lowercase )
__snake_case = np.block([[a, b], [b.T, c]] )
__snake_case = np.linalg.det(_lowercase )
__snake_case = np.linalg.det(_lowercase )
__snake_case = np.linalg.det(_lowercase )
self.assertAlmostEqual(_lowercase , det_a * det_s )
def lowercase__ ( self : Dict ):
__snake_case = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__snake_case = np.array([[0, 3], [3, 0], [2, 3]] )
__snake_case = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_lowercase ):
schur_complement(_lowercase , _lowercase , _lowercase )
def lowercase__ ( self : Dict ):
__snake_case = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__snake_case = np.array([[0, 3], [3, 0], [2, 3]] )
__snake_case = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_lowercase ):
schur_complement(_lowercase , _lowercase , _lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 707 |
'''simple docstring'''
import argparse
import copy
def lowerCamelCase__ ( a ):
__snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__snake_case = []
_list.append([line.split()[1], line.split()[2]] )
__snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
__snake_case = []
_list.append([line.split()[0], line.split()[2]] )
__snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCamelCase__ ( a , a ):
with open(a ) as f:
__snake_case = f.read(1 )
__snake_case = start_node
__snake_case = []
__snake_case = start_node
__snake_case = 0
while visiting not in first_solution:
__snake_case = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
__snake_case = k[1]
__snake_case = k[0]
first_solution.append(a )
__snake_case = distance_of_first_solution + int(a )
__snake_case = best_node
first_solution.append(a )
__snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
__snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def lowerCamelCase__ ( a , a ):
__snake_case = []
for n in solution[1:-1]:
__snake_case = solution.index(a )
for kn in solution[1:-1]:
__snake_case = solution.index(a )
if n == kn:
continue
__snake_case = copy.deepcopy(a )
__snake_case = kn
__snake_case = n
__snake_case = 0
for k in _tmp[:-1]:
__snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
__snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
__snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCamelCase__ ( a , a , a , a , a ):
__snake_case = 1
__snake_case = first_solution
__snake_case = []
__snake_case = distance_of_first_solution
__snake_case = solution
while count <= iters:
__snake_case = find_neighborhood(a , a )
__snake_case = 0
__snake_case = neighborhood[index_of_best_solution]
__snake_case = len(a ) - 1
__snake_case = False
while not found:
__snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
__snake_case = best_solution[i]
__snake_case = solution[i]
break
__snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
__snake_case = True
__snake_case = best_solution[:-1]
__snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
__snake_case = cost
__snake_case = solution
else:
__snake_case = index_of_best_solution + 1
__snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
__snake_case = count + 1
return best_solution_ever, best_cost
def lowerCamelCase__ ( a=None ):
__snake_case = generate_neighbours(args.File )
__snake_case , __snake_case = generate_first_solution(
args.File , a )
__snake_case , __snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f'Best solution: {best_sol}, with total distance: {best_cost}.' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 427 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =ProphetNetTokenizer
SCREAMING_SNAKE_CASE_ =False
def __a ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : int = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def __a ( self : Any , snake_case__ : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = "UNwant\u00E9d,running"
UpperCAmelCase__ : str = "unwanted, running"
return input_text, output_text
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase__ : List[Any] = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(__snake_case , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [9, 6, 7, 1_2, 1_0, 1_1] )
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def __a ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = BasicTokenizer(do_lower_case=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Any = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = BasicTokenizer(do_lower_case=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : int = BasicTokenizer(do_lower_case=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = BasicTokenizer(do_lower_case=__snake_case , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
UpperCAmelCase__ : Any = {}
for i, token in enumerate(__snake_case ):
UpperCAmelCase__ : Union[str, Any] = i
UpperCAmelCase__ : Any = WordpieceTokenizer(vocab=__snake_case , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
@require_torch
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )
UpperCAmelCase__ : List[str] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
UpperCAmelCase__ : List[Any] = [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
UpperCAmelCase__ : int = tokenizer(__snake_case , padding=__snake_case , return_tensors="pt" )
self.assertIsInstance(__snake_case , __snake_case )
UpperCAmelCase__ : Union[str, Any] = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__snake_case , __snake_case )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def __a ( self : Dict ):
'''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 ):
'''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] ):
'''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(" " ) )
@slow
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )
UpperCAmelCase__ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=__snake_case )
UpperCAmelCase__ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=__snake_case )
UpperCAmelCase__ : int = tokenizer.build_inputs_with_special_tokens(__snake_case )
UpperCAmelCase__ : Optional[int] = tokenizer.build_inputs_with_special_tokens(__snake_case , __snake_case )
assert encoded_sentence == text + [1_0_2]
assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]
| 438 |
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_rembert import RemBertTokenizer
else:
_lowerCamelCase = None
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}
_lowerCamelCase = {
'vocab_file': {
'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
},
'tokenizer_file': {
'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
},
}
_lowerCamelCase = {
'google/rembert': 2_56,
}
_lowerCamelCase = '▁'
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : Dict = VOCAB_FILES_NAMES
lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : List[Any] = RemBertTokenizer
def __init__( self : List[str] , __snake_case : int=None , __snake_case : str=None , __snake_case : Optional[int]=True , __snake_case : Dict=True , __snake_case : Optional[int]=False , __snake_case : Tuple="[CLS]" , __snake_case : Any="[SEP]" , __snake_case : Dict="<unk>" , __snake_case : List[str]="[SEP]" , __snake_case : Dict="<pad>" , __snake_case : str="[CLS]" , __snake_case : Union[str, Any]="[MASK]" , **__snake_case : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase_ = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token
super().__init__(
__snake_case , tokenizer_file=__snake_case , do_lower_case=__snake_case , remove_space=__snake_case , keep_accents=__snake_case , bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , pad_token=__snake_case , cls_token=__snake_case , mask_token=__snake_case , **__snake_case , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = False if not self.vocab_file else True
def lowerCamelCase_ ( self : str , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ):
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Union[str, Any] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1]
def lowerCamelCase_ ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ):
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 ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : List[str] , __snake_case : str , __snake_case : Optional[str] = None ):
if not os.path.isdir(__snake_case ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(__snake_case ) )
return
UpperCAmelCase_ = os.path.join(
__snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ):
copyfile(self.vocab_file , __snake_case )
return (out_vocab_file,)
| 144 | 0 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__magic_name__ = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
__magic_name__ = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
__magic_name__ = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
__magic_name__ = train_datagen.flow_from_directory(
"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
__magic_name__ = test_datagen.flow_from_directory(
"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
__magic_name__ = tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(64, 64)
)
__magic_name__ = tf.keras.preprocessing.image.img_to_array(test_image)
__magic_name__ = np.expand_dims(test_image, axis=0)
__magic_name__ = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
__magic_name__ = "Normal"
if result[0][0] == 1:
__magic_name__ = "Abnormality detected"
| 248 |
"""simple docstring"""
__magic_name__ = tuple[float, float, float]
__magic_name__ = tuple[float, float, float]
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = end_pointa[0] - end_pointa[0]
__SCREAMING_SNAKE_CASE = end_pointa[1] - end_pointa[1]
__SCREAMING_SNAKE_CASE = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = ab[1] * ac[2] - ab[2] * ac[1] # *i
__SCREAMING_SNAKE_CASE = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
__SCREAMING_SNAKE_CASE = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
return tuple(round(UpperCamelCase_ , UpperCamelCase_ ) for x in vector ) == (0, 0, 0)
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 10 ):
__SCREAMING_SNAKE_CASE = create_vector(UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = create_vector(UpperCamelCase_ , UpperCamelCase_ )
return is_zero_vector(get_ad_vectors_cross(UpperCamelCase_ , UpperCamelCase_ ) , UpperCamelCase_ )
| 248 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _lowercase ( __snake_case ) -> None:
__lowerCAmelCase , __lowerCAmelCase : Optional[Any] = analyze_text(__snake_case )
__lowerCAmelCase : str = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
__lowerCAmelCase : Tuple = sum(single_char_strings.values() )
# one length string
__lowerCAmelCase : Union[str, Any] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
__lowerCAmelCase : List[str] = single_char_strings[ch]
__lowerCAmelCase : Union[str, Any] = my_str / all_sum
my_fir_sum += prob * math.loga(__snake_case ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
__lowerCAmelCase : List[Any] = sum(two_char_strings.values() )
__lowerCAmelCase : str = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
__lowerCAmelCase : Union[str, Any] = cha + cha
if sequence in two_char_strings:
__lowerCAmelCase : List[str] = two_char_strings[sequence]
__lowerCAmelCase : Tuple = int(__snake_case ) / all_sum
my_sec_sum += prob * math.loga(__snake_case )
# print second entropy
print(F"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def _lowercase ( __snake_case ) -> tuple[dict, dict]:
__lowerCAmelCase : Optional[int] = Counter() # type: ignore
__lowerCAmelCase : Optional[Any] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 ,len(__snake_case ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def _lowercase ( ) -> Union[str, Any]:
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main() | 293 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__snake_case : Optional[Any] = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
__snake_case : int = dataset.iloc[:, 1:2].values
__snake_case : Tuple = dataset.iloc[:, 2].values
__snake_case , __snake_case , __snake_case , __snake_case : int = train_test_split(X, y, test_size=0.2, random_state=0)
__snake_case : Tuple = PolynomialFeatures(degree=4)
__snake_case : Any = poly_reg.fit_transform(X)
__snake_case : List[str] = LinearRegression()
pol_reg.fit(X_poly, y)
def _lowercase ( ) -> List[Any]:
plt.scatter(__snake_case ,__snake_case ,color="red" )
plt.plot(__snake_case ,pol_reg.predict(poly_reg.fit_transform(__snake_case ) ) ,color="blue" )
plt.title("Truth or Bluff (Linear Regression)" )
plt.xlabel("Position level" )
plt.ylabel("Salary" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003 | 293 | 1 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__a = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
__a = logging.WARNING
def a ( ):
'''simple docstring'''
lowercase_ = os.getenv('''DATASETS_VERBOSITY''' , snake_case__ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
F'''has to be one of: { ', '.join(log_levels.keys() ) }''' )
return _default_log_level
def a ( ):
'''simple docstring'''
return __name__.split('''.''' )[0]
def a ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def a ( ):
'''simple docstring'''
# Apply our default configuration to the library root logger.
lowercase_ = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def a ( ):
'''simple docstring'''
lowercase_ = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def a ( snake_case__: Tuple = None ):
'''simple docstring'''
if name is None:
lowercase_ = _get_library_name()
return logging.getLogger(snake_case__ )
def a ( ):
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def a ( snake_case__: List[str] ):
'''simple docstring'''
_get_library_root_logger().setLevel(snake_case__ )
def a ( ):
'''simple docstring'''
return set_verbosity(snake_case__ )
def a ( ):
'''simple docstring'''
return set_verbosity(snake_case__ )
def a ( ):
'''simple docstring'''
return set_verbosity(snake_case__ )
def a ( ):
'''simple docstring'''
return set_verbosity(snake_case__ )
def a ( ):
'''simple docstring'''
lowercase_ = False
def a ( ):
'''simple docstring'''
lowercase_ = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> int: # pylint: disable=unused-argument
lowercase_ = args[0] if args else None
def __iter__( self : Optional[Any] ) -> Any:
return iter(self._iterator )
def __getattr__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
def empty_fn(*SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : List[str] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : List[str] ) -> List[Any]:
return self
def __exit__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> str:
return
__a = True
class lowercase__:
"""simple docstring"""
def __call__( self : List[str] , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[Any]=False , **SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]:
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*lowerCamelCase_ , **lowerCamelCase_ )
else:
return EmptyTqdm(*lowerCamelCase_ , **lowerCamelCase_ )
def _lowercase ( self : List[Any] , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : int ) -> Dict:
lowercase_ = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowerCamelCase_ , **lowerCamelCase_ )
def _lowercase ( self : Optional[Any] ) -> Any:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__a = _tqdm_cls()
def a ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def a ( ):
'''simple docstring'''
global _tqdm_active
lowercase_ = True
def a ( ):
'''simple docstring'''
global _tqdm_active
lowercase_ = False
| 710 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__a = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase__( datasets.BuilderConfig ):
"""simple docstring"""
a :Optional[datasets.Features] = None
a :str = "utf-8"
a :Optional[str] = None
a :Optional[str] = None
a :bool = True # deprecated
a :Optional[int] = None # deprecated
a :int = 10 << 20 # 10MB
a :Optional[bool] = None
class lowercase__( datasets.ArrowBasedBuilder ):
"""simple docstring"""
a :Any = JsonConfig
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
lowercase_ = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowercase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(SCREAMING_SNAKE_CASE_ , (str, list, tuple) ):
lowercase_ = data_files
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = [files]
lowercase_ = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
lowercase_ = []
for split_name, files in data_files.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = [files]
lowercase_ = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_ ) for file in files]
splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_ , gen_kwargs={'''files''': files} ) )
return splits
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : pa.Table ) -> pa.Table:
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
lowercase_ = self.config.features.arrow_schema.field(SCREAMING_SNAKE_CASE_ ).type
lowercase_ = pa_table.append_column(SCREAMING_SNAKE_CASE_ , pa.array([None] * len(SCREAMING_SNAKE_CASE_ ) , type=SCREAMING_SNAKE_CASE_ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
lowercase_ = table_cast(SCREAMING_SNAKE_CASE_ , self.config.features.arrow_schema )
return pa_table
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Optional[int]:
for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(SCREAMING_SNAKE_CASE_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase_ = json.load(SCREAMING_SNAKE_CASE_ )
# We keep only the field we are interested in
lowercase_ = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ):
lowercase_ = set().union(*[row.keys() for row in dataset] )
lowercase_ = {col: [row.get(SCREAMING_SNAKE_CASE_ ) for row in dataset] for col in keys}
else:
lowercase_ = dataset
lowercase_ = pa.Table.from_pydict(SCREAMING_SNAKE_CASE_ )
yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_ )
# If the file has one json object per line
else:
with open(SCREAMING_SNAKE_CASE_ , '''rb''' ) as f:
lowercase_ = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
lowercase_ = max(self.config.chunksize // 3_2 , 1_6 << 1_0 )
lowercase_ = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
lowercase_ = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(SCREAMING_SNAKE_CASE_ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
lowercase_ = batch.decode(self.config.encoding , errors=SCREAMING_SNAKE_CASE_ ).encode('''utf-8''' )
try:
while True:
try:
lowercase_ = paj.read_json(
io.BytesIO(SCREAMING_SNAKE_CASE_ ) , read_options=paj.ReadOptions(block_size=SCREAMING_SNAKE_CASE_ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(SCREAMING_SNAKE_CASE_ , pa.ArrowInvalid )
and "straddling" not in str(SCREAMING_SNAKE_CASE_ )
or block_size > len(SCREAMING_SNAKE_CASE_ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(SCREAMING_SNAKE_CASE_ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
SCREAMING_SNAKE_CASE_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase_ = json.load(SCREAMING_SNAKE_CASE_ )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_ )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): # list is the only sequence type supported in JSON
try:
lowercase_ = set().union(*[row.keys() for row in dataset] )
lowercase_ = {col: [row.get(SCREAMING_SNAKE_CASE_ ) for row in dataset] for col in keys}
lowercase_ = pa.Table.from_pydict(SCREAMING_SNAKE_CASE_ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_ )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_ )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_ )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE_ )
batch_idx += 1
| 409 | 0 |
'''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
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
'''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__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'gpt_neo'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , snake_case_=5_02_57 , snake_case_=20_48 , snake_case_=20_48 , snake_case_=24 , snake_case_=[[["global", "local"], 12]] , snake_case_=16 , snake_case_=None , snake_case_=2_56 , snake_case_="gelu_new" , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_=True , snake_case_=5_02_56 , snake_case_=5_02_56 , **snake_case_ , ):
lowercase =vocab_size
lowercase =max_position_embeddings
lowercase =hidden_size
lowercase =num_layers
lowercase =num_heads
lowercase =intermediate_size
lowercase =window_size
lowercase =activation_function
lowercase =resid_dropout
lowercase =embed_dropout
lowercase =attention_dropout
lowercase =classifier_dropout
lowercase =layer_norm_epsilon
lowercase =initializer_range
lowercase =use_cache
lowercase =bos_token_id
lowercase =eos_token_id
lowercase =attention_types
lowercase =self.expand_attention_types_params(snake_case_ )
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=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
@staticmethod
def _A( snake_case_ ):
lowercase =[]
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : str , lowercase_ : str , lowercase_ : Optional[int] ) -> str:
'''simple docstring'''
import torch
lowercase =input.size()
lowercase =len(lowercase_ )
lowercase =shape[dimension]
lowercase =torch.arange(0 , lowercase_ , lowercase_ )
lowercase =torch.div(sizedim - size , lowercase_ , rounding_mode='''floor''' ) + 1
lowercase =torch.arange(lowercase_ ) + low_indices[:min_length][:, None]
lowercase =[slice(lowercase_ )] * rank
lowercase =indices
lowercase =input[s]
lowercase =list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(lowercase_ )
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
import torch
lowercase =torch.arange(1 , lowercase_ )
lowercase =torch.remainder(lowercase_ , lowercase_ )
lowercase =remainders == 0
lowercase =candidates[divisor_indices]
lowercase =torch.max(lowercase_ )
return largest_divisor, torch.div(lowercase_ , lowercase_ , rounding_mode='''floor''' )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
def _A( self ):
lowercase =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
lowercase ={0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def _A( self ):
return self._config.num_heads
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =super(snake_case_ , self ).generate_dummy_inputs(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
# We need to order the input in the way they appears in the forward()
lowercase =OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase =(
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(self.num_layers )
]
lowercase =common_inputs['''attention_mask''']
if self.use_past:
lowercase =ordered_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
return ordered_inputs
@property
def _A( self ):
return 13
| 72 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __UpperCamelCase ( UpperCamelCase ):
lowercase_ : Optional[Any] = """Salesforce/blip-image-captioning-base"""
lowercase_ : Optional[int] = (
"""This is a tool that generates a description of an image. It takes an input named `image` which should be the """
"""image to caption, and returns a text that contains the description in English."""
)
lowercase_ : int = """image_captioner"""
lowercase_ : int = AutoModelForVisionaSeq
lowercase_ : List[str] = ["""image"""]
lowercase_ : int = ["""text"""]
def __init__( self : Any , *UpperCAmelCase : str , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
requires_backends(self , ['vision'] )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase__ ( self : int , UpperCAmelCase : "Image" ) -> Union[str, Any]:
return self.pre_processor(images=UpperCAmelCase , return_tensors='pt' )
def UpperCAmelCase__ ( self : int , UpperCAmelCase : Optional[int] ) -> Optional[int]:
return self.model.generate(**UpperCAmelCase )
def UpperCAmelCase__ ( self : Dict , UpperCAmelCase : int ) -> Dict:
return self.pre_processor.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )[0].strip() | 553 | 0 |
from scipy.stats import spearmanr
import datasets
snake_case_ : Union[str, Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"
snake_case_ : List[Any] = "\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {'spearmanr': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results['spearmanr'])\n -0.7\n >>> print(round(results['spearmanr_pvalue'], 2))\n 0.19\n"
snake_case_ : Tuple = r"\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def lowerCamelCase ( self : str):
"""simple docstring"""
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.spearmanr.html'''] , )
def lowerCamelCase ( self : str , _snake_case : Optional[Any] , _snake_case : Optional[Any] , _snake_case : List[Any]=False):
"""simple docstring"""
UpperCAmelCase_ = spearmanr(_snake_case , _snake_case)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 169 |
def A (__A : int ) -> bool:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__A )
if number < 0:
return False
UpperCAmelCase_ = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 169 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : pyspark.sql.DataFrame , SCREAMING_SNAKE_CASE_ : Optional[NamedSplit] = None , SCREAMING_SNAKE_CASE_ : Optional[Features] = None , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : str = "arrow" , **SCREAMING_SNAKE_CASE_ : str , ) -> Optional[int]:
super().__init__(
split=SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ , streaming=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowercase_ = load_from_cache_file
lowercase_ = file_format
lowercase_ = Spark(
df=SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , working_dir=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def _lowercase ( self : int ) -> Optional[Any]:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowercase_ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=SCREAMING_SNAKE_CASE_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >= key:
lowercase_ = m
else:
lowercase_ = m # noqa: E741
return r
def a ( snake_case__: list[int] ):
'''simple docstring'''
if len(snake_case__ ) == 0:
return 0
lowercase_ = [0] * len(snake_case__ )
lowercase_ = 1
lowercase_ = v[0]
for i in range(1 , len(snake_case__ ) ):
if v[i] < tail[0]:
lowercase_ = v[i]
elif v[i] > tail[length - 1]:
lowercase_ = v[i]
length += 1
else:
lowercase_ = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 97 | 1 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__A ='scheduler_config.json'
class _snake_case ( a__ ):
lowerCAmelCase :Union[str, Any] = 1
lowerCAmelCase :Union[str, Any] = 2
lowerCAmelCase :Optional[int] = 3
lowerCAmelCase :Optional[int] = 4
lowerCAmelCase :Any = 5
lowerCAmelCase :Tuple = 6
lowerCAmelCase :List[Any] = 7
lowerCAmelCase :str = 8
lowerCAmelCase :List[Any] = 9
lowerCAmelCase :List[str] = 10
lowerCAmelCase :Union[str, Any] = 11
lowerCAmelCase :Optional[int] = 12
lowerCAmelCase :str = 13
lowerCAmelCase :Dict = 14
@dataclass
class _snake_case ( a__ ):
lowerCAmelCase :torch.FloatTensor
class _snake_case :
lowerCAmelCase :str = SCHEDULER_CONFIG_NAME
lowerCAmelCase :Union[str, Any] = []
lowerCAmelCase :List[str] = True
@classmethod
def snake_case__ ( cls , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase=False , **_lowerCamelCase , ):
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = cls.load_config(
pretrained_model_name_or_path=_lowerCamelCase , subfolder=_lowerCamelCase , return_unused_kwargs=_lowerCamelCase , return_commit_hash=_lowerCamelCase , **_lowerCamelCase , )
return cls.from_config(_lowerCamelCase , return_unused_kwargs=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = False , **_lowerCamelCase):
self.save_config(save_directory=_lowerCamelCase , push_to_hub=_lowerCamelCase , **_lowerCamelCase)
@property
def snake_case__ ( self):
return self._get_compatibles()
@classmethod
def snake_case__ ( cls):
UpperCAmelCase__ : List[Any] = list(set([cls.__name__] + cls._compatibles))
UpperCAmelCase__ : int = importlib.import_module(__name__.split(""".""")[0])
UpperCAmelCase__ : Dict = [
getattr(_lowerCamelCase , _lowerCamelCase) for c in compatible_classes_str if hasattr(_lowerCamelCase , _lowerCamelCase)
]
return compatible_classes | 113 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__A =logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : List[str] = R"""\w+[.]\d+"""
UpperCAmelCase__ : List[Any] = re.findall(UpperCamelCase__ , UpperCamelCase__ )
for pat in pats:
UpperCAmelCase__ : str = key.replace(UpperCamelCase__ , """_""".join(pat.split(""".""" ) ) )
return key
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase__ : Any = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCAmelCase__ : Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase__ : Optional[int] = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
UpperCAmelCase__ : Any = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase__ : Dict = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=4_2 ):
# Step 1: Convert pytorch tensor to numpy
UpperCAmelCase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCAmelCase__ : str = flax_model.init_weights(PRNGKey(UpperCamelCase__ ) )
UpperCAmelCase__ : Union[str, Any] = flatten_dict(UpperCamelCase__ )
UpperCAmelCase__ : int = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase__ : str = rename_key(UpperCamelCase__ )
UpperCAmelCase__ : Optional[Any] = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = rename_key_and_reshape_tensor(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
# also add unexpected weight so that warning is thrown
UpperCAmelCase__ : str = jnp.asarray(UpperCamelCase__ )
return unflatten_dict(UpperCamelCase__ ) | 113 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
def update_area_of_max_square(__lowerCamelCase , __lowerCamelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase__ : int = update_area_of_max_square(__lowerCamelCase , col + 1 )
lowercase__ : Any = update_area_of_max_square(row + 1 , col + 1 )
lowercase__ : str = update_area_of_max_square(row + 1 , __lowerCamelCase )
if mat[row][col]:
lowercase__ : Optional[Any] = 1 + min([right, diagonal, down] )
lowercase__ : List[str] = max(largest_square_area[0] , __lowerCamelCase )
return sub_problem_sol
else:
return 0
lowercase__ : Dict = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
def update_area_of_max_square_using_dp_array(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase__ : Any = update_area_of_max_square_using_dp_array(__lowerCamelCase , col + 1 , __lowerCamelCase )
lowercase__ : int = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , __lowerCamelCase )
lowercase__ : Optional[Any] = update_area_of_max_square_using_dp_array(row + 1 , __lowerCamelCase , __lowerCamelCase )
if mat[row][col]:
lowercase__ : Optional[int] = 1 + min([right, diagonal, down] )
lowercase__ : str = max(largest_square_area[0] , __lowerCamelCase )
lowercase__ : str = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase__ : List[str] = [0]
lowercase__ : Dict = [[-1] * cols for _ in range(__lowerCamelCase )]
update_area_of_max_square_using_dp_array(0 , 0 , __lowerCamelCase )
return largest_square_area[0]
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : Any = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase__ : Any = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase__ : Optional[Any] = dp_array[row][col + 1]
lowercase__ : int = dp_array[row + 1][col + 1]
lowercase__ : int = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase__ : Any = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase__ : List[str] = max(dp_array[row][col] , __lowerCamelCase )
else:
lowercase__ : Union[str, Any] = 0
return largest_square_area
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : List[str] = [0] * (cols + 1)
lowercase__ : List[str] = [0] * (cols + 1)
lowercase__ : Dict = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase__ : Any = current_row[col + 1]
lowercase__ : Dict = next_row[col + 1]
lowercase__ : str = next_row[col]
if mat[row][col] == 1:
lowercase__ : str = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase__ : int = max(current_row[col] , __lowerCamelCase )
else:
lowercase__ : Optional[Any] = 0
lowercase__ : Tuple = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 560 |
"""simple docstring"""
class __A :
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : int ,_snake_case : str ,_snake_case : Optional[Any] ) -> int:
"""simple docstring"""
lowercase__ : Tuple = None
lowercase__ : str = None
lowercase__ : Dict = graph
self._normalize_graph(_snake_case ,_snake_case )
lowercase__ : Any = len(_snake_case )
lowercase__ : Any = None
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[str] ,_snake_case : List[str] ) -> List[str]:
"""simple docstring"""
if sources is int:
lowercase__ : Optional[int] = [sources]
if sinks is int:
lowercase__ : str = [sinks]
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
return
lowercase__ : str = sources[0]
lowercase__ : Optional[int] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(_snake_case ) > 1 or len(_snake_case ) > 1:
lowercase__ : Tuple = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
lowercase__ : Dict = len(self.graph ) + 1
for room in self.graph:
room.insert(0 ,0 )
self.graph.insert(0 ,[0] * size )
for i in sources:
lowercase__ : Optional[Any] = max_input_flow
lowercase__ : Dict = 0
lowercase__ : List[Any] = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
lowercase__ : List[str] = max_input_flow
lowercase__ : int = size - 1
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
if self.maximum_flow_algorithm is None:
raise Exception('''You need to set maximum flow algorithm before.''' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def UpperCAmelCase ( self : str ,_snake_case : List[Any] ) -> int:
"""simple docstring"""
lowercase__ : Tuple = algorithm(self )
class __A :
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> int:
"""simple docstring"""
lowercase__ : int = flow_network
lowercase__ : int = flow_network.verticesCount
lowercase__ : Tuple = flow_network.sourceIndex
lowercase__ : str = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
lowercase__ : Optional[Any] = flow_network.graph
lowercase__ : Optional[int] = False
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
if not self.executed:
self._algorithm()
lowercase__ : Tuple = True
def UpperCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
pass
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
super().__init__(_snake_case )
# use this to save your result
lowercase__ : Union[str, Any] = -1
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
if not self.executed:
raise Exception('''You should execute algorithm before using its result!''' )
return self.maximum_flow
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : int = [[0] * self.verticies_count for i in range(self.verticies_count )]
lowercase__ : List[str] = [0] * self.verticies_count
lowercase__ : Tuple = [0] * self.verticies_count
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ : str = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
lowercase__ : Union[str, Any] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
lowercase__ : Tuple = 0
while i < len(_snake_case ):
lowercase__ : Dict = vertices_list[i]
lowercase__ : Optional[Any] = self.heights[vertex_index]
self.process_vertex(_snake_case )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 ,vertices_list.pop(_snake_case ) )
lowercase__ : Optional[int] = 0
else:
i += 1
lowercase__ : Dict = sum(self.preflow[self.source_index] )
def UpperCAmelCase ( self : Any ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(_snake_case ,_snake_case )
self.relabel(_snake_case )
def UpperCAmelCase ( self : int ,_snake_case : int ,_snake_case : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ : Tuple = min(
self.excesses[from_index] ,self.graph[from_index][to_index] - self.preflow[from_index][to_index] ,)
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : int = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
lowercase__ : Optional[int] = self.heights[to_index]
if min_height is not None:
lowercase__ : Optional[int] = min_height + 1
if __name__ == "__main__":
lowerCAmelCase_ = [0]
lowerCAmelCase_ = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
lowerCAmelCase_ = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
lowerCAmelCase_ = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
lowerCAmelCase_ = flow_network.find_maximum_flow()
print(F'''maximum flow is {maximum_flow}''')
| 560 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : str = ['torch']
def __init__( self : List[str] , *A__ : List[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Optional[int] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : Tuple , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : List[str] , *A__ : Tuple , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Tuple , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Dict , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : int = ['torch']
def __init__( self : List[str] , *A__ : Tuple , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Optional[Any] , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Optional[Any] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : str = ['torch']
def __init__( self : Union[str, Any] , *A__ : List[Any] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Union[str, Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Union[str, Any] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : Tuple , *A__ : List[str] , **A__ : str ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : Any , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Union[str, Any] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[Any] = ['torch']
def __init__( self : List[Any] , *A__ : str , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Optional[int] , **A__ : Any ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Optional[int] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : Optional[int] , *A__ : Dict , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Any , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Union[str, Any] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : Optional[int] , *A__ : List[str] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Any , **A__ : Any ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : str , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : int = ['torch']
def __init__( self : Dict , *A__ : Dict , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : int , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Dict , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : List[str] , *A__ : Union[str, Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Dict , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Dict , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : Dict , *A__ : str , **A__ : Any ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Any , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Any , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> Optional[Any]:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> List[str]:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> Optional[Any]:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> str:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> Dict:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> List[str]:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
def __lowercase (*_lowercase, **_lowercase ) -> Optional[int]:
"""simple docstring"""
requires_backends(_lowercase, ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : int , *A__ : str , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Optional[Any] , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Optional[Any] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[int] = ['torch']
def __init__( self : List[str] , *A__ : Optional[Any] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Optional[int] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Optional[int] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[int] = ['torch']
def __init__( self : str , *A__ : int , **A__ : Tuple ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : str , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Optional[int] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : str , *A__ : Optional[Any] , **A__ : Any ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Optional[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : int , **A__ : Any ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Optional[int] , *A__ : Any , **A__ : Tuple ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : List[str] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Optional[int] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[Any] = ['torch']
def __init__( self : Optional[Any] , *A__ : int , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : List[Any] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Dict , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Union[str, Any] , *A__ : Dict , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : List[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : List[Any] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : str = ['torch']
def __init__( self : int , *A__ : Tuple , **A__ : str ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : List[Any] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : Optional[Any] , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[Any] = ['torch']
def __init__( self : Any , *A__ : str , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Dict , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Tuple , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : int , *A__ : Any , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Optional[Any] , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : str , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : Tuple , *A__ : Dict , **A__ : str ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : List[Any] , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : int , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[int] = ['torch']
def __init__( self : List[str] , *A__ : Optional[int] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : List[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : Optional[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Optional[Any] , *A__ : Optional[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : List[str] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Dict , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[Any] = ['torch']
def __init__( self : List[str] , *A__ : str , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Any , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Tuple , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Union[str, Any] , *A__ : List[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Dict , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Dict , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : str , *A__ : int , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : List[str] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Optional[Any] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Tuple = ['torch']
def __init__( self : List[str] , *A__ : Optional[int] , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Union[str, Any] , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Optional[int] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : Dict , *A__ : List[Any] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Optional[int] , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Optional[int] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : Dict , *A__ : str , **A__ : str ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : List[Any] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : int , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[Any] = ['torch']
def __init__( self : List[Any] , *A__ : Optional[int] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Any , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Optional[Any] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Tuple , *A__ : int , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Tuple , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Any , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Dict = ['torch']
def __init__( self : Any , *A__ : Optional[Any] , **A__ : str ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Optional[int] , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : List[str] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[int] = ['torch']
def __init__( self : Tuple , *A__ : Dict , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Any , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Optional[int] , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : List[Any] , *A__ : List[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Any , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : Union[str, Any] , **A__ : Any ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : Tuple , *A__ : List[str] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : List[str] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : List[Any] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : Any , *A__ : Optional[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : int , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : List[Any] , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[Any] = ['torch']
def __init__( self : str , *A__ : Union[str, Any] , **A__ : Any ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : str , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : str , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Union[str, Any] = ['torch']
def __init__( self : Union[str, Any] , *A__ : Dict , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : Tuple , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Optional[int] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[int] = ['torch']
def __init__( self : List[Any] , *A__ : Any , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Union[str, Any] , **A__ : int ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Any , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : Union[str, Any] , *A__ : Union[str, Any] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[Any] , *A__ : Optional[Any] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : List[Any] , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Optional[Any] = ['torch']
def __init__( self : int , *A__ : List[str] , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Dict , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Optional[int] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : int = ['torch']
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : int ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : int , *A__ : Optional[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : int , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : int = ['torch']
def __init__( self : Optional[Any] , *A__ : Any , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Union[str, Any] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Union[str, Any] , *A__ : Any , **A__ : Any ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : Optional[int] , *A__ : Union[str, Any] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : List[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : Optional[int] , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : Union[str, Any] , *A__ : Union[str, Any] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Optional[Any] , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Optional[int] , **A__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : str = ['torch']
def __init__( self : Tuple , *A__ : List[str] , **A__ : Any ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : str , *A__ : int , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Any , *A__ : List[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : List[str] = ['torch']
def __init__( self : List[str] , *A__ : Optional[Any] , **A__ : List[Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Optional[Any] , **A__ : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : Dict , *A__ : Any , **A__ : List[str] ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : Any = ['torch']
def __init__( self : List[str] , *A__ : List[str] , **A__ : Any ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Tuple , *A__ : str , **A__ : Dict ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[Any] , *A__ : Any , **A__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
snake_case__ : str = ['torch']
def __init__( self : List[str] , *A__ : Any , **A__ : Optional[int] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
@classmethod
def a_ ( cls : Optional[int] , *A__ : Any , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
@classmethod
def a_ ( cls : List[str] , *A__ : Union[str, Any] , **A__ : str ):
"""simple docstring"""
requires_backends(cls , ["""torch"""] )
| 483 |
'''simple docstring'''
import math
def __lowercase () -> None:
"""simple docstring"""
__lowerCamelCase : Optional[int] = input("""Enter message: """ )
__lowerCamelCase : Optional[Any] = int(input(f"Enter key [2-{len(_lowercase ) - 1}]: " ) )
__lowerCamelCase : Dict = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
__lowerCamelCase : Any = encrypt_message(_lowercase, _lowercase )
elif mode.lower().startswith("""d""" ):
__lowerCamelCase : int = decrypt_message(_lowercase, _lowercase )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"Output:\n{text + '|'}" )
def __lowercase (_lowercase, _lowercase ) -> str:
"""simple docstring"""
__lowerCamelCase : int = [""""""] * key
for col in range(_lowercase ):
__lowerCamelCase : Optional[Any] = col
while pointer < len(_lowercase ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(_lowercase )
def __lowercase (_lowercase, _lowercase ) -> str:
"""simple docstring"""
__lowerCamelCase : List[str] = math.ceil(len(_lowercase ) / key )
__lowerCamelCase : List[str] = key
__lowerCamelCase : Tuple = (num_cols * num_rows) - len(_lowercase )
__lowerCamelCase : List[str] = [""""""] * num_cols
__lowerCamelCase : List[str] = 0
__lowerCamelCase : List[Any] = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
__lowerCamelCase : int = 0
row += 1
return "".join(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 483 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowercase:
'''simple docstring'''
def __init__( self: Tuple, a_: Tuple=None ):
'''simple docstring'''
_snake_case : Optional[int] = data
_snake_case : int = None
def __repr__( self: Optional[Any] ):
'''simple docstring'''
_snake_case : Union[str, Any] = []
_snake_case : List[Any] = self
while temp:
string_rep.append(f"{temp.data}" )
_snake_case : Dict = temp.next
return "->".join(a_ )
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if not elements_list:
raise Exception("""The Elements List is empty""" )
_snake_case : Any = Node(elements_list[0] )
for i in range(1 , len(snake_case__ ) ):
_snake_case : int = Node(elements_list[i] )
_snake_case : List[Any] = current.next
return head
def UpperCAmelCase__ (snake_case__ : Node ):
"""simple docstring"""
if head_node is not None and isinstance(snake_case__ , snake_case__ ):
print_reverse(head_node.next )
print(head_node.data )
def UpperCAmelCase__ ():
"""simple docstring"""
from doctest import testmod
testmod()
_snake_case : Tuple = make_linked_list([14, 52, 14, 12, 43] )
print("""Linked List:""" )
print(snake_case__ )
print("""Elements in Reverse:""" )
print_reverse(snake_case__ )
if __name__ == "__main__":
main()
| 609 |
"""simple docstring"""
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()
A_ = logging.get_logger(__name__)
A_ = {
'''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 UpperCAmelCase__ (snake_case__ : Optional[Any] , snake_case__ : Any , snake_case__ : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : Union[str, Any] ):
"""simple docstring"""
for attribute in key.split(""".""" ):
_snake_case : Dict = getattr(snake_case__ , snake_case__ )
if weight_type is not None:
_snake_case : List[Any] = getattr(snake_case__ , snake_case__ ).shape
else:
_snake_case : Optional[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":
_snake_case : List[str] = value
elif weight_type == "weight_g":
_snake_case : Optional[int] = value
elif weight_type == "weight_v":
_snake_case : List[str] = value
elif weight_type == "bias":
_snake_case : Optional[int] = value
else:
_snake_case : List[Any] = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ : List[Any] , snake_case__ : Any ):
"""simple docstring"""
_snake_case : Optional[int] = []
_snake_case : Optional[Any] = fairseq_model.state_dict()
_snake_case : Optional[Any] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_snake_case : Dict = False
if "conv_layers" in name:
load_conv_layer(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , hf_model.config.feat_extract_norm == """group""" , )
_snake_case : str = True
else:
for key, mapped_key in MAPPING.items():
_snake_case : Any = """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):
_snake_case : Tuple = True
if "*" in mapped_key:
_snake_case : Dict = name.split(snake_case__ )[0].split(""".""" )[-2]
_snake_case : Optional[int] = mapped_key.replace("""*""" , snake_case__ )
if "weight_g" in name:
_snake_case : int = """weight_g"""
elif "weight_v" in name:
_snake_case : Tuple = """weight_v"""
elif "weight" in name:
_snake_case : Optional[int] = """weight"""
elif "bias" in name:
_snake_case : str = """bias"""
else:
_snake_case : Tuple = None
set_recursively(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
continue
if not is_used:
unused_weights.append(snake_case__ )
logger.warning(F"Unused weights: {unused_weights}" )
def UpperCAmelCase__ (snake_case__ : Any , snake_case__ : Tuple , snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : int ):
"""simple docstring"""
_snake_case : Optional[int] = full_name.split("""conv_layers.""" )[-1]
_snake_case : Tuple = name.split(""".""" )
_snake_case : Dict = int(items[0] )
_snake_case : str = 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."
)
_snake_case : 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."
)
_snake_case : Optional[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."
)
_snake_case : Optional[Any] = 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."
)
_snake_case : Optional[int] = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(snake_case__ )
@torch.no_grad()
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : List[Any]=None , snake_case__ : int=None , snake_case__ : Tuple=True ):
"""simple docstring"""
if config_path is not None:
_snake_case : Dict = HubertConfig.from_pretrained(snake_case__ )
else:
_snake_case : List[str] = HubertConfig()
if is_finetuned:
if dict_path:
_snake_case : Optional[int] = Dictionary.load(snake_case__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_snake_case : Optional[int] = target_dict.pad_index
_snake_case : Union[str, Any] = target_dict.bos_index
_snake_case : List[Any] = target_dict.eos_index
_snake_case : List[str] = len(target_dict.symbols )
_snake_case : Any = os.path.join(snake_case__ , """vocab.json""" )
if not os.path.isdir(snake_case__ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case__ ) )
return
os.makedirs(snake_case__ , exist_ok=snake_case__ )
with open(snake_case__ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , snake_case__ )
_snake_case : Any = WavaVecaCTCTokenizer(
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=snake_case__ , )
_snake_case : List[str] = True if config.feat_extract_norm == """layer""" else False
_snake_case : int = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=snake_case__ , return_attention_mask=snake_case__ , )
_snake_case : Optional[Any] = WavaVecaProcessor(feature_extractor=snake_case__ , tokenizer=snake_case__ )
processor.save_pretrained(snake_case__ )
_snake_case : Tuple = HubertForCTC(snake_case__ )
else:
_snake_case : Any = HubertModel(snake_case__ )
if is_finetuned:
_snake_case , _snake_case , _snake_case : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_snake_case , _snake_case , _snake_case : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_snake_case : List[str] = model[0].eval()
recursively_load_weights(snake_case__ , snake_case__ , snake_case__ )
hf_wavavec.save_pretrained(snake_case__ )
if __name__ == "__main__":
A_ = 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'''
)
A_ = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 609 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase__( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase):
UpperCAmelCase__ : Any = StableDiffusionInstructPixaPixPipeline
UpperCAmelCase__ : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
UpperCAmelCase__ : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
UpperCAmelCase__ : int = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase__ : int = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase__ ( self: int ):
torch.manual_seed(0 )
__lowerCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__lowerCamelCase = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__lowerCamelCase = CLIPTextModel(UpperCamelCase_ )
__lowerCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCamelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: List[str]=0 ):
__lowerCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCamelCase = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert("""RGB""" )
if str(UpperCamelCase_ ).startswith("""mps""" ):
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
else:
__lowerCamelCase = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowerCamelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_ )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = self.get_dummy_inputs(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_ )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = self.get_dummy_inputs(UpperCamelCase_ )
__lowerCamelCase = """french fries"""
__lowerCamelCase = sd_pipe(**UpperCamelCase_ , negative_prompt=UpperCamelCase_ )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_ )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = self.get_dummy_inputs(UpperCamelCase_ )
__lowerCamelCase = [inputs["""prompt"""]] * 2
__lowerCamelCase = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
__lowerCamelCase = torch.from_numpy(UpperCamelCase_ ).unsqueeze(0 ).to(UpperCamelCase_ )
__lowerCamelCase = image / 2 + 0.5
__lowerCamelCase = image.permute(0 , 3 , 1 , 2 )
__lowerCamelCase = image.repeat(2 , 1 , 1 , 1 )
__lowerCamelCase = sd_pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
__lowerCamelCase = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_ )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = self.get_dummy_inputs(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = [round(UpperCamelCase_ , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(UpperCamelCase_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Union[str, Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_ )
__lowerCamelCase = VaeImageProcessor(do_resize=UpperCamelCase_ , do_normalize=UpperCamelCase_ )
__lowerCamelCase = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_ , input_image_type="""pt""" ) )[0]
__lowerCamelCase = components["""vae"""]
__lowerCamelCase = self.get_dummy_inputs_by_type(UpperCamelCase_ , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__lowerCamelCase = vae.encode(inputs[image_param] ).latent_dist.mode()
__lowerCamelCase = pipe(**UpperCamelCase_ )[0]
__lowerCamelCase = np.abs(out - out_latents_inputs ).max()
self.assertLess(UpperCamelCase_ , 1E-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: Union[str, Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: str=0 ):
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
__lowerCamelCase = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCamelCase = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase_ )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCamelCase = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase_ )
__lowerCamelCase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**UpperCamelCase_ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCamelCase = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = 0
def callback_fn(UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: torch.FloatTensor ) -> None:
__lowerCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowerCamelCase = latents[0, -3:, -3:, -1]
__lowerCamelCase = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
__lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowerCamelCase = latents[0, -3:, -3:, -1]
__lowerCamelCase = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
__lowerCamelCase = False
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowerCamelCase = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
__lowerCamelCase = self.get_inputs()
pipe(**UpperCamelCase_ , callback=UpperCamelCase_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowerCAmelCase__ ( self: List[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowerCamelCase = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__lowerCamelCase = self.get_inputs()
__lowerCamelCase = pipe(**UpperCamelCase_ )
__lowerCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__lowerCamelCase = inputs["""image"""].resize((5_04, 5_04) )
__lowerCamelCase = """timbrooks/instruct-pix2pix"""
__lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
UpperCamelCase_ , safety_checker=UpperCamelCase_ , )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
__lowerCamelCase = pipe(**UpperCamelCase_ )
__lowerCamelCase = output.images[0]
__lowerCamelCase = image[2_55:2_58, 3_83:3_86, -1]
assert image.shape == (5_04, 5_04, 3)
__lowerCamelCase = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
| 80 |
import math
def lowerCamelCase__ ( A__ : int ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = 2
__lowerCamelCase = int(math.sqrt(A__ ) ) # Size of every segment
__lowerCamelCase = [True] * (end + 1)
__lowerCamelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(A__ )
for i in range(start * start , end + 1 , A__ ):
__lowerCamelCase = False
start += 1
prime += in_prime
__lowerCamelCase = end + 1
__lowerCamelCase = min(2 * end , A__ )
while low <= n:
__lowerCamelCase = [True] * (high - low + 1)
for each in in_prime:
__lowerCamelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(A__ , high + 1 , A__ ):
__lowerCamelCase = False
for j in range(len(A__ ) ):
if temp[j] is True:
prime.append(j + low )
__lowerCamelCase = high + 1
__lowerCamelCase = min(high + end , A__ )
return prime
print(sieve(10**6))
| 80 | 1 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _UpperCamelCase ( _A ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = {}
_UpperCAmelCase = job["""started_at"""]
_UpperCAmelCase = job["""completed_at"""]
_UpperCAmelCase = date_parser.parse(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = date_parser.parse(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def _UpperCamelCase ( _A , _A=None ) -> str:
"""simple docstring"""
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}
_UpperCAmelCase = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
_UpperCAmelCase = requests.get(__SCREAMING_SNAKE_CASE , headers=__SCREAMING_SNAKE_CASE ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
_UpperCAmelCase = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0 )
for i in range(__SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = requests.get(url + F"""&page={i + 2}""" , headers=__SCREAMING_SNAKE_CASE ).json()
job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
return job_time
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
a : str = parser.parse_args()
a : Optional[int] = get_job_time(args.workflow_run_id)
a : Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F"{k}: {v['duration']}") | 555 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREAMING_SNAKE_CASE_ = 'src/diffusers'
SCREAMING_SNAKE_CASE_ = '.'
# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_ = importlib.util.spec_from_file_location(
'diffusers',
os.path.join(DIFFUSERS_PATH, '__init__.py'),
submodule_search_locations=[DIFFUSERS_PATH],
)
SCREAMING_SNAKE_CASE_ = spec.loader.load_module()
def __lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return line.startswith(__SCREAMING_SNAKE_CASE ) or len(__SCREAMING_SNAKE_CASE ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$""" , __SCREAMING_SNAKE_CASE ) is not None
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
__a = object_name.split(""".""" )
__a = 0
# First let's find the module where our object lives.
__a = parts[i]
while i < len(__SCREAMING_SNAKE_CASE ) and not os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE , F'''{module}.py''' ) ):
i += 1
if i < len(__SCREAMING_SNAKE_CASE ):
__a = os.path.join(__SCREAMING_SNAKE_CASE , parts[i] )
if i >= len(__SCREAMING_SNAKE_CASE ):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(__SCREAMING_SNAKE_CASE , F'''{module}.py''' ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__a = f.readlines()
# Now let's find the class / func in the code!
__a = """"""
__a = 0
for name in parts[i + 1 :]:
while (
line_index < len(__SCREAMING_SNAKE_CASE ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(__SCREAMING_SNAKE_CASE ):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
__a = line_index
while line_index < len(__SCREAMING_SNAKE_CASE ) and _should_continue(lines[line_index] , __SCREAMING_SNAKE_CASE ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
__a = lines[start_index:line_index]
return "".join(__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ = re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
SCREAMING_SNAKE_CASE_ = re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
SCREAMING_SNAKE_CASE_ = re.compile(R'<FILL\s+[^>]*>')
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
__a = code.split("""\n""" )
__a = 0
while idx < len(__SCREAMING_SNAKE_CASE ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(__SCREAMING_SNAKE_CASE ):
return re.search(r"""^(\s*)\S""" , lines[idx] ).groups()[0]
return ""
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__a = len(get_indent(__SCREAMING_SNAKE_CASE ) ) > 0
if has_indent:
__a = F'''class Bla:\n{code}'''
__a = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=__SCREAMING_SNAKE_CASE )
__a = black.format_str(__SCREAMING_SNAKE_CASE , mode=__SCREAMING_SNAKE_CASE )
__a , __a = style_docstrings_in_code(__SCREAMING_SNAKE_CASE )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def __lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ) -> str:
"""simple docstring"""
with open(__SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__a = f.readlines()
__a = []
__a = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(__SCREAMING_SNAKE_CASE ):
__a = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
__a , __a , __a = search.groups()
__a = find_code_in_diffusers(__SCREAMING_SNAKE_CASE )
__a = get_indent(__SCREAMING_SNAKE_CASE )
__a = line_index + 1 if indent == theoretical_indent else line_index + 2
__a = theoretical_indent
__a = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
__a = True
while line_index < len(__SCREAMING_SNAKE_CASE ) and should_continue:
line_index += 1
if line_index >= len(__SCREAMING_SNAKE_CASE ):
break
__a = lines[line_index]
__a = _should_continue(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and re.search(F'''^{indent}# End copy''' , __SCREAMING_SNAKE_CASE ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
__a = lines[start_index:line_index]
__a = """""".join(__SCREAMING_SNAKE_CASE )
# Remove any nested `Copied from` comments to avoid circular copies
__a = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(__SCREAMING_SNAKE_CASE ) is None]
__a = """\n""".join(__SCREAMING_SNAKE_CASE )
# Before comparing, use the `replace_pattern` on the original code.
if len(__SCREAMING_SNAKE_CASE ) > 0:
__a = replace_pattern.replace("""with""" , """""" ).split(""",""" )
__a = [_re_replace_pattern.search(__SCREAMING_SNAKE_CASE ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
__a , __a , __a = pattern.groups()
__a = re.sub(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if option.strip() == "all-casing":
__a = re.sub(obja.lower() , obja.lower() , __SCREAMING_SNAKE_CASE )
__a = re.sub(obja.upper() , obja.upper() , __SCREAMING_SNAKE_CASE )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
__a = blackify(lines[start_index - 1] + theoretical_code )
__a = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
__a = lines[:start_index] + [theoretical_code] + lines[line_index:]
__a = start_index + 1
if overwrite and len(__SCREAMING_SNAKE_CASE ) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''' )
with open(__SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__SCREAMING_SNAKE_CASE )
return diffs
def __lowercase ( __SCREAMING_SNAKE_CASE = False ) -> Union[str, Any]:
"""simple docstring"""
__a = glob.glob(os.path.join(__SCREAMING_SNAKE_CASE , """**/*.py""" ) , recursive=__SCREAMING_SNAKE_CASE )
__a = []
for filename in all_files:
__a = is_copy_consistent(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(__SCREAMING_SNAKE_CASE ) > 0:
__a = """\n""".join(__SCREAMING_SNAKE_CASE )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE_ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 582 | 0 |
from math import sqrt
def __lowerCamelCase ( A__ : int ) -> bool:
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(sqrt(A__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowerCamelCase ( A__ : int = 1_0001 ) -> int:
lowerCamelCase_ : int = 0
lowerCamelCase_ : Any = 1
while count != nth and number < 3:
number += 1
if is_prime(A__ ):
count += 1
while count != nth:
number += 2
if is_prime(A__ ):
count += 1
return number
if __name__ == "__main__":
print(F'{solution() = }')
| 171 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE_ (a__ ):
'''simple docstring'''
_a = ""
_a = "hf-legacy" # "hf://"" is reserved for hffs
def __init__( self : Any , __a : Optional[DatasetInfo] = None , __a : Optional[str] = None , **__a : Any , ) ->Any:
super().__init__(self , **__a )
lowerCamelCase_ : Tuple = repo_info
lowerCamelCase_ : Any = token
lowerCamelCase_ : Any = None
def _lowerCAmelCase ( self : Optional[int] ) ->List[Any]:
if self.dir_cache is None:
lowerCamelCase_ : Dict = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
lowerCamelCase_ : int = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(__a ): {"""name""": str(__a ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def _lowerCAmelCase ( self : int , __a : str , __a : str = "rb" , **__a : Optional[Any] , ) ->Dict:
if not isinstance(self.repo_info , __a ):
raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )
lowerCamelCase_ : int = hf_hub_url(self.repo_info.id , __a , revision=self.repo_info.sha )
return fsspec.open(
__a , mode=__a , headers=get_authentication_headers_for_url(__a , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def _lowerCAmelCase ( self : Dict , __a : str , **__a : List[Any] ) ->List[Any]:
self._get_dirs()
lowerCamelCase_ : Tuple = self._strip_protocol(__a )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__a )
def _lowerCAmelCase ( self : Any , __a : Optional[Any] , __a : str=False , **__a : List[str] ) ->List[Any]:
self._get_dirs()
lowerCamelCase_ : Optional[Any] = PurePosixPath(path.strip("""/""" ) )
lowerCamelCase_ : Dict = {}
for p, f in self.dir_cache.items():
lowerCamelCase_ : str = PurePosixPath(p.strip("""/""" ) )
lowerCamelCase_ : Dict = p.parent
if root == path:
lowerCamelCase_ : int = f
lowerCamelCase_ : List[str] = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 171 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Union[str, Any] = logging.getLogger(__name__)
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase__ : Dict=-1 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = label_idx
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Any = mode.value
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCAmelCase__ , F"{mode}.txt" )
__SCREAMING_SNAKE_CASE : List[str] = 1
__SCREAMING_SNAKE_CASE : int = []
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
__SCREAMING_SNAKE_CASE : List[str] = []
__SCREAMING_SNAKE_CASE : str = []
for line in f:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
__SCREAMING_SNAKE_CASE : Dict = []
__SCREAMING_SNAKE_CASE : List[Any] = []
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = line.split(""" """ )
words.append(splits[0] )
if len(lowerCAmelCase__ ) > 1:
labels.append(splits[self.label_idx].replace("""\n""" , """""" ) )
else:
# Examples could have no label for mode = "test"
labels.append("""O""" )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
return examples
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : List ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for line in test_input_reader:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
writer.write(lowerCAmelCase__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
__SCREAMING_SNAKE_CASE : List[str] = line.split()[0] + """ """ + preds_list[example_id].pop(0 ) + """\n"""
writer.write(lowerCAmelCase__ )
else:
logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""" , line.split()[0] )
def UpperCamelCase__ ( self : Union[str, Any] , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
__SCREAMING_SNAKE_CASE : Any = f.read().splitlines()
if "O" not in labels:
__SCREAMING_SNAKE_CASE : Union[str, Any] = ["""O"""] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : List[str] ):
"""simple docstring"""
super().__init__(label_idx=-2 )
def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
__SCREAMING_SNAKE_CASE : Optional[Any] = f.read().splitlines()
if "O" not in labels:
__SCREAMING_SNAKE_CASE : Tuple = ["""O"""] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : List[str] = mode.value
__SCREAMING_SNAKE_CASE : Dict = os.path.join(lowerCAmelCase__ , F"{mode}.txt" )
__SCREAMING_SNAKE_CASE : Dict = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = []
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
for sentence in parse_incr(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : Optional[int] = []
for token in sentence:
words.append(token["""form"""] )
labels.append(token["""upos"""] )
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
return examples
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : List ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = 0
for sentence in parse_incr(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = preds_list[example_id]
__SCREAMING_SNAKE_CASE : Union[str, Any] = """"""
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
out += "\n"
writer.write(lowerCAmelCase__ )
example_id += 1
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
] | 578 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: str , _lowerCamelCase: str , _lowerCamelCase: Path , _lowerCamelCase: str = None , _lowerCamelCase: str = None , _lowerCamelCase: str = None , ):
if config_name_or_path is None:
__SCREAMING_SNAKE_CASE : List[str] = """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""
if generator_tokenizer_name_or_path is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
__SCREAMING_SNAKE_CASE : Tuple = question_encoder_name_or_path
__SCREAMING_SNAKE_CASE : int = RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration
# Save model.
__SCREAMING_SNAKE_CASE : List[Any] = RagConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = gen_config
__SCREAMING_SNAKE_CASE : Union[str, Any] = question_encoder_config
__SCREAMING_SNAKE_CASE : Dict = 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.
__SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained(_lowerCamelCase )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(_lowerCamelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = 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``'''
),
)
UpperCamelCase__ : Dict = parser.parse_args()
UpperCamelCase__ : Any = 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,
) | 578 | 1 |
'''simple docstring'''
import argparse
import copy
def __lowercase (_lowercase ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase : int = {}
with open(_lowercase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__lowerCamelCase : List[str] = []
_list.append([line.split()[1], line.split()[2]] )
__lowerCamelCase : Tuple = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
__lowerCamelCase : List[Any] = []
_list.append([line.split()[0], line.split()[2]] )
__lowerCamelCase : Union[str, Any] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __lowercase (_lowercase, _lowercase ) -> str:
"""simple docstring"""
with open(_lowercase ) as f:
__lowerCamelCase : Optional[int] = f.read(1 )
__lowerCamelCase : List[Any] = start_node
__lowerCamelCase : List[Any] = []
__lowerCamelCase : str = start_node
__lowerCamelCase : str = 0
while visiting not in first_solution:
__lowerCamelCase : Optional[int] = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowercase ) and k[0] not in first_solution:
__lowerCamelCase : List[Any] = k[1]
__lowerCamelCase : str = k[0]
first_solution.append(_lowercase )
__lowerCamelCase : Any = distance_of_first_solution + int(_lowercase )
__lowerCamelCase : Optional[int] = best_node
first_solution.append(_lowercase )
__lowerCamelCase : str = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
__lowerCamelCase : str = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def __lowercase (_lowercase, _lowercase ) -> Tuple:
"""simple docstring"""
__lowerCamelCase : Tuple = []
for n in solution[1:-1]:
__lowerCamelCase : Dict = solution.index(_lowercase )
for kn in solution[1:-1]:
__lowerCamelCase : Tuple = solution.index(_lowercase )
if n == kn:
continue
__lowerCamelCase : Union[str, Any] = copy.deepcopy(_lowercase )
__lowerCamelCase : Optional[int] = kn
__lowerCamelCase : List[Any] = n
__lowerCamelCase : List[Any] = 0
for k in _tmp[:-1]:
__lowerCamelCase : Optional[int] = _tmp[_tmp.index(_lowercase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
__lowerCamelCase : Optional[int] = distance + int(i[1] )
_tmp.append(_lowercase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
__lowerCamelCase : Union[str, Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowercase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase : str = 1
__lowerCamelCase : List[Any] = first_solution
__lowerCamelCase : Any = []
__lowerCamelCase : str = distance_of_first_solution
__lowerCamelCase : str = solution
while count <= iters:
__lowerCamelCase : Union[str, Any] = find_neighborhood(_lowercase, _lowercase )
__lowerCamelCase : Dict = 0
__lowerCamelCase : int = neighborhood[index_of_best_solution]
__lowerCamelCase : Optional[int] = len(_lowercase ) - 1
__lowerCamelCase : List[Any] = False
while not found:
__lowerCamelCase : List[Any] = 0
while i < len(_lowercase ):
if best_solution[i] != solution[i]:
__lowerCamelCase : List[str] = best_solution[i]
__lowerCamelCase : Dict = solution[i]
break
__lowerCamelCase : Any = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
__lowerCamelCase : str = True
__lowerCamelCase : int = best_solution[:-1]
__lowerCamelCase : Any = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
__lowerCamelCase : Optional[int] = cost
__lowerCamelCase : str = solution
else:
__lowerCamelCase : Optional[int] = index_of_best_solution + 1
__lowerCamelCase : List[Any] = neighborhood[index_of_best_solution]
if len(_lowercase ) >= size:
tabu_list.pop(0 )
__lowerCamelCase : Optional[int] = count + 1
return best_solution_ever, best_cost
def __lowercase (_lowercase=None ) -> Tuple:
"""simple docstring"""
__lowerCamelCase : List[str] = generate_neighbours(args.File )
__lowerCamelCase : Optional[Any] = generate_first_solution(
args.File, _lowercase )
__lowerCamelCase : int = tabu_search(
_lowercase, _lowercase, _lowercase, args.Iterations, args.Size, )
print(f"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
UpperCAmelCase__ :Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 700 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ :List[str] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
snake_case__ : Optional[Any] = XLMRobertaTokenizer
snake_case__ : str = XLMRobertaTokenizerFast
snake_case__ : str = True
snake_case__ : int = True
def a_ ( self : Dict ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase : str = XLMRobertaTokenizer(A__ , keep_accents=A__ )
tokenizer.save_pretrained(self.tmpdirname )
def a_ ( self : int ):
"""simple docstring"""
__lowerCamelCase : List[Any] = """<pad>"""
__lowerCamelCase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__ ) , A__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__ ) , A__ )
def a_ ( self : str ):
"""simple docstring"""
__lowerCamelCase : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(A__ ) , 1002 )
def a_ ( self : List[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def a_ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase : Any = XLMRobertaTokenizer(A__ , keep_accents=A__ )
__lowerCamelCase : List[str] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(A__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
A__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
__lowerCamelCase : List[str] = tokenizer.convert_tokens_to_ids(A__ )
self.assertListEqual(
A__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__lowerCamelCase : Dict = tokenizer.convert_ids_to_tokens(A__ )
self.assertListEqual(
A__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def a_ ( self : Any ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__lowerCamelCase : Tuple = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
__lowerCamelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
__lowerCamelCase : Dict = self.tokenizer_class.from_pretrained(A__ , **A__ )
__lowerCamelCase : str = tempfile.mkdtemp()
__lowerCamelCase : Union[str, Any] = tokenizer_r.save_pretrained(A__ )
__lowerCamelCase : Union[str, Any] = tokenizer_p.save_pretrained(A__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
__lowerCamelCase : Any = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(A__ , A__ )
# Checks everything loads correctly in the same way
__lowerCamelCase : str = tokenizer_r.from_pretrained(A__ )
__lowerCamelCase : str = tokenizer_p.from_pretrained(A__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A__ , A__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(A__ )
# Save tokenizer rust, legacy_format=True
__lowerCamelCase : Optional[Any] = tempfile.mkdtemp()
__lowerCamelCase : Dict = tokenizer_r.save_pretrained(A__ , legacy_format=A__ )
__lowerCamelCase : List[Any] = tokenizer_p.save_pretrained(A__ )
# Checks it save with the same files
self.assertSequenceEqual(A__ , A__ )
# Checks everything loads correctly in the same way
__lowerCamelCase : Any = tokenizer_r.from_pretrained(A__ )
__lowerCamelCase : List[str] = tokenizer_p.from_pretrained(A__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A__ , A__ ) )
shutil.rmtree(A__ )
# Save tokenizer rust, legacy_format=False
__lowerCamelCase : int = tempfile.mkdtemp()
__lowerCamelCase : Any = tokenizer_r.save_pretrained(A__ , legacy_format=A__ )
__lowerCamelCase : List[str] = tokenizer_p.save_pretrained(A__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__lowerCamelCase : List[Any] = tokenizer_r.from_pretrained(A__ )
__lowerCamelCase : Optional[int] = tokenizer_p.from_pretrained(A__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A__ , A__ ) )
shutil.rmtree(A__ )
@cached_property
def a_ ( self : str ):
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def a_ ( self : int ):
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(A__ , f.name )
__lowerCamelCase : int = XLMRobertaTokenizer(f.name , keep_accents=A__ )
__lowerCamelCase : str = pickle.dumps(A__ )
pickle.loads(A__ )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCamelCase : Union[str, Any] = self.get_tokenizer()
__lowerCamelCase : Tuple = self.get_rust_tokenizer()
__lowerCamelCase : Optional[Any] = """I was born in 92000, and this is falsé."""
__lowerCamelCase : int = tokenizer.tokenize(A__ )
__lowerCamelCase : Optional[Any] = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
__lowerCamelCase : Any = tokenizer.encode(A__ , add_special_tokens=A__ )
__lowerCamelCase : Dict = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
__lowerCamelCase : Optional[Any] = self.get_rust_tokenizer()
__lowerCamelCase : Optional[int] = tokenizer.encode(A__ )
__lowerCamelCase : str = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
@slow
def a_ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : List[Any] = """Hello World!"""
__lowerCamelCase : str = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(A__ , self.big_tokenizer.encode(A__ ) )
@slow
def a_ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : Any = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
__lowerCamelCase : Optional[Any] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(A__ , self.big_tokenizer.encode(A__ ) )
@slow
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase : Tuple = {"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A__ , model_name="""xlm-roberta-base""" , revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" , )
| 483 | 0 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( __A ):
def __init__( self : int , _lowerCamelCase : List[str]=-1 ):
# in NER datasets, the last column is usually reserved for NER label
_snake_case = label_idx
def lowercase ( self : int , _lowerCamelCase : str , _lowerCamelCase : Union[Split, str] ):
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_snake_case = mode.value
_snake_case = os.path.join(_lowerCamelCase , f'''{mode}.txt''' )
_snake_case = 1
_snake_case = []
with open(_lowerCamelCase , encoding='''utf-8''' ) as f:
_snake_case = []
_snake_case = []
for line in f:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=_lowerCamelCase , labels=_lowerCamelCase ) )
guid_index += 1
_snake_case = []
_snake_case = []
else:
_snake_case = line.split(''' ''' )
words.append(splits[0] )
if len(_lowerCamelCase ) > 1:
labels.append(splits[self.label_idx].replace('''\n''' , '''''' ) )
else:
# Examples could have no label for mode = "test"
labels.append('''O''' )
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=_lowerCamelCase , labels=_lowerCamelCase ) )
return examples
def lowercase ( self : str , _lowerCamelCase : TextIO , _lowerCamelCase : TextIO , _lowerCamelCase : List ):
_snake_case = 0
for line in test_input_reader:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
writer.write(_lowerCamelCase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
_snake_case = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(_lowerCamelCase )
else:
logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' , line.split()[0] )
def lowercase ( self : List[str] , _lowerCamelCase : str ):
if path:
with open(_lowerCamelCase , '''r''' ) as f:
_snake_case = f.read().splitlines()
if "O" not in labels:
_snake_case = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class lowerCAmelCase__ ( __A ):
def __init__( self : Optional[Any] ):
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def lowercase ( self : Tuple , _lowerCamelCase : str ):
if path:
with open(_lowerCamelCase , '''r''' ) as f:
_snake_case = f.read().splitlines()
if "O" not in labels:
_snake_case = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class lowerCAmelCase__ ( __A ):
def lowercase ( self : List[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Union[Split, str] ):
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_snake_case = mode.value
_snake_case = os.path.join(_lowerCamelCase , f'''{mode}.txt''' )
_snake_case = 1
_snake_case = []
with open(_lowerCamelCase , encoding='''utf-8''' ) as f:
for sentence in parse_incr(_lowerCamelCase ):
_snake_case = []
_snake_case = []
for token in sentence:
words.append(token['''form'''] )
labels.append(token['''upos'''] )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=_lowerCamelCase , labels=_lowerCamelCase ) )
guid_index += 1
return examples
def lowercase ( self : List[str] , _lowerCamelCase : TextIO , _lowerCamelCase : TextIO , _lowerCamelCase : List ):
_snake_case = 0
for sentence in parse_incr(_lowerCamelCase ):
_snake_case = preds_list[example_id]
_snake_case = ""
for token in sentence:
out += f'''{token['form']} ({token['upos']}|{s_p.pop(0 )}) '''
out += "\n"
writer.write(_lowerCamelCase )
example_id += 1
def lowercase ( self : int , _lowerCamelCase : str ):
if path:
with open(_lowerCamelCase , '''r''' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 224 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ : Tuple = logging.get_logger(__name__)
snake_case_ : Tuple = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class snake_case_ ( __A ):
'''simple docstring'''
lowerCamelCase = "distilbert"
lowerCamelCase = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__( self : Any , __magic_name__ : int=3_0522 , __magic_name__ : int=512 , __magic_name__ : List[str]=False , __magic_name__ : List[Any]=6 , __magic_name__ : List[str]=12 , __magic_name__ : List[str]=768 , __magic_name__ : Optional[Any]=4 * 768 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[Any]="gelu" , __magic_name__ : List[str]=0.02 , __magic_name__ : Tuple=0.1 , __magic_name__ : Union[str, Any]=0.2 , __magic_name__ : Any=0 , **__magic_name__ : Dict , ) -> str:
lowerCamelCase_ : List[str] = vocab_size
lowerCamelCase_ : Optional[Any] = max_position_embeddings
lowerCamelCase_ : Optional[int] = sinusoidal_pos_embds
lowerCamelCase_ : Dict = n_layers
lowerCamelCase_ : Union[str, Any] = n_heads
lowerCamelCase_ : List[str] = dim
lowerCamelCase_ : Optional[Any] = hidden_dim
lowerCamelCase_ : int = dropout
lowerCamelCase_ : Any = attention_dropout
lowerCamelCase_ : Any = activation
lowerCamelCase_ : Tuple = initializer_range
lowerCamelCase_ : int = qa_dropout
lowerCamelCase_ : Tuple = seq_classif_dropout
super().__init__(**__magic_name__ , pad_token_id=__magic_name__ )
class snake_case_ ( __A ):
'''simple docstring'''
@property
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCamelCase_ : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase_ : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 488 | 0 |
'''simple docstring'''
def snake_case__ ( a , a , a ) -> Tuple:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a , n - 1 , a ) * a) % mod
else:
snake_case__ = binary_exponentiation(a , n / 2 , a )
return (b * b) % mod
# a prime number
a__ = 701
a__ = 1_000_000_000
a__ = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 711 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
a__ = pd.read_csv(
'''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
'''position_salaries.csv'''
)
a__ = dataset.iloc[:, 1:2].values
a__ = dataset.iloc[:, 2].values
a__ , a__ , a__ , a__ = train_test_split(X, y, test_size=0.2, random_state=0)
a__ = PolynomialFeatures(degree=4)
a__ = poly_reg.fit_transform(X)
a__ = LinearRegression()
pol_reg.fit(X_poly, y)
def snake_case__ ( ) -> int:
'''simple docstring'''
plt.scatter(a , a , color="""red""" )
plt.plot(a , pol_reg.predict(poly_reg.fit_transform(a ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003 | 566 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowercase__ ( _UpperCamelCase) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = filter(lambda _UpperCamelCase: p.requires_grad , model.parameters())
UpperCamelCase = sum([np.prod(p.size()) for p in model_parameters])
return params
__magic_name__ : List[str] = logging.getLogger(__name__)
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Union[str, Any]:
"""simple docstring"""
if metric == "rouge2":
UpperCamelCase = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
UpperCamelCase = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
UpperCamelCase = '{val_avg_em:.4f}-{step_count}'
else:
raise NotImplementedError(
F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
' function.')
UpperCamelCase = ModelCheckpoint(
dirpath=__snake_case , filename=__snake_case , monitor=F'val_{metric}' , mode='max' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Dict:
"""simple docstring"""
return EarlyStopping(
monitor=F'val_{metric}' , mode='min' if 'loss' in metric else 'max' , patience=__snake_case , verbose=__snake_case , )
class A__ ( pl.Callback ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : List[str] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
UpperCamelCase = {f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__lowerCAmelCase )
@rank_zero_only
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[Any]=True ):
"""simple docstring"""
logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
UpperCamelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} )
# Log results
UpperCamelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCamelCase = od / 'test_results.txt'
UpperCamelCase = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCamelCase = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
UpperCamelCase = od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=__lowerCAmelCase )
generations_file.parent.mkdir(exist_ok=__lowerCAmelCase )
with open(__lowerCAmelCase , 'a+' ) as writer:
for key in sorted(__lowerCAmelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCamelCase = metrics[key]
if isinstance(__lowerCAmelCase , torch.Tensor ):
UpperCamelCase = val.item()
UpperCamelCase = f'{key}: {val:.6f}\n'
writer.write(__lowerCAmelCase )
if not save_generations:
return
if "preds" in metrics:
UpperCamelCase = '\n'.join(metrics['preds'] )
generations_file.open('w+' ).write(__lowerCAmelCase )
@rank_zero_only
def _SCREAMING_SNAKE_CASE ( self : str , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
try:
UpperCamelCase = pl_module.model.model.num_parameters()
except AttributeError:
UpperCamelCase = pl_module.model.num_parameters()
UpperCamelCase = count_trainable_parameters(__lowerCAmelCase )
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6} )
@rank_zero_only
def _SCREAMING_SNAKE_CASE ( self : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__lowerCAmelCase , __lowerCAmelCase , 'test' )
@rank_zero_only
def _SCREAMING_SNAKE_CASE ( self : int , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 280 |
from itertools import permutations
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCamelCase__ = [7, 11, 13, 17]
for i, test in enumerate(__snake_case ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCAmelCase__(__snake_case = 10 ) -> int:
'''simple docstring'''
return sum(
int(''''''.join(map(__snake_case ,__snake_case ) ) )
for num in permutations(range(__snake_case ) )
if is_substring_divisible(__snake_case ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 481 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_ : Any = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
class a ( unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCAmelCase : Optional[Any] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCAmelCase : Optional[Any] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCAmelCase : Dict = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[Any]:
_a : str = ZeroShotClassificationPipeline(
model=lowerCamelCase_ , tokenizer=lowerCamelCase_ , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
_a : Tuple = classifier('Who are you voting for in 2020?' , candidate_labels='politics' )
self.assertEqual(lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ )]} )
# No kwarg
_a : Dict = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ )]} )
_a : str = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ )]} )
_a : str = classifier('Who are you voting for in 2020?' , candidate_labels='politics, public health' )
self.assertEqual(
lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_a : int = classifier('Who are you voting for in 2020?' , candidate_labels=['politics', 'public health'] )
self.assertEqual(
lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
_a : str = classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='This text is about {}' )
self.assertEqual(lowerCamelCase_ , {'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ )]} )
# https://github.com/huggingface/transformers/issues/13846
_a : Union[str, Any] = classifier(['I am happy'] , ['positive', 'negative'] )
self.assertEqual(
lowerCamelCase_ , [
{'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )]}
for i in range(1 )
] , )
_a : List[str] = classifier(['I am happy', 'I am sad'] , ['positive', 'negative'] )
self.assertEqual(
lowerCamelCase_ , [
{'sequence': ANY(lowerCamelCase_ ), 'labels': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )], 'scores': [ANY(lowerCamelCase_ ), ANY(lowerCamelCase_ )]}
for i in range(2 )
] , )
with self.assertRaises(lowerCamelCase_ ):
classifier('' , candidate_labels='politics' )
with self.assertRaises(lowerCamelCase_ ):
classifier(lowerCamelCase_ , candidate_labels='politics' )
with self.assertRaises(lowerCamelCase_ ):
classifier('Who are you voting for in 2020?' , candidate_labels='' )
with self.assertRaises(lowerCamelCase_ ):
classifier('Who are you voting for in 2020?' , candidate_labels=lowerCamelCase_ )
with self.assertRaises(lowerCamelCase_ ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='Not formatting template' , )
with self.assertRaises(lowerCamelCase_ ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template=lowerCamelCase_ , )
self.run_entailment_id(lowerCamelCase_ )
def __UpperCamelCase ( self , lowerCamelCase_ ) -> str:
_a : List[Any] = zero_shot_classifier.model.config
_a : int = config.labelaid
_a : Union[str, Any] = zero_shot_classifier.entailment_id
_a : Dict = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
_a : Optional[int] = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_a : int = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_a : Tuple = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
_a : Optional[int] = original_labelaid
self.assertEqual(lowerCamelCase_ , zero_shot_classifier.entailment_id )
@require_torch
def __UpperCamelCase ( self ) -> str:
_a : List[str] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 1_0_0 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def __UpperCamelCase ( self ) -> Any:
_a : Union[str, Any] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
_a : List[Any] = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} , )
@require_tf
def __UpperCamelCase ( self ) -> Dict:
_a : int = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
_a : Optional[int] = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def __UpperCamelCase ( self ) -> Dict:
_a : str = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
_a : Optional[int] = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} , )
_a : str = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=lowerCamelCase_ , )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def __UpperCamelCase ( self ) -> Optional[int]:
_a : Optional[Any] = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
_a : List[Any] = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.976, 0.015, 0.009],
} , )
_a : Dict = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=lowerCamelCase_ , )
self.assertEqual(
nested_simplify(lowerCamelCase_ ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.817, 0.713, 0.018, 0.018],
} , )
| 424 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """"""
__lowerCAmelCase : str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
__lowerCAmelCase : str = None # compression type in fsspec. ex: "gzip"
__lowerCAmelCase : str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self , lowerCamelCase_ = "" , lowerCamelCase_ = None , lowerCamelCase_ = None , **lowerCamelCase_ ) -> int:
super().__init__(self , **lowerCamelCase_ )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
_a : str = fsspec.open(
lowerCamelCase_ , mode='rb' , protocol=lowerCamelCase_ , compression=self.compression , client_kwargs={
'requote_redirect_url': False, # see https://github.com/huggingface/datasets/pull/5459
'trust_env': True, # Enable reading proxy env variables.
**(target_options or {}).pop('client_kwargs' , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
_a : int = os.path.basename(self.file.path.split('::' )[0] )
_a : Union[str, Any] = (
self.compressed_name[: self.compressed_name.rindex('.' )]
if '.' in self.compressed_name
else self.compressed_name
)
_a : Dict = None
@classmethod
def __UpperCamelCase ( cls , lowerCamelCase_ ) -> Optional[int]:
# compressed file paths are always relative to the archive root
return super()._strip_protocol(lowerCamelCase_ ).lstrip('/' )
def __UpperCamelCase ( self ) -> int:
if self.dir_cache is None:
_a : Dict = {**self.file.fs.info(self.file.path ), 'name': self.uncompressed_name}
_a : Any = {f['name']: f}
def __UpperCamelCase ( self , lowerCamelCase_ ) -> Union[str, Any]:
return self.file.open().read()
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ = "rb" , lowerCamelCase_=None , lowerCamelCase_=True , lowerCamelCase_=None , **lowerCamelCase_ , ) -> int:
_a : Union[str, Any] = self._strip_protocol(lowerCamelCase_ )
if mode != "rb":
raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
return self.file.open()
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Any = """bz2"""
__lowerCAmelCase : List[str] = """bz2"""
__lowerCAmelCase : Optional[int] = """.bz2"""
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = """gzip"""
__lowerCAmelCase : Optional[int] = """gzip"""
__lowerCAmelCase : List[str] = """.gz"""
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : int = """lz4"""
__lowerCAmelCase : List[Any] = """lz4"""
__lowerCAmelCase : Dict = """.lz4"""
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : List[Any] = """xz"""
__lowerCAmelCase : List[Any] = """xz"""
__lowerCAmelCase : Union[str, Any] = """.xz"""
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = """zstd"""
__lowerCAmelCase : int = """zstd"""
__lowerCAmelCase : str = """.zst"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_ = "rb" , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = DEFAULT_BLOCK_SIZE , **lowerCamelCase_ , ) -> str:
super().__init__(
fo=lowerCamelCase_ , mode=lowerCamelCase_ , target_protocol=lowerCamelCase_ , target_options=lowerCamelCase_ , block_size=lowerCamelCase_ , **lowerCamelCase_ , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
_a : Union[str, Any] = self.file.__enter__
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> Any:
_a : str = file_
def __enter__( self ) -> List[str]:
self._file.__enter__()
return self
def __exit__( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> Optional[int]:
self._file.__exit__(*lowerCamelCase_ , **lowerCamelCase_ )
def __iter__( self ) -> int:
return iter(self._file )
def __UpperCamelCase ( self ) -> int:
return next(self._file )
def __getattr__( self , lowerCamelCase_ ) -> Optional[Any]:
return getattr(self._file , lowerCamelCase_ )
def fixed_enter(*lowerCamelCase_ , **lowerCamelCase_ ):
return WrappedFile(_enter(*lowerCamelCase_ , **lowerCamelCase_ ) )
_a : List[Any] = fixed_enter
| 424 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = CycleDiffusionPipeline
__lowerCAmelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
__lowerCAmelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
__lowerCAmelCase : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
__lowerCAmelCase : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowerCAmelCase : List[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __UpperCamelCase ( self ) -> str:
torch.manual_seed(0 )
_a : int = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , )
_a : int = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , num_train_timesteps=1_0_0_0 , clip_sample=lowerCamelCase_ , set_alpha_to_one=lowerCamelCase_ , )
torch.manual_seed(0 )
_a : Any = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
_a : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
_a : List[str] = CLIPTextModel(lowerCamelCase_ )
_a : List[str] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_a : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_=0 ) -> Tuple:
_a : Tuple = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
_a : Union[str, Any] = image / 2 + 0.5
if str(lowerCamelCase_ ).startswith('mps' ):
_a : Tuple = torch.manual_seed(lowerCamelCase_ )
else:
_a : Dict = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
_a : Tuple = {
'prompt': 'An astronaut riding an elephant',
'source_prompt': 'An astronaut riding a horse',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'eta': 0.1,
'strength': 0.8,
'guidance_scale': 3,
'source_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : List[str] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_a : Any = self.get_dummy_components()
_a : Union[str, Any] = CycleDiffusionPipeline(**lowerCamelCase_ )
_a : Optional[Any] = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_a : Union[str, Any] = self.get_dummy_inputs(lowerCamelCase_ )
_a : Union[str, Any] = pipe(**lowerCamelCase_ )
_a : Tuple = output.images
_a : Optional[Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 3_2, 3_2, 3)
_a : List[str] = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def __UpperCamelCase ( self ) -> Optional[int]:
_a : Optional[int] = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowerCamelCase_ , 'half' ):
_a : Dict = module.half()
_a : Any = CycleDiffusionPipeline(**lowerCamelCase_ )
_a : Tuple = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_a : Any = self.get_dummy_inputs(lowerCamelCase_ )
_a : Tuple = pipe(**lowerCamelCase_ )
_a : List[Any] = output.images
_a : Union[str, Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 3_2, 3_2, 3)
_a : Optional[int] = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def __UpperCamelCase ( self ) -> Any:
return super().test_save_load_local()
@unittest.skip('non-deterministic pipeline' )
def __UpperCamelCase ( self ) -> Dict:
return super().test_inference_batch_single_identical()
@skip_mps
def __UpperCamelCase ( self ) -> List[str]:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __UpperCamelCase ( self ) -> Union[str, Any]:
return super().test_save_load_optional_components()
@skip_mps
def __UpperCamelCase ( self ) -> Optional[int]:
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self ) -> List[str]:
_a : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
_a : List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy' )
_a : List[Any] = init_image.resize((5_1_2, 5_1_2) )
_a : str = 'CompVis/stable-diffusion-v1-4'
_a : Optional[Any] = DDIMScheduler.from_pretrained(lowerCamelCase_ , subfolder='scheduler' )
_a : Any = CycleDiffusionPipeline.from_pretrained(
lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ , torch_dtype=torch.floataa , revision='fp16' )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
_a : Union[str, Any] = 'A black colored car'
_a : List[Any] = 'A blue colored car'
_a : List[str] = torch.manual_seed(0 )
_a : str = pipe(
prompt=lowerCamelCase_ , source_prompt=lowerCamelCase_ , image=lowerCamelCase_ , num_inference_steps=1_0_0 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowerCamelCase_ , output_type='np' , )
_a : Any = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def __UpperCamelCase ( self ) -> int:
_a : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
_a : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy' )
_a : List[Any] = init_image.resize((5_1_2, 5_1_2) )
_a : Dict = 'CompVis/stable-diffusion-v1-4'
_a : Optional[Any] = DDIMScheduler.from_pretrained(lowerCamelCase_ , subfolder='scheduler' )
_a : Any = CycleDiffusionPipeline.from_pretrained(lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
_a : Union[str, Any] = 'A black colored car'
_a : Any = 'A blue colored car'
_a : List[str] = torch.manual_seed(0 )
_a : Any = pipe(
prompt=lowerCamelCase_ , source_prompt=lowerCamelCase_ , image=lowerCamelCase_ , num_inference_steps=1_0_0 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowerCamelCase_ , output_type='np' , )
_a : Any = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 120 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowercase ( a__ : Dict ) -> Any:
"""simple docstring"""
if not is_accelerate_available():
return method
_UpperCamelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(a__ ) < version.parse("0.17.0" ):
return method
def wrapper(self : List[str] , *a__ : str , **a__ : int ):
if hasattr(self , "_hf_hook" ) and hasattr(self._hf_hook , "pre_forward" ):
self._hf_hook.pre_forward(self )
return method(self , *a__ , **a__ )
return wrapper
| 147 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__lowercase : List[str] = logging.get_logger(__name__)
class _A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Optional[int] , *A_ : Dict , **A_ : Optional[Any] ) -> None:
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 ) | 702 | """simple docstring"""
import re
def SCREAMING_SNAKE_CASE ( snake_case):
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_)]
def SCREAMING_SNAKE_CASE ( snake_case):
__snake_case = split_input(str_)
return "".join(
[''''''.join([char.capitalize() for char in sub_str]) for sub_str in string_split])
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case):
try:
__snake_case = split_input(snake_case)
if upper:
__snake_case = ''''''.join(
[
separator.join([char.upper() for char in sub_str])
for sub_str in string_split
])
else:
__snake_case = ''''''.join(
[
separator.join([char.lower() for char in sub_str])
for sub_str in string_split
])
return res_str
except IndexError:
return "not valid string"
def SCREAMING_SNAKE_CASE ( snake_case):
return to_simple_case(snake_case)
def SCREAMING_SNAKE_CASE ( snake_case):
try:
__snake_case = to_simple_case(snake_case)
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def SCREAMING_SNAKE_CASE ( snake_case, snake_case):
return to_complex_case(snake_case, snake_case, '''_''')
def SCREAMING_SNAKE_CASE ( snake_case, snake_case):
return to_complex_case(snake_case, snake_case, '''-''')
if __name__ == "__main__":
__import__("doctest").testmod() | 93 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
set_seed(770)
UpperCamelCase = {
'c_attn': 'att_proj',
'c_proj': 'out_proj',
'c_fc': 'in_proj',
'transformer.': '',
'h.': 'layers.',
'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2',
'ln_f': 'layernorm_final',
'wpe': 'position_embeds_layer',
'wte': 'input_embeds_layer',
}
UpperCamelCase = {
'text_small': {
'repo_id': 'suno/bark',
'file_name': 'text.pt',
},
'coarse_small': {
'repo_id': 'suno/bark',
'file_name': 'coarse.pt',
},
'fine_small': {
'repo_id': 'suno/bark',
'file_name': 'fine.pt',
},
'text': {
'repo_id': 'suno/bark',
'file_name': 'text_2.pt',
},
'coarse': {
'repo_id': 'suno/bark',
'file_name': 'coarse_2.pt',
},
'fine': {
'repo_id': 'suno/bark',
'file_name': 'fine_2.pt',
},
}
UpperCamelCase = os.path.dirname(os.path.abspath(__file__))
UpperCamelCase = os.path.join(os.path.expanduser('~'), '.cache')
UpperCamelCase = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def _A ( lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple=False ):
"""simple docstring"""
lowerCAmelCase__ = model_type
if use_small:
key += "_small"
return os.path.join(lowerCAmelCase_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def _A ( lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any] ):
"""simple docstring"""
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
hf_hub_download(repo_id=lowerCAmelCase_ , filename=lowerCAmelCase_ , local_dir=lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : Optional[Any]="text" ):
"""simple docstring"""
if model_type == "text":
lowerCAmelCase__ = BarkSemanticModel
lowerCAmelCase__ = BarkSemanticConfig
lowerCAmelCase__ = BarkSemanticGenerationConfig
elif model_type == "coarse":
lowerCAmelCase__ = BarkCoarseModel
lowerCAmelCase__ = BarkCoarseConfig
lowerCAmelCase__ = BarkCoarseGenerationConfig
elif model_type == "fine":
lowerCAmelCase__ = BarkFineModel
lowerCAmelCase__ = BarkFineConfig
lowerCAmelCase__ = BarkFineGenerationConfig
else:
raise NotImplementedError()
lowerCAmelCase__ = F'{model_type}_small' if use_small else model_type
lowerCAmelCase__ = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCAmelCase_ ):
logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.' )
_download(model_info["repo_id"] , model_info["file_name"] )
lowerCAmelCase__ = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
# this is a hack
lowerCAmelCase__ = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
lowerCAmelCase__ = model_args["vocab_size"]
lowerCAmelCase__ = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
lowerCAmelCase__ = model_args.pop("n_head" )
lowerCAmelCase__ = model_args.pop("n_embd" )
lowerCAmelCase__ = model_args.pop("n_layer" )
lowerCAmelCase__ = ConfigClass(**checkpoint["model_args"] )
lowerCAmelCase__ = ModelClass(config=lowerCAmelCase_ )
lowerCAmelCase__ = GenerationConfigClass()
lowerCAmelCase__ = model_generation_config
lowerCAmelCase__ = checkpoint["model"]
# fixup checkpoint
lowerCAmelCase__ = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(lowerCAmelCase_ ):
# replace part of the key with corresponding layer name in HF implementation
lowerCAmelCase__ = k[len(lowerCAmelCase_ ) :]
for old_layer_name in new_layer_name_dict:
lowerCAmelCase__ = new_k.replace(lowerCAmelCase_ , new_layer_name_dict[old_layer_name] )
lowerCAmelCase__ = state_dict.pop(lowerCAmelCase_ )
lowerCAmelCase__ = set(state_dict.keys() ) - set(model.state_dict().keys() )
lowerCAmelCase__ = {k for k in extra_keys if not k.endswith(".attn.bias" )}
lowerCAmelCase__ = set(model.state_dict().keys() ) - set(state_dict.keys() )
lowerCAmelCase__ = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(lowerCAmelCase_ ) != 0:
raise ValueError(F'extra keys found: {extra_keys}' )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(F'missing keys: {missing_keys}' )
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
lowerCAmelCase__ = model.num_parameters(exclude_embeddings=lowerCAmelCase_ )
lowerCAmelCase__ = checkpoint["best_val_loss"].item()
logger.info(F'model loaded: {round(n_params/1E6 , 1 )}M params, {round(lowerCAmelCase_ , 3 )} loss' )
model.eval()
model.to(lowerCAmelCase_ )
del checkpoint, state_dict
return model
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str=False , lowerCAmelCase_ : int="text" ):
"""simple docstring"""
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
lowerCAmelCase__ = "cpu" # do conversion on cpu
lowerCAmelCase__ = _get_ckpt_path(lowerCAmelCase_ , use_small=lowerCAmelCase_ )
lowerCAmelCase__ = _load_model(lowerCAmelCase_ , lowerCAmelCase_ , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
# load bark initial model
lowerCAmelCase__ = _bark_load_model(lowerCAmelCase_ , "cpu" , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
if model_type == "text":
lowerCAmelCase__ = bark_model["model"]
if model.num_parameters(exclude_embeddings=lowerCAmelCase_ ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
lowerCAmelCase__ = 5
lowerCAmelCase__ = 10
if model_type in ["text", "coarse"]:
lowerCAmelCase__ = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
lowerCAmelCase__ = bark_model(lowerCAmelCase_ )[0]
lowerCAmelCase__ = model(lowerCAmelCase_ )
# take last logits
lowerCAmelCase__ = output_new_model_total.logits[:, [-1], :]
else:
lowerCAmelCase__ = 3
lowerCAmelCase__ = 8
lowerCAmelCase__ = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
lowerCAmelCase__ = model(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = bark_model(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError("initial and new outputs are not equal" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
lowerCAmelCase__ = BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
lowerCAmelCase__ = BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
lowerCAmelCase__ = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
lowerCAmelCase__ = BarkSemanticModel.from_pretrained(lowerCAmelCase_ )
lowerCAmelCase__ = BarkCoarseModel.from_pretrained(lowerCAmelCase_ )
lowerCAmelCase__ = BarkFineModel.from_pretrained(lowerCAmelCase_ )
lowerCAmelCase__ = EncodecModel.from_pretrained("facebook/encodec_24khz" )
lowerCAmelCase__ = BarkConfig.from_sub_model_configs(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
lowerCAmelCase__ = BarkModel(lowerCAmelCase_ )
lowerCAmelCase__ = semantic
lowerCAmelCase__ = coarseAcoustic
lowerCAmelCase__ = fineAcoustic
lowerCAmelCase__ = codec
lowerCAmelCase__ = bark_generation_config
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
bark.save_pretrained(lowerCAmelCase_ , repo_id=lowerCAmelCase_ , push_to_hub=lowerCAmelCase_ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('model_type', type=str, help='text, coarse or fine.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')
UpperCamelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 61 | '''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
_A : Union[str, Any] = logging.get_logger(__name__)
_A : Optional[int] = {
'''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
'''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
'''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
_A : List[Any] = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCamelCase_ ( snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Dict , snake_case_ : List[str] ) -> Optional[Any]:
'''simple docstring'''
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(snake_case_ , snake_case_ )
if weight_type is not None:
__lowerCAmelCase = getattr(snake_case_ , snake_case_ ).shape
else:
__lowerCAmelCase = 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":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def UpperCamelCase_ ( snake_case_ : Any , snake_case_ : List[str] ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(snake_case_ )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , snake_case_ )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name and "relative_attention_bias" not in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def UpperCamelCase_ ( snake_case_ : Tuple , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
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."""
)
__lowerCAmelCase = 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."""
)
__lowerCAmelCase = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
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."
)
__lowerCAmelCase = 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."""
)
__lowerCAmelCase = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(snake_case_ )
@torch.no_grad()
def UpperCamelCase_ ( snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : int=None ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase = torch.load(snake_case_ )
__lowerCAmelCase = WavLMConfigOrig(checkpoint["""cfg"""] )
__lowerCAmelCase = WavLMOrig(snake_case_ )
model.load_state_dict(checkpoint["""model"""] )
model.eval()
if config_path is not None:
__lowerCAmelCase = WavLMConfig.from_pretrained(snake_case_ )
else:
__lowerCAmelCase = WavLMConfig()
__lowerCAmelCase = WavLMModel(snake_case_ )
recursively_load_weights(snake_case_ , snake_case_ )
hf_wavlm.save_pretrained(snake_case_ )
if __name__ == "__main__":
_A : Optional[int] = 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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
_A : str = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 427 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
a_ = {
# 1536-bit
5: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 2048-bit
14: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 3072-bit
15: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 4096-bit
16: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
+ 'FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 6144-bit
17: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
+ '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
+ '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
+ 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
+ '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
+ 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
+ '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
+ '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
+ '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
+ 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
+ '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
+ 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
+ '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
+ '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
+ '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
+ 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
+ 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
+ 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
+ '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
+ 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
+ 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
+ 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
+ '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
+ '6DCC4024FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 8192-bit
18: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
+ 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
+ '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
+ 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
+ '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
+ 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
+ '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
+ '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
+ 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
+ '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
+ '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
+ '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
+ '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
+ '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
+ '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
+ '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
+ '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
+ 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
+ '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
+ '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
+ '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
}
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any , a : Tuple = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError("Unsupported Group" )
SCREAMING_SNAKE_CASE : List[Any] = primes[group]["prime"]
SCREAMING_SNAKE_CASE : List[Any] = primes[group]["generator"]
SCREAMING_SNAKE_CASE : List[Any] = int(hexlify(urandom(32 ) ) , base=16 )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = pow(self.generator , self.__private_key , self.prime )
return hex(lowerCAmelCase__ )[2:]
def __UpperCamelCase ( self : List[Any] , a : Any ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(lowerCAmelCase__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def __UpperCamelCase ( self : List[str] , a : Any ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = int(lowerCAmelCase__ , base=16 )
if not self.is_valid_public_key(lowerCAmelCase__ ):
raise ValueError("Invalid public key" )
SCREAMING_SNAKE_CASE : Optional[Any] = pow(lowerCAmelCase__ , self.__private_key , self.prime )
return shaaaa(str(lowerCAmelCase__ ).encode() ).hexdigest()
@staticmethod
def __UpperCamelCase ( a : List[Any] , a : Any ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCAmelCase__ , (prime - 1) // 2 , lowerCAmelCase__ ) == 1
)
@staticmethod
def __UpperCamelCase ( a : List[str] , a : List[str] , a : Tuple = 14 ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = int(lowerCAmelCase__ , base=16 )
SCREAMING_SNAKE_CASE : Any = int(lowerCAmelCase__ , base=16 )
SCREAMING_SNAKE_CASE : Tuple = primes[group]["prime"]
if not DiffieHellman.is_valid_public_key_static(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError("Invalid public key" )
SCREAMING_SNAKE_CASE : str = pow(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return shaaaa(str(lowerCAmelCase__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod() | 709 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='gptj'
lowerCamelCase__ ={
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Dict , a : Optional[Any]=5_0400 , a : List[str]=2048 , a : List[Any]=4096 , a : int=28 , a : Union[str, Any]=16 , a : List[Any]=64 , a : int=None , a : Optional[int]="gelu_new" , a : Optional[Any]=0.0 , a : Any=0.0 , a : Union[str, Any]=0.0 , a : Union[str, Any]=1e-5 , a : Any=0.02 , a : Optional[int]=True , a : Tuple=5_0256 , a : Union[str, Any]=5_0256 , a : List[Any]=False , **a : str , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = n_positions
SCREAMING_SNAKE_CASE : Tuple = n_embd
SCREAMING_SNAKE_CASE : Tuple = n_layer
SCREAMING_SNAKE_CASE : List[Any] = n_head
SCREAMING_SNAKE_CASE : Tuple = n_inner
SCREAMING_SNAKE_CASE : Any = rotary_dim
SCREAMING_SNAKE_CASE : str = activation_function
SCREAMING_SNAKE_CASE : int = resid_pdrop
SCREAMING_SNAKE_CASE : Optional[int] = embd_pdrop
SCREAMING_SNAKE_CASE : Tuple = attn_pdrop
SCREAMING_SNAKE_CASE : List[str] = layer_norm_epsilon
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : Tuple = use_cache
SCREAMING_SNAKE_CASE : Union[str, Any] = bos_token_id
SCREAMING_SNAKE_CASE : List[Any] = eos_token_id
super().__init__(
bos_token_id=a , eos_token_id=a , tie_word_embeddings=a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , a : PretrainedConfig , a : str = "default" , a : List[PatchingSpec] = None , a : bool = False , ) -> Any:
"""simple docstring"""
super().__init__(a , task=a , patching_specs=a , use_past=a )
if not getattr(self._config , "pad_token_id" , a ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE : Dict = 0
@property
def __UpperCamelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(a , direction="inputs" )
SCREAMING_SNAKE_CASE : int = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE : Any = {0: "batch", 1: "sequence"}
return common_inputs
@property
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
return self._config.n_head
def __UpperCamelCase ( self : str , a : PreTrainedTokenizer , a : int = -1 , a : int = -1 , a : bool = False , a : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 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()
SCREAMING_SNAKE_CASE : Tuple = 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
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE : Any = seqlen + 2
SCREAMING_SNAKE_CASE : Dict = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE : str = [
(torch.zeros(a ), torch.zeros(a )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE : Optional[int] = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE : List[str] = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE : Any = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(a , a , dtype=a )] , dim=1 )
return ordered_inputs
@property
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return 13 | 193 | 0 |
'''simple docstring'''
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
lowerCAmelCase :Optional[int] = 1_6
lowerCAmelCase :Tuple = 3_2
def lowerCamelCase ( lowerCAmelCase : List[str] ):
"""simple docstring"""
return int(x / 2**20 )
class _lowerCamelCase :
'''simple docstring'''
def __enter__( self : str ) -> int:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
__magic_name__ : Dict = torch.cuda.memory_allocated()
return self
def __exit__( self : Union[str, Any] , *_A : Optional[Any] ) -> Any:
gc.collect()
torch.cuda.empty_cache()
__magic_name__ : List[str] = torch.cuda.memory_allocated()
__magic_name__ : Tuple = torch.cuda.max_memory_allocated()
__magic_name__ : int = bamb(self.end - self.begin )
__magic_name__ : List[Any] = 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"""
__magic_name__ : Optional[int] = AutoTokenizer.from_pretrained(lowerCAmelCase )
__magic_name__ : Any = load_dataset(
'glue' , 'mrpc' , split={'train': f'train[:{n_train}]', 'validation': f'validation[:{n_val}]'} )
def tokenize_function(lowerCAmelCase : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
__magic_name__ : str = 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
__magic_name__ : Optional[Any] = 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
__magic_name__ : List[str] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCAmelCase : str ):
# 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.
__magic_name__ : List[str] = DataLoader(
tokenized_datasets['train'] , shuffle=lowerCAmelCase , collate_fn=lowerCAmelCase , batch_size=lowerCAmelCase )
__magic_name__ : Tuple = DataLoader(
tokenized_datasets['validation'] , shuffle=lowerCAmelCase , collate_fn=lowerCAmelCase , batch_size=lowerCAmelCase )
return train_dataloader, eval_dataloader
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : List[Any] ):
"""simple docstring"""
__magic_name__ : int = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__magic_name__ : Optional[Any] = config['lr']
__magic_name__ : Tuple = int(config['num_epochs'] )
__magic_name__ : Dict = int(config['seed'] )
__magic_name__ : int = int(config['batch_size'] )
__magic_name__ : List[Any] = args.model_name_or_path
set_seed(lowerCAmelCase )
__magic_name__ , __magic_name__ : Union[str, Any] = 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)
__magic_name__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase , return_dict=lowerCAmelCase )
# Instantiate optimizer
__magic_name__ : Any = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__magic_name__ : Tuple = optimizer_cls(params=model.parameters() , lr=lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
__magic_name__ : List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
__magic_name__ : Any = 1
__magic_name__ : Union[str, Any] = (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
):
__magic_name__ : List[Any] = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase , num_warmup_steps=0 , num_training_steps=lowerCAmelCase , )
else:
__magic_name__ : Optional[int] = 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.
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple = accelerator.prepare(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
__magic_name__ : Optional[Any] = 0
# We also need to keep track of the stating epoch so files are named properly
__magic_name__ : List[Any] = 0
# Now we train the model
__magic_name__ : Tuple = {}
for epoch in range(lowerCAmelCase , lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(lowerCAmelCase ):
__magic_name__ : int = model(**lowerCAmelCase )
__magic_name__ : str = outputs.loss
__magic_name__ : List[Any] = 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 ) ) )
__magic_name__ : int = 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"""
__magic_name__ : int = 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.' , )
__magic_name__ : Union[str, Any] = parser.parse_args()
__magic_name__ : Tuple = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
main() | 561 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
lowerCAmelCase :Any = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
lowerCAmelCase :str = 1_2_8_0_2_2
lowerCAmelCase :int = 1_2_8_0_2_8
@require_sentencepiece
class _lowerCamelCase ( lowercase__ , unittest.TestCase ):
'''simple docstring'''
A_ : str = MaMaaaTokenizer
A_ : Any = False
A_ : List[str] = False
A_ : Optional[Any] = True
def __lowerCAmelCase ( self : List[Any] ) -> int:
super().setUp()
__magic_name__ : int = ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
__magic_name__ : Optional[int] = dict(zip(_A , range(len(_A ) ) ) )
__magic_name__ : int = Path(self.tmpdirname )
save_json(_A , save_dir / VOCAB_FILES_NAMES['vocab_file'] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_A , save_dir / VOCAB_FILES_NAMES['spm_file'] )
__magic_name__ : Optional[Any] = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] , **_A : int ) -> str:
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **_A )
def __lowerCAmelCase ( self : Any , _A : str ) -> Optional[int]:
return (
"This is a test",
"This is a test",
)
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
__magic_name__ : Union[str, Any] = '</s>'
__magic_name__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __lowerCAmelCase ( self : int ) -> Optional[int]:
__magic_name__ : List[str] = self.get_tokenizer()
__magic_name__ : Optional[Any] = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '</s>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '<s>' )
self.assertEqual(len(_A ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('Skip this test while all models are still to be uploaded.' )
def __lowerCAmelCase ( self : Dict ) -> List[str]:
pass
def __lowerCAmelCase ( self : Union[str, Any] ) -> str:
__magic_name__ : Any = self.get_tokenizer()
__magic_name__ : Optional[int] = tokenizer.tokenize('This is a test' )
self.assertListEqual(_A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [2, 3, 4, 5, 6] , )
__magic_name__ : str = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(_A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_string(_A )
self.assertEqual(_A , 'This is a test' )
@slow
def __lowerCAmelCase ( self : Optional[int] ) -> int:
# fmt: off
__magic_name__ : int = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='facebook/m2m100_418M' , revision='c168bae485c864188cf9aa0e4108b0b6934dc91e' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : str = """facebook/m2m100_418M"""
A_ : Tuple = [
"""In my opinion, there are two levels of response from the French government.""",
"""NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
]
A_ : Tuple = [
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
]
# fmt: off
A_ : List[Any] = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def __lowerCAmelCase ( cls : int ) -> List[str]:
__magic_name__ : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en' , tgt_lang='fr' )
__magic_name__ : Optional[int] = 1
return cls
def __lowerCAmelCase ( self : List[str] ) -> str:
self.assertEqual(self.tokenizer.get_lang_id('ar' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('en' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('ro' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('mr' ) , 128063 )
def __lowerCAmelCase ( self : Any ) -> Union[str, Any]:
__magic_name__ : Optional[int] = self.tokenizer.get_vocab()
self.assertEqual(len(_A ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['<unk>'] , 3 )
self.assertIn(self.tokenizer.get_lang_token('en' ) , _A )
def __lowerCAmelCase ( self : Optional[int] ) -> Any:
__magic_name__ : List[str] = 'en'
__magic_name__ : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _A )
def __lowerCAmelCase ( self : int ) -> List[str]:
self.assertIn(_A , self.tokenizer.all_special_ids )
# fmt: off
__magic_name__ : List[str] = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
__magic_name__ : List[Any] = self.tokenizer.decode(_A , skip_special_tokens=_A )
__magic_name__ : Tuple = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_A )
self.assertEqual(_A , _A )
self.assertNotIn(self.tokenizer.eos_token , _A )
def __lowerCAmelCase ( self : int ) -> Optional[Any]:
__magic_name__ : Any = tempfile.mkdtemp()
__magic_name__ : List[str] = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(_A )
__magic_name__ : List[str] = MaMaaaTokenizer.from_pretrained(_A )
self.assertDictEqual(new_tok.lang_token_to_id , _A )
@require_torch
def __lowerCAmelCase ( self : Dict ) -> List[str]:
__magic_name__ : Tuple = 'en'
__magic_name__ : Dict = 'fr'
__magic_name__ : List[Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_A , return_tensors='pt' )
__magic_name__ : int = shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
__magic_name__ : Union[str, Any] = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def __lowerCAmelCase ( self : int ) -> Any:
__magic_name__ : int = 'mr'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
__magic_name__ : Optional[int] = 'zh'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def __lowerCAmelCase ( self : str ) -> List[Any]:
__magic_name__ : Union[str, Any] = 'mr'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
__magic_name__ : Union[str, Any] = 'zh'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def __lowerCAmelCase ( self : str ) -> Union[str, Any]:
__magic_name__ : Union[str, Any] = self.tokenizer._build_translation_inputs('A test' , return_tensors='pt' , src_lang='en' , tgt_lang='ar' )
self.assertEqual(
nested_simplify(_A ) , {
# en_XX, A, test, EOS
'input_ids': [[128022, 58, 4183, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 128006,
} , ) | 561 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
A_ = {
"169M": 12,
"430M": 24,
"1B5": 24,
"3B": 32,
"7B": 32,
"14B": 40,
}
A_ = {
"169M": 7_68,
"430M": 10_24,
"1B5": 20_48,
"3B": 25_60,
"7B": 40_96,
"14B": 51_20,
}
def A ( _UpperCAmelCase ) -> Dict:
'''simple docstring'''
__lowerCAmelCase : List[str] = list(state_dict.keys() )
for name in state_dict_keys:
__lowerCAmelCase : List[str] = state_dict.pop(_UpperCAmelCase )
# emb -> embedding
if name.startswith('emb.' ):
__lowerCAmelCase : List[Any] = name.replace('emb.' ,'embeddings.' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('blocks.0.ln0' ):
__lowerCAmelCase : List[str] = name.replace('blocks.0.ln0' ,'blocks.0.pre_ln' )
# att -> attention
__lowerCAmelCase : Optional[Any] = re.sub(r'blocks\.(\d+)\.att' ,r'blocks.\1.attention' ,_UpperCAmelCase )
# ffn -> feed_forward
__lowerCAmelCase : str = re.sub(r'blocks\.(\d+)\.ffn' ,r'blocks.\1.feed_forward' ,_UpperCAmelCase )
# time_mix_k -> time_mix_key and reshape
if name.endswith('.time_mix_k' ):
__lowerCAmelCase : Tuple = name.replace('.time_mix_k' ,'.time_mix_key' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('.time_mix_v' ):
__lowerCAmelCase : Any = name.replace('.time_mix_v' ,'.time_mix_value' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('.time_mix_r' ):
__lowerCAmelCase : List[Any] = name.replace('.time_mix_r' ,'.time_mix_receptance' )
if name != "head.weight":
__lowerCAmelCase : Union[str, Any] = 'rwkv.' + name
__lowerCAmelCase : Optional[int] = weight
return state_dict
def A ( _UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase=None ,_UpperCAmelCase=None ,_UpperCAmelCase=False ,_UpperCAmelCase=None ) -> List[str]:
'''simple docstring'''
if tokenizer_file is None:
print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
__lowerCAmelCase : Union[str, Any] = 5_0_2_7_7
__lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
else:
__lowerCAmelCase : Optional[Any] = PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase )
__lowerCAmelCase : Union[str, Any] = len(_UpperCAmelCase )
tokenizer.save_pretrained(_UpperCAmelCase )
# 2. Build the config
__lowerCAmelCase : Optional[int] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
__lowerCAmelCase : Optional[int] = candidate
break
if size is None:
raise ValueError('Could not infer the size, please provide it with the `--size` argument.' )
if size not in possible_sizes:
raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}.""" )
__lowerCAmelCase : Tuple = RwkvConfig(
vocab_size=_UpperCAmelCase ,num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] ,hidden_size=HIDEN_SIZE_MAPPING[size] ,)
config.save_pretrained(_UpperCAmelCase )
# 3. Download model file then convert state_dict
__lowerCAmelCase : Tuple = hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase )
__lowerCAmelCase : Optional[Any] = torch.load(_UpperCAmelCase ,map_location='cpu' )
__lowerCAmelCase : Optional[Any] = convert_state_dict(_UpperCAmelCase )
# 4. Split in shards and save
__lowerCAmelCase : Dict = shard_checkpoint(_UpperCAmelCase )
for shard_file, shard in shards.items():
torch.save(_UpperCAmelCase ,os.path.join(_UpperCAmelCase ,_UpperCAmelCase ) )
if index is not None:
__lowerCAmelCase : int = os.path.join(_UpperCAmelCase ,_UpperCAmelCase )
# Save the index as well
with open(_UpperCAmelCase ,'w' ,encoding='utf-8' ) as f:
__lowerCAmelCase : Any = json.dumps(_UpperCAmelCase ,indent=2 ,sort_keys=_UpperCAmelCase ) + '\n'
f.write(_UpperCAmelCase )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.' )
__lowerCAmelCase : List[Any] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
__lowerCAmelCase : Optional[Any] = torch.load(os.path.join(_UpperCAmelCase ,_UpperCAmelCase ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} ,os.path.join(_UpperCAmelCase ,_UpperCAmelCase ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('Please provide a `model_name` to push the model to the Hub.' )
__lowerCAmelCase : List[Any] = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase )
model.push_to_hub(_UpperCAmelCase ,max_shard_size='2GB' )
tokenizer.push_to_hub(_UpperCAmelCase )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
)
parser.add_argument(
"--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
)
parser.add_argument(
"--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
)
parser.add_argument(
"--tokenizer_file",
default=None,
type=str,
help="Path to the tokenizer file to use (if not provided, only the model is converted).",
)
parser.add_argument(
"--size",
default=None,
type=str,
help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Push to the Hub the converted model.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the pushed model on the Hub, including the username / organization.",
)
A_ = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 716 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( a ):
'''simple docstring'''
_snake_case = (IPNDMScheduler,)
_snake_case = (('''num_inference_steps''', 50),)
def snake_case ( self , **SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase : Optional[int] = {'num_train_timesteps': 10_00}
config.update(**SCREAMING_SNAKE_CASE )
return config
def snake_case ( self , SCREAMING_SNAKE_CASE=0 , **SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase : Dict = dict(self.forward_default_kwargs )
__lowerCAmelCase : List[Any] = kwargs.pop('num_inference_steps' , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = self.dummy_sample
__lowerCAmelCase : Any = 0.1 * sample
__lowerCAmelCase : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase : List[str] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = scheduler_class(**SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
__lowerCAmelCase : Any = dummy_past_residuals[:]
if time_step is None:
__lowerCAmelCase : Union[str, Any] = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE )
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
__lowerCAmelCase : Union[str, Any] = dummy_past_residuals[:]
__lowerCAmelCase : Optional[int] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : Union[str, Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__lowerCAmelCase : int = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : int = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def snake_case ( self ) -> Optional[Any]:
pass
def snake_case ( self , SCREAMING_SNAKE_CASE=0 , **SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase : Union[str, Any] = dict(self.forward_default_kwargs )
__lowerCAmelCase : Union[str, Any] = kwargs.pop('num_inference_steps' , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = self.dummy_sample
__lowerCAmelCase : Optional[int] = 0.1 * sample
__lowerCAmelCase : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase : Tuple = self.get_scheduler_config()
__lowerCAmelCase : Tuple = scheduler_class(**SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase : int = dummy_past_residuals[:]
if time_step is None:
__lowerCAmelCase : List[str] = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase : Union[str, Any] = dummy_past_residuals[:]
__lowerCAmelCase : int = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : Optional[Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__lowerCAmelCase : List[str] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : int = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def snake_case ( self , **SCREAMING_SNAKE_CASE ) -> Tuple:
__lowerCAmelCase : Any = self.scheduler_classes[0]
__lowerCAmelCase : List[str] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = scheduler_class(**SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = 10
__lowerCAmelCase : Any = self.dummy_model()
__lowerCAmelCase : List[str] = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase : Any = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase : Dict = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample
return sample
def snake_case ( self ) -> Any:
__lowerCAmelCase : List[Any] = dict(self.forward_default_kwargs )
__lowerCAmelCase : Optional[int] = kwargs.pop('num_inference_steps' , SCREAMING_SNAKE_CASE )
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase : Dict = self.get_scheduler_config()
__lowerCAmelCase : str = scheduler_class(**SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = self.dummy_sample
__lowerCAmelCase : Optional[int] = 0.1 * sample
if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE , 'set_timesteps' ):
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE , 'set_timesteps' ):
__lowerCAmelCase : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__lowerCAmelCase : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
__lowerCAmelCase : Union[str, Any] = dummy_past_residuals[:]
__lowerCAmelCase : Dict = scheduler.timesteps[5]
__lowerCAmelCase : str = scheduler.timesteps[6]
__lowerCAmelCase : Tuple = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : List[str] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__lowerCAmelCase : Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
__lowerCAmelCase : Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def snake_case ( self ) -> int:
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE , time_step=SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Optional[int]:
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE , time_step=SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> List[str]:
__lowerCAmelCase : List[str] = self.full_loop()
__lowerCAmelCase : Tuple = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) )
assert abs(result_mean.item() - 2_54_05_29 ) < 10
| 123 | 0 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase_ ) )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
# Base Case
if index == len(lowerCamelCase_ ):
return True
# Recursive Step
for i in range(lowerCamelCase_ ):
if valid_coloring(graph[index] , lowerCamelCase_ , lowerCamelCase_ ):
# Color current vertex
A : Any = i
# Validate coloring
if util_color(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , index + 1 ):
return True
# Backtrack
A : Optional[Any] = -1
return False
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : List[Any] = [-1] * len(lowerCamelCase_ )
if util_color(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , 0 ):
return colored_vertices
return []
| 542 |
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 __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : int = '''pixel_values'''
UpperCAmelCase_ : List[str] = False
UpperCAmelCase_ : Tuple = TimmBackboneConfig
def __init__( self , __UpperCAmelCase , **__UpperCAmelCase ) -> int:
requires_backends(self , '''timm''' )
super().__init__(__UpperCAmelCase )
A : List[str] = 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.''' )
A : 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.
A : Optional[int] = config.out_indices if getattr(__UpperCAmelCase , '''out_indices''' , __UpperCAmelCase ) is not None else (-1,)
A : str = 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.
A : str = self._backbone.return_layers
A : Any = {layer['''module''']: str(__UpperCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(__UpperCAmelCase )
@classmethod
def snake_case ( cls , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''vision''', '''timm'''] )
from ...models.timm_backbone import TimmBackboneConfig
A : Optional[Any] = kwargs.pop('''config''' , TimmBackboneConfig() )
A : str = kwargs.pop('''use_timm_backbone''' , __UpperCAmelCase )
if not use_timm:
raise ValueError('''use_timm_backbone must be True for timm backbones''' )
A : Optional[int] = kwargs.pop('''num_channels''' , config.num_channels )
A : List[str] = kwargs.pop('''features_only''' , config.features_only )
A : Optional[int] = kwargs.pop('''use_pretrained_backbone''' , config.use_pretrained_backbone )
A : Optional[int] = kwargs.pop('''out_indices''' , config.out_indices )
A : int = TimmBackboneConfig(
backbone=__UpperCAmelCase , num_channels=__UpperCAmelCase , features_only=__UpperCAmelCase , use_pretrained_backbone=__UpperCAmelCase , out_indices=__UpperCAmelCase , )
return super()._from_config(__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase ) -> Any:
pass
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
A : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
A : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
A : 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
A : Dict = self._all_layers
A : int = self._backbone(__UpperCAmelCase , **__UpperCAmelCase )
A : Any = self._return_layers
A : Union[str, Any] = tuple(hidden_states[i] for i in self.out_indices )
else:
A : Optional[int] = self._backbone(__UpperCAmelCase , **__UpperCAmelCase )
A : List[Any] = None
A : str = tuple(__UpperCAmelCase )
A : int = tuple(__UpperCAmelCase ) if hidden_states is not None else None
if not return_dict:
A : Optional[Any] = (feature_maps,)
if output_hidden_states:
A : Tuple = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=__UpperCAmelCase , hidden_states=__UpperCAmelCase , attentions=__UpperCAmelCase )
| 542 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
if len(lowercase_ ) <= 1 or n <= 1:
return
insert_next(lowercase_ , n - 1 )
rec_insertion_sort(lowercase_ , n - 1 )
def UpperCamelCase( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
if index >= len(lowercase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
snake_case_ , snake_case_ = (
collection[index],
collection[index - 1],
)
insert_next(lowercase_ , index + 1 )
if __name__ == "__main__":
lowerCamelCase_ = input('''Enter integers separated by spaces: ''')
lowerCamelCase_ = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list) | 161 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AdaptiveEmbedding''',
'''TransfoXLForSequenceClassification''',
'''TransfoXLLMHeadModel''',
'''TransfoXLModel''',
'''TransfoXLPreTrainedModel''',
'''load_tf_weights_in_transfo_xl''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAdaptiveEmbedding''',
'''TFTransfoXLForSequenceClassification''',
'''TFTransfoXLLMHeadModel''',
'''TFTransfoXLMainLayer''',
'''TFTransfoXLModel''',
'''TFTransfoXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 161 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=[10, 20, 30, 40] , SCREAMING_SNAKE_CASE_=[1, 1, 2, 1] , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="relu" , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , ) -> str:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embeddings_size
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_labels
lowerCamelCase_ = scope
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = TFResNetModel(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFResNetForImageClassification(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ = (
{'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = TFResNetModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return
@unittest.skip(reason='ResNet does not use inputs_embeds' )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='ResNet does not support input and output embeddings' )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
def check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase_ = layer_type
lowerCamelCase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFResNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( ) -> Dict:
lowerCamelCase_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='tf' )
# forward pass
lowerCamelCase_ = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
lowerCamelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : str = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowerCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 460 | 0 |
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = credit_card_number
lowercase = 0
lowercase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
lowercase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
lowercase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = f"""{credit_card_number} is an invalid credit card number because"""
if not credit_card_number.isdigit():
print(f"""{error_message} it has nonnumerical characters.""" )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(f"""{error_message} of its length.""" )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(f"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(_UpperCAmelCase ):
print(f"""{error_message} it fails the Luhn check.""" )
return False
print(f"""{credit_card_number} is a valid credit card number.""" )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('''4111111111111111''')
validate_credit_card_number('''32323''')
| 314 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = OrderedDict()
for key, value in state_dict.items():
if key.startswith('module.encoder' ):
lowercase = key.replace('module.encoder' , 'glpn.encoder' )
if key.startswith('module.decoder' ):
lowercase = key.replace('module.decoder' , 'decoder.stages' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowercase = key[key.find('patch_embed' ) + len('patch_embed' )]
lowercase = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(_UpperCAmelCase )-1}""" )
if "norm" in key:
lowercase = key.replace('norm' , 'layer_norm' )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowercase = key[key.find('glpn.encoder.layer_norm' ) + len('glpn.encoder.layer_norm' )]
lowercase = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(_UpperCAmelCase )-1}""" )
if "layer_norm1" in key:
lowercase = key.replace('layer_norm1' , 'layer_norm_1' )
if "layer_norm2" in key:
lowercase = key.replace('layer_norm2' , 'layer_norm_2' )
if "block" in key:
# replace for example block1 by block.0
lowercase = key[key.find('block' ) + len('block' )]
lowercase = key.replace(f"""block{idx}""" , f"""block.{int(_UpperCAmelCase )-1}""" )
if "attn.q" in key:
lowercase = key.replace('attn.q' , 'attention.self.query' )
if "attn.proj" in key:
lowercase = key.replace('attn.proj' , 'attention.output.dense' )
if "attn" in key:
lowercase = key.replace('attn' , 'attention.self' )
if "fc1" in key:
lowercase = key.replace('fc1' , 'dense1' )
if "fc2" in key:
lowercase = key.replace('fc2' , 'dense2' )
if "linear_pred" in key:
lowercase = key.replace('linear_pred' , 'classifier' )
if "linear_fuse" in key:
lowercase = key.replace('linear_fuse.conv' , 'linear_fuse' )
lowercase = key.replace('linear_fuse.bn' , 'batch_norm' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowercase = key[key.find('linear_c' ) + len('linear_c' )]
lowercase = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(_UpperCAmelCase )-1}""" )
if "bot_conv" in key:
lowercase = key.replace('bot_conv' , '0.convolution' )
if "skip_conv1" in key:
lowercase = key.replace('skip_conv1' , '1.convolution' )
if "skip_conv2" in key:
lowercase = key.replace('skip_conv2' , '2.convolution' )
if "fusion1" in key:
lowercase = key.replace('fusion1' , '1.fusion' )
if "fusion2" in key:
lowercase = key.replace('fusion2' , '2.fusion' )
if "fusion3" in key:
lowercase = key.replace('fusion3' , '3.fusion' )
if "fusion" in key and "conv" in key:
lowercase = key.replace('conv' , 'convolutional_layer' )
if key.startswith('module.last_layer_depth' ):
lowercase = key.replace('module.last_layer_depth' , 'head.head' )
lowercase = value
return new_state_dict
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowercase = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowercase = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowercase = kv_weight[
: config.hidden_sizes[i], :
]
lowercase = kv_bias[: config.hidden_sizes[i]]
lowercase = kv_weight[
config.hidden_sizes[i] :, :
]
lowercase = kv_bias[config.hidden_sizes[i] :]
def __snake_case ( ):
"""simple docstring"""
lowercase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return image
@torch.no_grad()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=None ):
"""simple docstring"""
lowercase = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
lowercase = GLPNImageProcessor()
# prepare image
lowercase = prepare_img()
lowercase = image_processor(images=_UpperCAmelCase , return_tensors='pt' ).pixel_values
logger.info('Converting model...' )
# load original state dict
lowercase = torch.load(_UpperCAmelCase , map_location=torch.device('cpu' ) )
# rename keys
lowercase = rename_keys(_UpperCAmelCase )
# key and value matrices need special treatment
read_in_k_v(_UpperCAmelCase , _UpperCAmelCase )
# create HuggingFace model and load state dict
lowercase = GLPNForDepthEstimation(_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
# forward pass
lowercase = model(_UpperCAmelCase )
lowercase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowercase = torch.tensor(
[[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
elif "kitti" in model_name:
lowercase = torch.tensor(
[[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowercase = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print('Looks ok!' )
# finally, push to hub if required
if push_to_hub:
logger.info('Pushing model and image processor to the hub...' )
model.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization='nielsr' , commit_message='Add model' , use_temp_dir=_UpperCAmelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization='nielsr' , commit_message='Add image processor' , use_temp_dir=_UpperCAmelCase , )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''',
default=None,
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 folder to output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
parser.add_argument(
'''--model_name''',
default='''glpn-kitti''',
type=str,
help='''Name of the model in case you\'re pushing to the hub.''',
)
__magic_name__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 314 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join(chr(ord(lowerCamelCase ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""simple docstring"""
super().__init__(unk_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , **_lowerCAmelCase )
with open(_lowerCAmelCase , encoding="""utf-8""" ) as vocab_handle:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """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 _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
import sys
from pathlib import Path
_lowerCamelCase : Optional[int] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(4_2)
_lowerCamelCase : Union[str, Any] = {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}
_lowerCamelCase : str = """zero2"""
_lowerCamelCase : Tuple = """zero3"""
_lowerCamelCase : int = [ZEROa, ZEROa]
def _a ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE__ ) for x in param.args ) )
return f'''{func.__name__}_{param_based_name}'''
# Cartesian-product of zero stages with models to test
_lowerCamelCase : int = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class lowerCamelCase (_snake_case ):
"""simple docstring"""
@parameterized.expand(snake_case_, name_func=snake_case_ )
def A_ ( self : Any, _UpperCAmelCase : List[Any], _UpperCAmelCase : str ) -> int:
"""simple docstring"""
self.run_and_check(
stage=snake_case_, model=snake_case_, distributed=snake_case_, fpaa=snake_case_, )
@require_torch_multi_gpu
@parameterized.expand(snake_case_, name_func=snake_case_ )
def A_ ( self : Any, _UpperCAmelCase : Tuple, _UpperCAmelCase : str ) -> str:
"""simple docstring"""
self.run_and_check(
stage=snake_case_, model=snake_case_, distributed=snake_case_, fpaa=snake_case_, )
@parameterized.expand(snake_case_, name_func=snake_case_ )
def A_ ( self : Tuple, _UpperCAmelCase : str, _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
self.run_and_check(
stage=snake_case_, model=snake_case_, distributed=snake_case_, fpaa=snake_case_, )
@require_torch_multi_gpu
@parameterized.expand(snake_case_, name_func=snake_case_ )
def A_ ( self : List[str], _UpperCAmelCase : List[str], _UpperCAmelCase : str ) -> int:
"""simple docstring"""
self.run_and_check(
stage=snake_case_, model=snake_case_, distributed=snake_case_, fpaa=snake_case_, )
def A_ ( self : str, _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def A_ ( self : Optional[Any], _UpperCAmelCase : Dict, _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any] = 1_0, _UpperCAmelCase : Dict = True, _UpperCAmelCase : int = True, _UpperCAmelCase : Optional[Any] = True, ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = models[model]
SCREAMING_SNAKE_CASE__ : Any = self.run_trainer(
stage=snake_case_, model_name=snake_case_, eval_steps=snake_case_, num_train_epochs=1, distributed=snake_case_, fpaa=snake_case_, )
self.do_checks(snake_case_ )
return output_dir
def A_ ( self : Any, _UpperCAmelCase : Tuple, _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any] = 1_0, _UpperCAmelCase : str = 1, _UpperCAmelCase : Union[str, Any] = True, _UpperCAmelCase : Dict = True, ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_auto_remove_tmp_dir("./xxx", after=snake_case_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(snake_case_ )}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n '''.split()
if fpaa:
args.extend(["--fp16"] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
SCREAMING_SNAKE_CASE__ : str = F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
SCREAMING_SNAKE_CASE__ : Optional[int] = [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
SCREAMING_SNAKE_CASE__ : List[str] = self.get_launcher(snake_case_ )
SCREAMING_SNAKE_CASE__ : int = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(snake_case_, env=self.get_env() )
return output_dir
def A_ ( self : Union[str, Any], _UpperCAmelCase : List[Any]=False ) -> Optional[int]:
"""simple docstring"""
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(2, get_gpu_count() ) if distributed else 1
return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()
| 721 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _a ( SCREAMING_SNAKE_CASE__ : Any ) -> int:
'''simple docstring'''
def wrapper(*SCREAMING_SNAKE_CASE__ : Any , **SCREAMING_SNAKE_CASE__ : Any ):
SCREAMING_SNAKE_CASE__ : List[str] = timeit.default_timer()
SCREAMING_SNAKE_CASE__ : int = func(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = timeit.default_timer() - starttime
return delta
SCREAMING_SNAKE_CASE__ : Optional[Any] = func.__name__
return wrapper
def _a ( SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : Optional[Any]=1_00 , SCREAMING_SNAKE_CASE__ : Optional[int]=None ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_shapes or {}
for i in range(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(SCREAMING_SNAKE_CASE__ , _ArrayXD ):
SCREAMING_SNAKE_CASE__ : Tuple = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(SCREAMING_SNAKE_CASE__ , datasets.Value ):
if v.dtype == "string":
SCREAMING_SNAKE_CASE__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
SCREAMING_SNAKE_CASE__ : List[Any] = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(SCREAMING_SNAKE_CASE__ , datasets.Sequence ):
while isinstance(SCREAMING_SNAKE_CASE__ , datasets.Sequence ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = v.feature
SCREAMING_SNAKE_CASE__ : Dict = seq_shapes[k]
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(*SCREAMING_SNAKE_CASE__ ).astype(v.dtype )
SCREAMING_SNAKE_CASE__ : Any = data
dummy_data.append((i, example) )
return dummy_data
def _a ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Dict=1_00 , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = generate_examples(SCREAMING_SNAKE_CASE__ , num_examples=SCREAMING_SNAKE_CASE__ , seq_shapes=SCREAMING_SNAKE_CASE__ )
with ArrowWriter(features=SCREAMING_SNAKE_CASE__ , path=SCREAMING_SNAKE_CASE__ ) as writer:
for key, record in dummy_data:
SCREAMING_SNAKE_CASE__ : int = features.encode_example(SCREAMING_SNAKE_CASE__ )
writer.write(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Union[str, Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE__ , info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__ ) )
return dataset
| 157 | 0 |
def lowerCamelCase__ ( _a):
if a < 0:
raise ValueError("Input value must be a positive integer")
elif isinstance(_a , _a):
raise TypeError("Input value must be a 'int' type")
return bin(_a).count("1")
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | from __future__ import annotations
def lowerCAmelCase__ ( a__ , a__ = None , a__ = None , a__ = False , ) ->tuple[int, float, str]:
'''simple docstring'''
_UpperCamelCase = cipher_alphabet or [chr(a__ ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
_UpperCamelCase = {
"a": 0.08497,
"b": 0.01492,
"c": 0.02202,
"d": 0.04253,
"e": 0.11162,
"f": 0.02228,
"g": 0.02015,
"h": 0.06094,
"i": 0.07546,
"j": 0.00153,
"k": 0.01292,
"l": 0.04025,
"m": 0.02406,
"n": 0.06749,
"o": 0.07507,
"p": 0.01929,
"q": 0.00095,
"r": 0.07587,
"s": 0.06327,
"t": 0.09356,
"u": 0.02758,
"v": 0.00978,
"w": 0.02560,
"x": 0.00150,
"y": 0.01994,
"z": 0.00077,
}
else:
# Custom frequencies dictionary
_UpperCamelCase = frequencies_dict
if not case_sensitive:
_UpperCamelCase = ciphertext.lower()
# Chi squared statistic values
_UpperCamelCase = {}
# cycle through all of the shifts
for shift in range(len(a__ ) ):
_UpperCamelCase = ""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
_UpperCamelCase = (alphabet_letters.index(letter.lower() ) - shift) % len(
a__ )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
_UpperCamelCase = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
_UpperCamelCase = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
_UpperCamelCase = decrypted_with_shift.lower().count(a__ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_UpperCamelCase = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_UpperCamelCase = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
_UpperCamelCase = decrypted_with_shift.count(a__ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_UpperCamelCase = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_UpperCamelCase = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
_UpperCamelCase = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
_UpperCamelCase = min(
a__ , key=a__ , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 547 | 0 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
_UpperCAmelCase = [image]
if isinstance(image[0] , PIL.Image.Image ):
_UpperCAmelCase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
_UpperCAmelCase = np.concatenate(_SCREAMING_SNAKE_CASE , axis=0 )
_UpperCAmelCase = np.array(_SCREAMING_SNAKE_CASE ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image.transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = 2.0 * image - 1.0
_UpperCAmelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE )
elif isinstance(image[0] , torch.Tensor ):
_UpperCAmelCase = torch.cat(_SCREAMING_SNAKE_CASE , dim=0 )
return image
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any=0.9995 ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
_UpperCAmelCase = True
_UpperCAmelCase = va.device
_UpperCAmelCase = va.cpu().numpy()
_UpperCAmelCase = va.cpu().numpy()
_UpperCAmelCase = np.sum(va * va / (np.linalg.norm(_SCREAMING_SNAKE_CASE ) * np.linalg.norm(_SCREAMING_SNAKE_CASE )) )
if np.abs(_SCREAMING_SNAKE_CASE ) > DOT_THRESHOLD:
_UpperCAmelCase = (1 - t) * va + t * va
else:
_UpperCAmelCase = np.arccos(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.sin(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = theta_a * t
_UpperCAmelCase = np.sin(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.sin(theta_a - theta_t ) / sin_theta_a
_UpperCAmelCase = sin_theta_t / sin_theta_a
_UpperCAmelCase = sa * va + sa * va
if inputs_are_torch:
_UpperCAmelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
return va
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = F.normalize(_SCREAMING_SNAKE_CASE , dim=-1 )
_UpperCAmelCase = F.normalize(_SCREAMING_SNAKE_CASE , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
for param in model.parameters():
_UpperCAmelCase = value
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : AutoencoderKL , __UpperCamelCase : CLIPTextModel , __UpperCamelCase : CLIPModel , __UpperCamelCase : CLIPTokenizer , __UpperCamelCase : UNetaDConditionModel , __UpperCamelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __UpperCamelCase : CLIPFeatureExtractor , __UpperCamelCase : List[Any]=None , __UpperCamelCase : str=None , __UpperCamelCase : Any=None , )->Union[str, Any]:
super().__init__()
self.register_modules(
vae=__UpperCamelCase , text_encoder=__UpperCamelCase , clip_model=__UpperCamelCase , tokenizer=__UpperCamelCase , unet=__UpperCamelCase , scheduler=__UpperCamelCase , feature_extractor=__UpperCamelCase , coca_model=__UpperCamelCase , coca_tokenizer=__UpperCamelCase , coca_transform=__UpperCamelCase , )
_UpperCAmelCase = (
feature_extractor.size
if isinstance(feature_extractor.size , __UpperCamelCase )
else feature_extractor.size['''shortest_edge''']
)
_UpperCAmelCase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __UpperCamelCase )
set_requires_grad(self.clip_model , __UpperCamelCase )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Optional[Union[str, int]] = "auto" )->List[Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__UpperCamelCase )
def lowercase__ ( self : str )->str:
self.enable_attention_slicing(__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Optional[int]:
set_requires_grad(self.vae , __UpperCamelCase )
def lowercase__ ( self : int )->List[Any]:
set_requires_grad(self.vae , __UpperCamelCase )
def lowercase__ ( self : Optional[int] )->List[Any]:
set_requires_grad(self.unet , __UpperCamelCase )
def lowercase__ ( self : List[Any] )->List[Any]:
set_requires_grad(self.unet , __UpperCamelCase )
def lowercase__ ( self : Tuple , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Tuple )->Tuple:
# get the original timestep using init_timestep
_UpperCAmelCase = min(int(num_inference_steps * strength ) , __UpperCamelCase )
_UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
_UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowercase__ ( self : Dict , __UpperCamelCase : int , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any]=None )->Tuple:
if not isinstance(__UpperCamelCase , torch.Tensor ):
raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__UpperCamelCase )}' )
_UpperCAmelCase = image.to(device=__UpperCamelCase , dtype=__UpperCamelCase )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCAmelCase = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__UpperCamelCase )
]
_UpperCAmelCase = torch.cat(__UpperCamelCase , dim=0 )
else:
_UpperCAmelCase = self.vae.encode(__UpperCamelCase ).latent_dist.sample(__UpperCamelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 0.1_8_2_1_5 * init_latents
_UpperCAmelCase = init_latents.repeat_interleave(__UpperCamelCase , dim=0 )
_UpperCAmelCase = randn_tensor(init_latents.shape , generator=__UpperCamelCase , device=__UpperCamelCase , dtype=__UpperCamelCase )
# get latents
_UpperCAmelCase = self.scheduler.add_noise(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = init_latents
return latents
def lowercase__ ( self : str , __UpperCamelCase : str )->str:
_UpperCAmelCase = self.coca_transform(__UpperCamelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_UpperCAmelCase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_UpperCAmelCase = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : List[str] , __UpperCamelCase : str )->Any:
_UpperCAmelCase = self.feature_extractor.preprocess(__UpperCamelCase )
_UpperCAmelCase = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_UpperCAmelCase = self.clip_model.get_image_features(__UpperCamelCase )
_UpperCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCamelCase )
_UpperCAmelCase = image_embeddings_clip.repeat_interleave(__UpperCamelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : Union[str, Any] , )->str:
_UpperCAmelCase = latents.detach().requires_grad_()
_UpperCAmelCase = self.scheduler.scale_model_input(__UpperCamelCase , __UpperCamelCase )
# predict the noise residual
_UpperCAmelCase = self.unet(__UpperCamelCase , __UpperCamelCase , encoder_hidden_states=__UpperCamelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_UpperCAmelCase = self.scheduler.alphas_cumprod[timestep]
_UpperCAmelCase = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_UpperCAmelCase = torch.sqrt(__UpperCamelCase )
_UpperCAmelCase = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __UpperCamelCase ):
_UpperCAmelCase = self.scheduler.sigmas[index]
_UpperCAmelCase = latents - sigma * noise_pred
else:
raise ValueError(F'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 1 / 0.1_8_2_1_5 * sample
_UpperCAmelCase = self.vae.decode(__UpperCamelCase ).sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = transforms.Resize(self.feature_extractor_size )(__UpperCamelCase )
_UpperCAmelCase = self.normalize(__UpperCamelCase ).to(latents.dtype )
_UpperCAmelCase = self.clip_model.get_image_features(__UpperCamelCase )
_UpperCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__UpperCamelCase )
_UpperCAmelCase = spherical_dist_loss(__UpperCamelCase , __UpperCamelCase ).mean() * clip_guidance_scale
_UpperCAmelCase = -torch.autograd.grad(__UpperCamelCase , __UpperCamelCase )[0]
if isinstance(self.scheduler , __UpperCamelCase ):
_UpperCAmelCase = latents.detach() + grads * (sigma**2)
_UpperCAmelCase = noise_pred_original
else:
_UpperCAmelCase = noise_pred_original - torch.sqrt(__UpperCamelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Optional[Any] , __UpperCamelCase : Union[torch.FloatTensor, PIL.Image.Image] , __UpperCamelCase : Union[torch.FloatTensor, PIL.Image.Image] , __UpperCamelCase : Optional[str] = None , __UpperCamelCase : Optional[str] = None , __UpperCamelCase : Optional[int] = 5_1_2 , __UpperCamelCase : Optional[int] = 5_1_2 , __UpperCamelCase : float = 0.6 , __UpperCamelCase : Optional[int] = 5_0 , __UpperCamelCase : Optional[float] = 7.5 , __UpperCamelCase : Optional[int] = 1 , __UpperCamelCase : float = 0.0 , __UpperCamelCase : Optional[float] = 1_0_0 , __UpperCamelCase : Optional[torch.Generator] = None , __UpperCamelCase : Optional[str] = "pil" , __UpperCamelCase : bool = True , __UpperCamelCase : float = 0.8 , __UpperCamelCase : float = 0.1 , __UpperCamelCase : float = 0.1 , )->str:
if isinstance(__UpperCamelCase , __UpperCamelCase ) and len(__UpperCamelCase ) != batch_size:
raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__UpperCamelCase )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(__UpperCamelCase , torch.Generator ) and batch_size > 1:
_UpperCAmelCase = [generator] + [None] * (batch_size - 1)
_UpperCAmelCase = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_UpperCAmelCase = [x[0] for x in coca_is_none if x[1]]
_UpperCAmelCase = ''', '''.join(__UpperCamelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__UpperCamelCase ):
raise ValueError(
F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_UpperCAmelCase = self.get_image_description(__UpperCamelCase )
if style_prompt is None:
if len(__UpperCamelCase ):
raise ValueError(
F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_UpperCAmelCase = self.get_image_description(__UpperCamelCase )
# get prompt text embeddings for content and style
_UpperCAmelCase = self.tokenizer(
__UpperCamelCase , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors='''pt''' , )
_UpperCAmelCase = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_UpperCAmelCase = self.tokenizer(
__UpperCamelCase , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors='''pt''' , )
_UpperCAmelCase = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_UpperCAmelCase = slerp(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# duplicate text embeddings for each generation per prompt
_UpperCAmelCase = text_embeddings.repeat_interleave(__UpperCamelCase , dim=0 )
# set timesteps
_UpperCAmelCase = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_offset:
_UpperCAmelCase = 1
self.scheduler.set_timesteps(__UpperCamelCase , **__UpperCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_UpperCAmelCase , _UpperCAmelCase = self.get_timesteps(__UpperCamelCase , __UpperCamelCase , self.device )
_UpperCAmelCase = timesteps[:1].repeat(__UpperCamelCase )
# Preprocess image
_UpperCAmelCase = preprocess(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = self.prepare_latents(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , text_embeddings.dtype , self.device , __UpperCamelCase )
_UpperCAmelCase = preprocess(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = self.prepare_latents(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , text_embeddings.dtype , self.device , __UpperCamelCase )
_UpperCAmelCase = slerp(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if clip_guidance_scale > 0:
_UpperCAmelCase = self.get_clip_image_embeddings(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = self.get_clip_image_embeddings(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = slerp(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# 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.
_UpperCAmelCase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_UpperCAmelCase = content_text_input.input_ids.shape[-1]
_UpperCAmelCase = self.tokenizer([''''''] , padding='''max_length''' , max_length=__UpperCamelCase , return_tensors='''pt''' )
_UpperCAmelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_UpperCAmelCase = uncond_embeddings.repeat_interleave(__UpperCamelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_UpperCAmelCase = 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`.
_UpperCAmelCase = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_UpperCAmelCase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_UpperCAmelCase = torch.randn(__UpperCamelCase , generator=__UpperCamelCase , device='''cpu''' , dtype=__UpperCamelCase ).to(
self.device )
else:
_UpperCAmelCase = 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}' )
_UpperCAmelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = 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]
_UpperCAmelCase = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
# check if the scheduler accepts generator
_UpperCAmelCase = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_UpperCAmelCase = generator
with self.progress_bar(total=__UpperCamelCase ):
for i, t in enumerate(__UpperCamelCase ):
# expand the latents if we are doing classifier free guidance
_UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCAmelCase = self.scheduler.scale_model_input(__UpperCamelCase , __UpperCamelCase )
# predict the noise residual
_UpperCAmelCase = self.unet(__UpperCamelCase , __UpperCamelCase , encoder_hidden_states=__UpperCamelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_UpperCAmelCase , _UpperCAmelCase = noise_pred.chunk(2 )
_UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_UpperCAmelCase = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_UpperCAmelCase , _UpperCAmelCase = self.cond_fn(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 1 / 0.1_8_2_1_5 * latents
_UpperCAmelCase = self.vae.decode(__UpperCamelCase ).sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__UpperCamelCase , nsfw_content_detected=__UpperCamelCase )
| 95 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _a ( yaml.SafeLoader):
"""simple docstring"""
def lowercase__ ( self : List[str] , __UpperCamelCase : Any )->List[Any]:
_UpperCAmelCase = [self.constructed_objects[key_node] for key_node, _ in node.value]
_UpperCAmelCase = [tuple(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else key for key in keys]
_UpperCAmelCase = Counter(__UpperCamelCase )
_UpperCAmelCase = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}' )
def lowercase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str=False )->Dict:
_UpperCAmelCase = super().construct_mapping(__UpperCamelCase , deep=__UpperCamelCase )
self._check_no_duplicates_on_constructed_node(__UpperCamelCase )
return mapping
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
_UpperCAmelCase = full_content[1:].index('''---''' ) + 1
_UpperCAmelCase = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_SCREAMING_SNAKE_CASE )
class _a ( lowerCAmelCase):
"""simple docstring"""
# class attributes
UpperCamelCase__ = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def lowercase__ ( cls : List[Any] , __UpperCamelCase : Path )->"DatasetMetadata":
with open(__UpperCamelCase , encoding='''utf-8''' ) as readme_file:
_UpperCAmelCase , _UpperCAmelCase = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__UpperCamelCase )
else:
return cls()
def lowercase__ ( self : Tuple , __UpperCamelCase : Path )->List[Any]:
if path.exists():
with open(__UpperCamelCase , encoding='''utf-8''' ) as readme_file:
_UpperCAmelCase = readme_file.read()
else:
_UpperCAmelCase = None
_UpperCAmelCase = self._to_readme(__UpperCamelCase )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(__UpperCamelCase )
def lowercase__ ( self : List[str] , __UpperCamelCase : Optional[str] = None )->str:
if readme_content is not None:
_UpperCAmelCase , _UpperCAmelCase = _split_yaml_from_readme(__UpperCamelCase )
_UpperCAmelCase = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
_UpperCAmelCase = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def lowercase__ ( cls : str , __UpperCamelCase : str )->"DatasetMetadata":
_UpperCAmelCase = yaml.load(__UpperCamelCase , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
_UpperCAmelCase = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__UpperCamelCase )
def lowercase__ ( self : str )->str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__UpperCamelCase , allow_unicode=__UpperCamelCase , encoding='''utf-8''' , ).decode('''utf-8''' )
__A : str = {
"image-classification": [],
"translation": [],
"image-segmentation": [],
"fill-mask": [],
"automatic-speech-recognition": [],
"token-classification": [],
"sentence-similarity": [],
"audio-classification": [],
"question-answering": [],
"summarization": [],
"zero-shot-classification": [],
"table-to-text": [],
"feature-extraction": [],
"other": [],
"multiple-choice": [],
"text-classification": [],
"text-to-image": [],
"text2text-generation": [],
"zero-shot-image-classification": [],
"tabular-classification": [],
"tabular-regression": [],
"image-to-image": [],
"tabular-to-text": [],
"unconditional-image-generation": [],
"text-retrieval": [],
"text-to-speech": [],
"object-detection": [],
"audio-to-audio": [],
"text-generation": [],
"conversational": [],
"table-question-answering": [],
"visual-question-answering": [],
"image-to-text": [],
"reinforcement-learning": [],
"voice-activity-detection": [],
"time-series-forecasting": [],
"document-question-answering": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__A : str = ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
ap.add_argument("readme_filepath")
__A : Union[str, Any] = ap.parse_args()
__A : Dict = Path(args.readme_filepath)
__A : Tuple = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 95 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'adapter_layer': 'encoder.layers.*.adapter_layer',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
'pooling_layer.linear': 'projector',
'pooling_layer.projection': 'classifier',
}
__A = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'projector',
'classifier',
]
def __A ( _lowercase ):
'''simple docstring'''
_A = {}
with open(_lowercase , '''r''' ) as file:
for line_number, line in enumerate(_lowercase ):
_A = line.strip()
if line:
_A = line.split()
_A = line_number
_A = words[0]
_A = value
return result
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
for attribute in key.split('''.''' ):
_A = getattr(_lowercase , _lowercase )
_A = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowercase ):
_A = PARAM_MAPPING[full_name.split('''.''' )[-1]]
_A = '''param'''
if weight_type is not None and weight_type != "param":
_A = getattr(_lowercase , _lowercase ).shape
elif weight_type is not None and weight_type == "param":
_A = hf_pointer
for attribute in hf_param_name.split('''.''' ):
_A = getattr(_lowercase , _lowercase )
_A = shape_pointer.shape
# let's reduce dimension
_A = value[0]
else:
_A = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
_A = getattr(_lowercase , _lowercase )
_A = value
else:
_A = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowercase ):
_A = PARAM_MAPPING[full_name.split('''.''' )[-1]]
_A = '''param'''
if weight_type is not None and weight_type != "param":
_A = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_A = '''.'''.join([key, hf_param_name] )
else:
_A = key
_A = value if '''lm_head''' in full_key else value[0]
__A = {
'W_a': 'linear_1.weight',
'W_b': 'linear_2.weight',
'b_a': 'linear_1.bias',
'b_b': 'linear_2.bias',
'ln_W': 'norm.weight',
'ln_b': 'norm.bias',
}
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None ):
'''simple docstring'''
_A = False
for key, mapped_key in MAPPING.items():
_A = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_lowercase )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _lowercase )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "bias" in name:
_A = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_A = '''weight'''
else:
_A = None
if hf_dict is not None:
rename_dict(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
else:
set_recursively(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return is_used
return is_used
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_lowercase , _lowercase , _lowercase , _lowercase , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
_A = load_wavaveca_layer(_lowercase , _lowercase , _lowercase )
if not is_used:
unused_weights.append(_lowercase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
_A = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
_A = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
_A = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
_A = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowercase )
@torch.no_grad()
def __A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=True , _lowercase=False ):
'''simple docstring'''
if config_path is not None:
_A = WavaVecaConfig.from_pretrained(_lowercase )
else:
_A = WavaVecaConfig()
if is_seq_class:
_A = read_txt_into_dict(_lowercase )
_A = idalabel
_A = WavaVecaForSequenceClassification(_lowercase )
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_lowercase , return_attention_mask=_lowercase , )
feature_extractor.save_pretrained(_lowercase )
elif is_finetuned:
if dict_path:
_A = Dictionary.load(_lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_lowercase , '''vocab.json''' )
if not os.path.isdir(_lowercase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowercase ) )
return
os.makedirs(_lowercase , exist_ok=_lowercase )
_A = target_dict.indices
# fairseq has the <pad> and <s> switched
_A = 0
_A = 1
with open(_lowercase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_lowercase , _lowercase )
_A = WavaVecaCTCTokenizer(
_lowercase , 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=_lowercase , )
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_lowercase , return_attention_mask=_lowercase , )
_A = WavaVecaProcessor(feature_extractor=_lowercase , tokenizer=_lowercase )
processor.save_pretrained(_lowercase )
_A = WavaVecaForCTC(_lowercase )
else:
_A = WavaVecaForPreTraining(_lowercase )
if is_finetuned or is_seq_class:
_A ,_A ,_A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A = argparse.Namespace(task='''audio_pretraining''' )
_A = fairseq.tasks.setup_task(_lowercase )
_A ,_A ,_A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowercase )
_A = model[0].eval()
recursively_load_weights(_lowercase , _lowercase , not is_finetuned )
hf_wavavec.save_pretrained(_lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
parser.add_argument(
'--is_seq_class',
action='store_true',
help='Whether the model to convert is a fine-tuned sequence classification model or not',
)
__A = parser.parse_args()
__A = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 484 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['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
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 484 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def lowerCAmelCase__(self ):
'''simple docstring'''
super().tearDown()
gc.collect()
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Optional[int] = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=_lowercase , dtype=jnp.bfloataa )
__a : Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
__a : List[str] = controlnet_params
__a : Any = """bird"""
__a : Tuple = jax.device_count()
__a : int = pipe.prepare_text_inputs([prompts] * num_samples )
__a : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
__a : Tuple = pipe.prepare_image_inputs([canny_image] * num_samples )
__a : Union[str, Any] = jax.random.PRNGKey(0 )
__a : Optional[int] = jax.random.split(_lowercase , jax.device_count() )
__a : str = replicate(_lowercase )
__a : Optional[int] = shard(_lowercase )
__a : Optional[int] = shard(_lowercase )
__a : Tuple = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=50 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
__a : List[Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a : Union[str, Any] = images[0, 253:256, 253:256, -1]
__a : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a : Tuple = jnp.array(
[0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Any = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=_lowercase , dtype=jnp.bfloataa )
__a : str = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
__a : List[str] = controlnet_params
__a : Union[str, Any] = """Chef in the kitchen"""
__a : List[str] = jax.device_count()
__a : Dict = pipe.prepare_text_inputs([prompts] * num_samples )
__a : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
__a : Optional[Any] = pipe.prepare_image_inputs([pose_image] * num_samples )
__a : Dict = jax.random.PRNGKey(0 )
__a : int = jax.random.split(_lowercase , jax.device_count() )
__a : int = replicate(_lowercase )
__a : Optional[int] = shard(_lowercase )
__a : str = shard(_lowercase )
__a : Optional[int] = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=50 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
__a : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a : List[str] = images[0, 253:256, 253:256, -1]
__a : str = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a : Tuple = jnp.array(
[[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 711 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
def __init__(self , _lowercase , _lowercase=None , _lowercase=None , _lowercase=0 ):
'''simple docstring'''
__a : Any = 1.0 if scale is None else scale
__a : str = 0.0 if loc is None else loc
super().__init__(_lowercase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=_lowercase )] )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.base_dist.mean * self.scale + self.loc
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.base_dist.variance * self.scale**2
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.variance.sqrt()
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__(self , _lowercase , _lowercase , _lowercase , **_lowercase ):
'''simple docstring'''
super().__init__(**_lowercase )
__a : str = args_dim
__a : List[Any] = nn.ModuleList([nn.Linear(_lowercase , _lowercase ) for dim in args_dim.values()] )
__a : Dict = domain_map
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a : List[Any] = [proj(_lowercase ) for proj in self.proj]
return self.domain_map(*_lowercase )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__(self , _lowercase ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = function
def lowerCAmelCase__(self , _lowercase , *_lowercase ):
'''simple docstring'''
return self.function(_lowercase , *_lowercase )
class SCREAMING_SNAKE_CASE__ :
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
def __init__(self , _lowercase = 1 ):
'''simple docstring'''
__a : Optional[int] = dim
__a : str = {k: dim * self.args_dim[k] for k in self.args_dim}
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
if self.dim == 1:
return self.distribution_class(*_lowercase )
else:
return Independent(self.distribution_class(*_lowercase ) , 1 )
def lowerCAmelCase__(self , _lowercase , _lowercase = None , _lowercase = None , ):
'''simple docstring'''
__a : Tuple = self._base_distribution(_lowercase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(_lowercase , loc=_lowercase , scale=_lowercase , event_dim=self.event_dim )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return () if self.dim == 1 else (self.dim,)
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return len(self.event_shape )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 0.0
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
return ParameterProjection(
in_features=_lowercase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def lowerCAmelCase__(self , *_lowercase ):
'''simple docstring'''
raise NotImplementedError()
@staticmethod
def lowerCAmelCase__(_lowercase ):
'''simple docstring'''
return (x + torch.sqrt(torch.square(_lowercase ) + 4.0 )) / 2.0
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"df": 1, "loc": 1, "scale": 1}
_lowerCAmelCase = StudentT
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
__a : int = cls.squareplus(_lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
__a : Optional[Any] = 2.0 + cls.squareplus(_lowercase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"loc": 1, "scale": 1}
_lowerCAmelCase = Normal
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase ):
'''simple docstring'''
__a : str = cls.squareplus(_lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = {"total_count": 1, "logits": 1}
_lowerCAmelCase = NegativeBinomial
@classmethod
def lowerCAmelCase__(cls , _lowercase , _lowercase ):
'''simple docstring'''
__a : Union[str, Any] = cls.squareplus(_lowercase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a , __a : Optional[Any] = distr_args
if self.dim == 1:
return self.distribution_class(total_count=_lowercase , logits=_lowercase )
else:
return Independent(self.distribution_class(total_count=_lowercase , logits=_lowercase ) , 1 )
def lowerCAmelCase__(self , _lowercase , _lowercase = None , _lowercase = None ):
'''simple docstring'''
__a , __a : List[Any] = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 63 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ : Optional[Any] = logging.getLogger(__name__)
@dataclass(frozen=UpperCAmelCase__ )
class _a :
"""simple docstring"""
A_ = 42
A_ = 42
A_ = None
A_ = None
A_ = None
@dataclass(frozen=UpperCAmelCase__ )
class _a :
"""simple docstring"""
A_ = 42
A_ = None
A_ = None
A_ = None
A_ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
A_ = 42
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase=False , _UpperCAmelCase = False , ) -> Optional[Any]:
UpperCamelCase_ = hans_processors[task]()
UpperCamelCase_ = os.path.join(
_UpperCAmelCase , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(_UpperCAmelCase ) , _UpperCAmelCase , ) , )
UpperCamelCase_ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase_ , UpperCamelCase_ = label_list[2], label_list[1]
UpperCamelCase_ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCamelCase_ = cached_features_file + '.lock'
with FileLock(_UpperCAmelCase ):
if os.path.exists(_UpperCAmelCase ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
UpperCamelCase_ = torch.load(_UpperCAmelCase )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
UpperCamelCase_ = (
processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
)
logger.info('Training examples: %s' , len(_UpperCAmelCase ) )
UpperCamelCase_ = hans_convert_examples_to_features(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
logger.info('Saving features into cached file %s' , _UpperCAmelCase )
torch.save(self.features , _UpperCAmelCase )
def __len__( self ) -> int:
return len(self.features )
def __getitem__( self , _UpperCAmelCase ) -> InputFeatures:
return self.features[i]
def _UpperCAmelCase ( self ) -> List[Any]:
return self.label_list
if is_tf_available():
import tensorflow as tf
class _a :
"""simple docstring"""
A_ = 42
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 128 , _UpperCAmelCase=False , _UpperCAmelCase = False , ) -> Dict:
UpperCamelCase_ = hans_processors[task]()
UpperCamelCase_ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase_ , UpperCamelCase_ = label_list[2], label_list[1]
UpperCamelCase_ = label_list
UpperCamelCase_ = processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
UpperCamelCase_ = hans_convert_examples_to_features(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 10000 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(_UpperCAmelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCamelCase_ = tf.data.Dataset.from_generator(
_UpperCAmelCase , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _UpperCAmelCase ( self ) -> List[Any]:
return self.dataset
def __len__( self ) -> str:
return len(self.features )
def __getitem__( self , _UpperCAmelCase ) -> InputFeatures:
return self.features[i]
def _UpperCAmelCase ( self ) -> List[str]:
return self.label_list
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Optional[Any]:
return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase , 'heuristics_train_set.txt' ) ) , 'train' )
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> List[str]:
return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def _UpperCAmelCase ( self ) -> List[str]:
return ["contradiction", "entailment", "neutral"]
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
UpperCamelCase_ = []
for i, line in enumerate(_UpperCAmelCase ):
if i == 0:
continue
UpperCamelCase_ = '%s-%s' % (set_type, line[0])
UpperCamelCase_ = line[5]
UpperCamelCase_ = line[6]
UpperCamelCase_ = line[7][2:] if line[7].startswith('ex' ) else line[7]
UpperCamelCase_ = line[0]
examples.append(InputExample(guid=_UpperCAmelCase , text_a=_UpperCAmelCase , text_b=_UpperCAmelCase , label=_UpperCAmelCase , pairID=_UpperCAmelCase ) )
return examples
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , ):
UpperCamelCase_ = {label: i for i, label in enumerate(__lowercase)}
UpperCamelCase_ = []
for ex_index, example in tqdm.tqdm(enumerate(__lowercase) , desc='convert examples to features'):
if ex_index % 10000 == 0:
logger.info('Writing example %d' % (ex_index))
UpperCamelCase_ = tokenizer(
example.text_a , example.text_b , add_special_tokens=__lowercase , max_length=__lowercase , padding='max_length' , truncation=__lowercase , return_overflowing_tokens=__lowercase , )
UpperCamelCase_ = label_map[example.label] if example.label in label_map else 0
UpperCamelCase_ = int(example.pairID)
features.append(InputFeatures(**__lowercase , label=__lowercase , pairID=__lowercase))
for i, example in enumerate(examples[:5]):
logger.info('*** Example ***')
logger.info(f"""guid: {example}""")
logger.info(f"""features: {features[i]}""")
return features
snake_case__ : str = {
"""hans""": 3,
}
snake_case__ : Optional[Any] = {
"""hans""": HansProcessor,
}
| 23 |
'''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
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Dict = {'vocab_file': 'sentencepiece.bpe.model'}
__snake_case : Dict = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
__snake_case : int = {
'camembert-base': 512,
}
__snake_case : List[str] = '▁'
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ['input_ids', 'attention_mask']
def __init__( self : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any="<s>" , lowerCAmelCase_ : int="</s>" , lowerCAmelCase_ : Optional[Any]="</s>" , lowerCAmelCase_ : Union[str, Any]="<s>" , lowerCAmelCase_ : List[Any]="<unk>" , lowerCAmelCase_ : Optional[Any]="<pad>" , lowerCAmelCase_ : Tuple="<mask>" , lowerCAmelCase_ : int=["<s>NOTUSED", "</s>NOTUSED"] , lowerCAmelCase_ : Optional[Dict[str, Any]] = None , **lowerCAmelCase_ : Any , ) -> None:
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it
A__ : Any =AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) else mask_token
A__ : 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_ , )
A__ : Optional[Any] =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase_ ) )
A__ : Tuple =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>
A__ : List[str] ={"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
A__ : str =len(self.fairseq_tokens_to_ids )
A__ : Optional[int] =len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
A__ : Optional[Any] ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowercase__ ( self : str , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : 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__ : Optional[Any] =[self.cls_token_id]
A__ : List[str] =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase__ ( self : Dict , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : bool = False ) -> List[int]:
'''simple docstring'''
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 : List[str] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ : Dict =[self.sep_token_id]
A__ : List[str] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowercase__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def lowercase__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
A__ : Optional[int] ={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 : Optional[Any] , lowerCAmelCase_ : str ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCAmelCase_ , out_type=lowerCAmelCase_ )
def lowercase__ ( self : Union[str, Any] , lowerCAmelCase_ : Optional[int] ) -> int:
'''simple docstring'''
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 : Optional[Any] , lowerCAmelCase_ : int ) -> Any:
'''simple docstring'''
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[Any] , lowerCAmelCase_ : Dict ) -> Union[str, Any]:
'''simple docstring'''
A__ : Any =[]
A__ : Optional[int] =""""""
A__ : 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
A__ : Any =True
A__ : Tuple =[]
else:
current_sub_tokens.append(lowerCAmelCase_ )
A__ : Dict =False
out_string += self.sp_model.decode(lowerCAmelCase_ )
return out_string.strip()
def __getstate__( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
A__ : Dict =self.__dict__.copy()
A__ : Union[str, Any] =None
return state
def __setstate__( self : Union[str, Any] , lowerCAmelCase_ : List[Any] ) -> Tuple:
'''simple docstring'''
A__ : Union[str, Any] =d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
A__ : List[str] ={}
A__ : str =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase__ ( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
A__ : List[Any] =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:
A__ : Optional[Any] =self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase_ )
return (out_vocab_file,)
| 215 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : str = logging.get_logger(__name__)
__snake_case : Tuple = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = 'time_series_transformer'
__snake_case = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : str = "student_t" , lowerCAmelCase_ : str = "nll" , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase_ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : int = 32 , lowerCAmelCase_ : int = 32 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : str = "gelu" , lowerCAmelCase_ : int = 64 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : int = 1_00 , lowerCAmelCase_ : float = 0.02 , lowerCAmelCase_ : Dict=True , **lowerCAmelCase_ : str , ) -> Union[str, Any]:
'''simple docstring'''
A__ : Any =prediction_length
A__ : Any =context_length or prediction_length
A__ : Dict =distribution_output
A__ : str =loss
A__ : int =input_size
A__ : Optional[int] =num_time_features
A__ : Optional[int] =lags_sequence
A__ : str =scaling
A__ : Dict =num_dynamic_real_features
A__ : Tuple =num_static_real_features
A__ : List[Any] =num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
A__ : Any =cardinality
else:
A__ : Optional[int] =[0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
A__ : Optional[int] =embedding_dimension
else:
A__ : int =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
A__ : List[str] =num_parallel_samples
# Transformer architecture configuration
A__ : int =input_size * len(lowerCAmelCase_ ) + self._number_of_features
A__ : List[Any] =d_model
A__ : int =encoder_attention_heads
A__ : int =decoder_attention_heads
A__ : Optional[int] =encoder_ffn_dim
A__ : List[Any] =decoder_ffn_dim
A__ : int =encoder_layers
A__ : List[Any] =decoder_layers
A__ : int =dropout
A__ : Optional[Any] =attention_dropout
A__ : int =activation_dropout
A__ : List[Any] =encoder_layerdrop
A__ : List[str] =decoder_layerdrop
A__ : Optional[Any] =activation_function
A__ : str =init_std
A__ : Dict =use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowercase__ ( self : Optional[Any] ) -> int:
'''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
)
| 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Tuple = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : int = [
'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:
__snake_case : Union[str, Any] = [
'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
__snake_case : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 687 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Dict = False ) -> Optional[Any]:
if not arr:
return 0
__snake_case : int = 0 if allow_empty_subarrays else float('-inf' )
__snake_case : Tuple = 0.0
for num in arr:
__snake_case : Dict = max(0 if allow_empty_subarrays else num ,curr_sum + num )
__snake_case : Optional[int] = max(UpperCamelCase_ ,UpperCamelCase_ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
A__ : Dict = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"""{max_subarray_sum(nums) = }""")
| 286 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l''': '''ABABA''',
'''m''': '''ABABB''',
'''n''': '''ABBAA''',
'''o''': '''ABBAB''',
'''p''': '''ABBBA''',
'''q''': '''ABBBB''',
'''r''': '''BAAAA''',
'''s''': '''BAAAB''',
'''t''': '''BAABA''',
'''u''': '''BAABB''',
'''v''': '''BBBAB''',
'''w''': '''BABAA''',
'''x''': '''BABAB''',
'''y''': '''BABBA''',
'''z''': '''BABBB''',
''' ''': ''' ''',
}
__SCREAMING_SNAKE_CASE : Tuple = {value: key for key, value in encode_dict.items()}
def a_ ( UpperCamelCase_ ):
A_ = ""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("encode() accepts only letters of the alphabet and spaces" )
return encoded
def a_ ( UpperCamelCase_ ):
if set(UpperCamelCase_ ) - {"A", "B", " "} != set():
raise Exception("decode() accepts only 'A', 'B' and spaces" )
A_ = ""
for word in coded.split():
while len(UpperCamelCase_ ) != 0:
decoded += decode_dict[word[:5]]
A_ = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 452 | 0 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
A_ : List[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_ : Union[str, Any] = spec.loader.load_module()
A_ : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
A_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCamelCase_ ( ):
lowerCamelCase__ : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCamelCase__ : Dict = False
# source code of `config_class`
lowerCamelCase__ : str = inspect.getsource(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = _re_checkpoint.findall(_lowerCamelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCamelCase__ : Any = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowerCamelCase__ : Any = True
break
lowerCamelCase__ : Dict = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase__ : Optional[Any] = '\n'.join(sorted(_lowerCamelCase ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 696 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
UpperCAmelCase__ = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def _UpperCAmelCase ( ) -> int:
_snake_case = _ask_options(
'''In which compute environment are you running?''' , ['''This machine''', '''AWS (Amazon SageMaker)'''] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
_snake_case = get_sagemaker_input()
else:
_snake_case = get_cluster_input()
return config
def _UpperCAmelCase ( __lowerCamelCase : Any=None ) -> Optional[int]:
if subparsers is not None:
_snake_case = subparsers.add_parser('''config''' , description=__lowerCamelCase )
else:
_snake_case = argparse.ArgumentParser('''Accelerate config command''' , description=__lowerCamelCase )
parser.add_argument(
'''--config_file''' , default=__lowerCamelCase , 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=__lowerCamelCase )
return parser
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Optional[Any]:
_snake_case = get_user_input()
if args.config_file is not None:
_snake_case = args.config_file
else:
if not os.path.isdir(__lowerCamelCase ):
os.makedirs(__lowerCamelCase )
_snake_case = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(__lowerCamelCase )
else:
config.to_yaml_file(__lowerCamelCase )
print(f'''accelerate configuration saved at {config_file}''' )
def _UpperCAmelCase ( ) -> Tuple:
_snake_case = config_command_parser()
_snake_case = parser.parse_args()
config_command(__lowerCamelCase )
if __name__ == "__main__":
main()
| 224 |
"""simple docstring"""
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
#
########################################################################
UpperCAmelCase__ = 16
UpperCAmelCase__ = 32
def _UpperCAmelCase ( __lowerCamelCase : Accelerator , __lowerCamelCase : int = 16 ) -> Optional[int]:
_snake_case = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_snake_case = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase : Tuple ):
# max_length=None => use the model max length (it's actually the default)
_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():
_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
_snake_case = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_snake_case = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_snake_case = 16
elif accelerator.mixed_precision != "no":
_snake_case = 8
else:
_snake_case = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
_snake_case = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
_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
UpperCAmelCase__ = mocked_dataloaders # noqa: F811
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : Union[str, Any] ) -> str:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
_snake_case = 2
# Initialize accelerator
_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
_snake_case = config['''lr''']
_snake_case = int(config['''num_epochs'''] )
_snake_case = int(config['''seed'''] )
_snake_case = int(config['''batch_size'''] )
_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 : str ):
# 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)
_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).
_snake_case = model.to(accelerator.device )
# Instantiate optimizer
_snake_case = AdamW(params=model.parameters() , lr=__lowerCamelCase )
_snake_case , _snake_case = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate scheduler
_snake_case = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , 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.
_snake_case , _snake_case , _snake_case , _snake_case , _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 )
_snake_case = model(**__lowerCamelCase )
_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():
_snake_case = model(**__lowerCamelCase )
_snake_case = outputs.logits.argmax(dim=-1 )
_snake_case , _snake_case = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
_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 _UpperCAmelCase ( ) -> Union[str, Any]:
_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.''' )
_snake_case = parser.parse_args()
_snake_case = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 224 | 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
snake_case = 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")
snake_case = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
with open(lowerCAmelCase__ , "rb" ) as f:
_lowerCAmelCase : Dict = Image.open(lowerCAmelCase__ )
return im.convert("RGB" )
@dataclass
class __A :
'''simple docstring'''
a_ = field(
default=snake_case__ ,metadata={
'''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
} ,)
a_ = field(
default=snake_case__ ,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
a_ = field(default=snake_case__ ,metadata={'''help''': '''A folder containing the training data.'''} )
a_ = field(default=snake_case__ ,metadata={'''help''': '''A folder containing the validation data.'''} )
a_ = field(
default=0.1_5 ,metadata={'''help''': '''Percent to split off of train for validation.'''} )
a_ = field(
default=snake_case__ ,metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} ,)
a_ = field(
default=snake_case__ ,metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} ,)
def SCREAMING_SNAKE_CASE__ ( self ):
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 __A :
'''simple docstring'''
a_ = field(
default='''google/vit-base-patch16-224-in21k''' ,metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ,)
a_ = field(
default=snake_case__ ,metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case__ )} ,)
a_ = field(
default=snake_case__ ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
a_ = field(
default=snake_case__ ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
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=snake_case__ ,metadata={'''help''': '''Name or path of preprocessor config.'''} )
a_ = field(
default=snake_case__ ,metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} ,)
a_ = field(
default=snake_case__ ,metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} ,)
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = torch.stack([example["pixel_values"] for example in examples] )
_lowerCAmelCase : Tuple = torch.tensor([example["labels"] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def UpperCamelCase_ ( ):
"""simple docstring"""
_lowerCAmelCase : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = 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 : List[str] = 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 : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowerCAmelCase : Tuple = 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 : Optional[int] = 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 : str = {}
if data_args.train_dir is not None:
_lowerCAmelCase : Any = os.path.join(data_args.train_dir , "**" )
if data_args.validation_dir is not None:
_lowerCAmelCase : Union[str, Any] = os.path.join(data_args.validation_dir , "**" )
_lowerCAmelCase : Tuple = 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 : str = 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 : Union[str, Any] = dataset["train"].train_test_split(data_args.train_val_split )
_lowerCAmelCase : List[Any] = split["train"]
_lowerCAmelCase : int = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_lowerCAmelCase : str = dataset["train"].features["labels"].names
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = {}, {}
for i, label in enumerate(lowerCAmelCase__ ):
_lowerCAmelCase : Dict = str(lowerCAmelCase__ )
_lowerCAmelCase : List[Any] = label
# Load the accuracy metric from the datasets package
_lowerCAmelCase : Optional[int] = 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(lowerCAmelCase__ ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
_lowerCAmelCase : Dict = 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 : int = 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 : List[Any] = 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 : int = image_processor.size["shortest_edge"]
else:
_lowerCAmelCase : str = (image_processor.size["height"], image_processor.size["width"])
_lowerCAmelCase : List[str] = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
_lowerCAmelCase : Dict = Compose(
[
RandomResizedCrop(lowerCAmelCase__ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
_lowerCAmelCase : Union[str, Any] = Compose(
[
Resize(lowerCAmelCase__ ),
CenterCrop(lowerCAmelCase__ ),
ToTensor(),
normalize,
] )
def train_transforms(lowerCAmelCase__ ):
_lowerCAmelCase : Dict = [
_train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
]
return example_batch
def val_transforms(lowerCAmelCase__ ):
_lowerCAmelCase : Any = [_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 : Tuple = (
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 : Tuple = (
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 : List[str] = 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 : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
_lowerCAmelCase : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowerCAmelCase : List[str] = last_checkpoint
_lowerCAmelCase : str = 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 : Any = trainer.evaluate()
trainer.log_metrics("eval" , lowerCAmelCase__ )
trainer.save_metrics("eval" , lowerCAmelCase__ )
# Write model card and (optionally) push to hub
_lowerCAmelCase : Union[str, Any] = {
"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()
| 587 | def UpperCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 587 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
'''tokenization_lxmert''': ['''LxmertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''LxmertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''LxmertEncoder''',
'''LxmertForPreTraining''',
'''LxmertForQuestionAnswering''',
'''LxmertModel''',
'''LxmertPreTrainedModel''',
'''LxmertVisualFeatureEncoder''',
'''LxmertXLayer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLxmertForPreTraining''',
'''TFLxmertMainLayer''',
'''TFLxmertModel''',
'''TFLxmertPreTrainedModel''',
'''TFLxmertVisualFeatureEncoder''',
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 91 | from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = 42
lowerCamelCase_ = None
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCAmelCase_ ( cls ):
"""simple docstring"""
return F'''`pip install {cls.pip_package or cls.name}`'''
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''optuna'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_optuna_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_optuna(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_optuna(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''ray'''
lowerCamelCase_ = '''\'ray[tune]\''''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_ray_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_ray(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_ray(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''sigopt'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_sigopt_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_sigopt(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_sigopt(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''wandb'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_wandb_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_wandb(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_wandb(lowercase )
_UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCamelCase ( ):
'''simple docstring'''
A_ : List[str] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
A_ : List[str] = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'''{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 558 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase__ ( lowerCAmelCase__ , unittest.TestCase):
'''simple docstring'''
_A = CpmAntTokenizer
_A = False
def _lowerCamelCase ( self :Optional[int] ) -> str:
super().setUp()
__UpperCamelCase : Dict = [
"<d>",
"</d>",
"<s>",
"</s>",
"</_>",
"<unk>",
"<pad>",
"</n>",
"我",
"是",
"C",
"P",
"M",
"A",
"n",
"t",
]
__UpperCamelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
@tooslow
def _lowerCamelCase ( self :Any ) -> Tuple:
__UpperCamelCase : List[Any] = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b" )
__UpperCamelCase : Optional[int] = "今天天气真好！"
__UpperCamelCase : Optional[int] = ["今天", "天气", "真", "好", "！"]
__UpperCamelCase : str = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
__UpperCamelCase : str = "今天天气真好！"
__UpperCamelCase : str = [tokenizer.bos_token] + tokens
__UpperCamelCase : Any = [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
__UpperCamelCase : List[Any] = tokenizer.decode(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase ) | 712 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : List[Any] = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Dict = [
'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwinForImageClassification',
'SwinForMaskedImageModeling',
'SwinModel',
'SwinPreTrainedModel',
'SwinBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[int] = [
'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSwinForImageClassification',
'TFSwinForMaskedImageModeling',
'TFSwinModel',
'TFSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
lowercase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 94 | 0 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
__snake_case = ['a', 'b', 'c']
# Defaults to last layer if both are None
__snake_case = get_aligned_output_features_output_indices(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,["c"] )
self.assertEqual(_lowerCAmelCase ,[2] )
# Out indices set to match out features
__snake_case = get_aligned_output_features_output_indices(["a", "c"] ,_lowerCAmelCase ,_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,["a", "c"] )
self.assertEqual(_lowerCAmelCase ,[0, 2] )
# Out features set to match out indices
__snake_case = get_aligned_output_features_output_indices(_lowerCAmelCase ,[0, 2] ,_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,["a", "c"] )
self.assertEqual(_lowerCAmelCase ,[0, 2] )
# Out features selected from negative indices
__snake_case = get_aligned_output_features_output_indices(_lowerCAmelCase ,[-3, -1] ,_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,["a", "c"] )
self.assertEqual(_lowerCAmelCase ,[-3, -1] )
def UpperCamelCase_ ( self : List[str] ):
"""simple docstring"""
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["a", "b"] ,(0, 1) ,_lowerCAmelCase )
# Out features must be a list
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(("a", "b") ,(0, 1) ,["a", "b"] )
# Out features must be a subset of stage names
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["a", "b"] ,(0, 1) ,["a"] )
# Out indices must be a list or tuple
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(_lowerCAmelCase ,0 ,["a", "b"] )
# Out indices must be a subset of stage names
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(_lowerCAmelCase ,(0, 1) ,["a"] )
# Out features and out indices must be the same length
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["a", "b"] ,(0,) ,["a", "b", "c"] )
# Out features should match out indices
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["a", "b"] ,(0, 2) ,["a", "b", "c"] )
# Out features and out indices should be in order
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["b", "a"] ,(0, 1) ,["a", "b"] )
# Check passes with valid inputs
verify_out_features_out_indices(["a", "b", "d"] ,(0, 1, -1) ,["a", "b", "c", "d"] )
def UpperCamelCase_ ( self : Any ):
"""simple docstring"""
__snake_case = BackboneMixin()
__snake_case = ['a', 'b', 'c']
__snake_case = ['a', 'c']
__snake_case = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features ,["a", "c"] )
self.assertEqual(backbone.out_indices ,[0, 2] )
# Check out features and indices are updated correctly
__snake_case = ['a', 'b']
self.assertEqual(backbone.out_features ,["a", "b"] )
self.assertEqual(backbone.out_indices ,[0, 1] )
__snake_case = [-3, -1]
self.assertEqual(backbone.out_features ,["a", "c"] )
self.assertEqual(backbone.out_indices ,[-3, -1] )
| 524 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
a_ = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class A_:
"""simple docstring"""
a_ : str
a_ : Optional[str] = None
a_ : Optional[Union[str, int]] = None
a_ : Optional[Union[str, int]] = None
a_ : Optional[Union[str, int]] = None
def _lowerCAmelCase ( self ):
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[str] = _str_to_version_tuple(self.version_str )
def __repr__( self ):
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def _lowerCAmelCase ( self ):
return self.major, self.minor, self.patch
def _lowerCAmelCase ( self , A ):
if isinstance(A , A ):
return Version(A )
elif isinstance(A , A ):
return other
raise TypeError(F"{other} (type {type(A )}) cannot be compared to version." )
def __eq__( self , A ):
try:
_lowerCamelCase : Dict = self._validate_operand(A )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , A ):
_lowerCamelCase : Any = self._validate_operand(A )
return self.tuple < other.tuple
def __hash__( self ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def _lowerCAmelCase ( cls , A ):
_lowerCamelCase : Optional[Any] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def _lowerCAmelCase ( self ):
return self.version_str
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = _VERSION_REG.match(__a )
if not res:
raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(__a ) for v in [res.group('major' ), res.group('minor' ), res.group('patch' )] )
def UpperCAmelCase_ ( __a : List[str] ):
'''simple docstring'''
return ".".join(str(__a ) for v in version_tuple )
| 437 | 0 |
from __future__ import annotations
def lowerCAmelCase ( UpperCamelCase_: list , UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: int ) -> list:
'''simple docstring'''
_a = []
_a , _a = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
_a = result + left + right
return input_list
def lowerCAmelCase ( UpperCamelCase_: list ) -> list:
'''simple docstring'''
if len(snake_case__ ) <= 1:
return input_list
_a = list(snake_case__ )
# iteration for two-way merging
_a = 2
while p <= len(snake_case__ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(snake_case__ ) , snake_case__ ):
_a = i
_a = i + p - 1
_a = (low + high + 1) // 2
_a = merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# final merge of last two parts
if p * 2 >= len(snake_case__ ):
_a = i
_a = merge(snake_case__ , 0 , snake_case__ , len(snake_case__ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
UpperCamelCase = input("""Enter numbers separated by a comma:\n""").strip()
if user_input == "":
UpperCamelCase = []
else:
UpperCamelCase = [int(item.strip()) for item in user_input.split(""",""")]
print(iter_merge_sort(unsorted))
| 717 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
UpperCamelCase = """pt"""
elif is_tf_available():
UpperCamelCase = """tf"""
else:
UpperCamelCase = """jax"""
class lowercase_ (_UpperCAmelCase, unittest.TestCase ):
A__ : List[str] = ByTaTokenizer
A__ : List[Any] = False
def lowerCamelCase__ ( self ) ->Any:
'''simple docstring'''
super().setUp()
_a = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase__ ( self ) ->List[str]:
'''simple docstring'''
return ByTaTokenizer.from_pretrained("google/byt5-small" )
def lowerCamelCase__ ( self , **a_ ) ->ByTaTokenizer:
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **a_ )
def lowerCamelCase__ ( self , a_ , a_=False , a_=2_0 , a_=5 ) ->Tuple[str, list]:
'''simple docstring'''
_a = []
for i in range(len(a_ ) ):
try:
_a = tokenizer.decode([i] , clean_up_tokenization_spaces=a_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
_a = list(filter(lambda a_ : re.match(R"^[ a-zA-Z]+$" , t[1] ) , a_ ) )
_a = list(filter(lambda a_ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=a_ ) , a_ ) )
if max_length is not None and len(a_ ) > max_length:
_a = toks[:max_length]
if min_length is not None and len(a_ ) < min_length and len(a_ ) > 0:
while len(a_ ) < min_length:
_a = toks + toks
# toks_str = [t[1] for t in toks]
_a = [t[0] for t in toks]
# Ensure consistency
_a = tokenizer.decode(a_ , clean_up_tokenization_spaces=a_ )
if " " not in output_txt and len(a_ ) > 1:
_a = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=a_ )
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=a_ )
)
if with_prefix_space:
_a = " " + output_txt
_a = tokenizer.encode(a_ , add_special_tokens=a_ )
return output_txt, output_ids
def lowerCamelCase__ ( self ) ->Union[str, Any]:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"] )
_a = tokenizer(["hi", "I went to the gym", ""] )
self.assertListEqual(batch_with_eos_added["input_ids"] , batch_without_eos_added["input_ids"] )
def lowerCamelCase__ ( self ) ->Tuple:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = "Unicode €."
_a = tokenizer(a_ )
_a = [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
self.assertEqual(encoded["input_ids"] , a_ )
# decoding
_a = tokenizer.decode(a_ )
self.assertEqual(a_ , "Unicode €.</s>" )
_a = tokenizer("e è é ê ë" )
_a = [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
self.assertEqual(encoded["input_ids"] , a_ )
# decoding
_a = tokenizer.decode(a_ )
self.assertEqual(a_ , "e è é ê ë</s>" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë" ) ) , "e è é ê ë</s>" )
def lowerCamelCase__ ( self ) ->int:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = ["A long paragraph for summarization.", "Another paragraph for summarization."]
# fmt: off
_a = [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
# fmt: on
_a = tokenizer(a_ , padding=a_ , return_tensors=a_ )
self.assertIsInstance(a_ , a_ )
if FRAMEWORK != "jax":
_a = list(batch.input_ids.numpy()[0] )
else:
_a = list(batch.input_ids.tolist()[0] )
self.assertListEqual(a_ , a_ )
self.assertEqual((2, 3_7) , batch.input_ids.shape )
self.assertEqual((2, 3_7) , batch.attention_mask.shape )
def lowerCamelCase__ ( self ) ->Dict:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_a = tokenizer(a_ , padding=a_ , return_tensors=a_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids" , a_ )
self.assertIn("attention_mask" , a_ )
self.assertNotIn("decoder_input_ids" , a_ )
self.assertNotIn("decoder_attention_mask" , a_ )
def lowerCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = [
"Summary of the text.",
"Another summary.",
]
_a = tokenizer(
text_target=a_ , max_length=3_2 , padding="max_length" , truncation=a_ , return_tensors=a_ )
self.assertEqual(3_2 , targets["input_ids"].shape[1] )
def lowerCamelCase__ ( self ) ->Dict:
'''simple docstring'''
_a = self.ta_base_tokenizer
_a = ["A long paragraph for summarization. </s>"]
_a = ["Summary of the text. </s>"]
# fmt: off
_a = [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
_a = [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
# fmt: on
_a = tokenizer(a_ , text_target=a_ )
self.assertEqual(a_ , batch["input_ids"][0] )
self.assertEqual(a_ , batch["labels"][0] )
def lowerCamelCase__ ( self ) ->int:
'''simple docstring'''
_a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
_a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
_a = tempfile.mkdtemp()
_a = " He is very happy, UNwant\u00E9d,running"
_a = tokenizer.encode(a_ , add_special_tokens=a_ )
tokenizer.save_pretrained(a_ )
_a = tokenizer.__class__.from_pretrained(a_ )
_a = after_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
shutil.rmtree(a_ )
_a = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
_a = tempfile.mkdtemp()
_a = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"] )
_a = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token" )
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
_a = tokenizer.encode(a_ , add_special_tokens=a_ )
tokenizer.save_pretrained(a_ )
_a = tokenizer.__class__.from_pretrained(a_ )
_a = after_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
self.assertIn("new_additional_special_token" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
_a = tokenizer.__class__.from_pretrained(a_ , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(a_ )
def lowerCamelCase__ ( self ) ->List[str]:
'''simple docstring'''
_a = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(a_ )
with open(os.path.join(a_ , "special_tokens_map.json" ) , encoding="utf-8" ) as json_file:
_a = json.load(a_ )
with open(os.path.join(a_ , "tokenizer_config.json" ) , encoding="utf-8" ) as json_file:
_a = json.load(a_ )
_a = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
_a = added_tokens_extra_ids + [
"an_additional_special_token"
]
_a = added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(a_ , "special_tokens_map.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(a_ , a_ )
with open(os.path.join(a_ , "tokenizer_config.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(a_ , a_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
_a = tokenizer_class.from_pretrained(
a_ , )
self.assertIn(
"an_additional_special_token" , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
["an_additional_special_token"] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
_a = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" , lstrip=a_ )]
_a = tokenizer_class.from_pretrained(
a_ , additional_special_tokens=a_ , )
self.assertIn("a_new_additional_special_token" , tokenizer.additional_special_tokens )
self.assertEqual(
["a_new_additional_special_token"] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"] ) ) , )
def lowerCamelCase__ ( self ) ->List[str]:
'''simple docstring'''
_a = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(a_ )
_a = tokenizer_class.from_pretrained(a_ )
self.assertTrue(tokenizer.decode([2_5_5] ) == "" )
def lowerCamelCase__ ( self ) ->Optional[int]:
'''simple docstring'''
pass
def lowerCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
pass
def lowerCamelCase__ ( self ) ->Tuple:
'''simple docstring'''
pass
def lowerCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
pass
def lowerCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
_a = self.get_tokenizers(fast=a_ , do_lower_case=a_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
_a = ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
_a = tokenizer.convert_tokens_to_string(a_ )
self.assertIsInstance(a_ , a_ )
def lowerCamelCase__ ( self ) ->Union[str, Any]:
'''simple docstring'''
_a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
_a = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
_a = 0
_a = tokenizer.convert_ids_to_tokens(
a_ , skip_special_tokens=a_ )
for attr in attributes_list:
setattr(a_ , attr + "_id" , a_ )
self.assertEqual(getattr(a_ , a_ ) , a_ )
self.assertEqual(getattr(a_ , attr + "_id" ) , a_ )
setattr(a_ , attr + "_id" , a_ )
self.assertEqual(getattr(a_ , a_ ) , a_ )
self.assertEqual(getattr(a_ , attr + "_id" ) , a_ )
setattr(a_ , "additional_special_tokens_ids" , [] )
self.assertListEqual(getattr(a_ , "additional_special_tokens" ) , [] )
self.assertListEqual(getattr(a_ , "additional_special_tokens_ids" ) , [] )
setattr(a_ , "additional_special_tokens_ids" , [token_id_to_test_setters] )
self.assertListEqual(getattr(a_ , "additional_special_tokens" ) , [token_to_test_setters] )
self.assertListEqual(getattr(a_ , "additional_special_tokens_ids" ) , [token_id_to_test_setters] )
| 612 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : str = int(__lowerCAmelCase )
# Initialize Result
SCREAMING_SNAKE_CASE__ : int = []
# 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__":
a :Any = []
a :str = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
a :str = 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()))
a :str = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
a :int = [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
a :List[Any] = 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}: ')
a :Optional[Any] = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 680 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a :Union[str, Any] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
"SpeechT5Config",
"SpeechT5HifiGanConfig",
],
"feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
"processing_speecht5": ["SpeechT5Processor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = ["SpeechT5Tokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = [
"SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"SpeechT5ForSpeechToText",
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForTextToSpeech",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
"SpeechT5HifiGan",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
a :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 680 | 1 |
'''simple docstring'''
import pprint
import requests
lowercase_ = '''https://zenquotes.io/api'''
def UpperCamelCase__ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCamelCase__ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
lowercase_ = random_quotes()
pprint.pprint(response)
| 708 | '''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"{sampling_rate}"
SCREAMING_SNAKE_CASE_: Union[str, Any] = "1"
SCREAMING_SNAKE_CASE_: int = "f32le"
SCREAMING_SNAKE_CASE_: Tuple = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
with subprocess.Popen(A__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
SCREAMING_SNAKE_CASE_: str = ffmpeg_process.communicate(A__ )
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to load audio files from filename" ) from error
SCREAMING_SNAKE_CASE_: Dict = output_stream[0]
SCREAMING_SNAKE_CASE_: Dict = np.frombuffer(A__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile" )
return audio
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = "f32le" , ):
SCREAMING_SNAKE_CASE_: Optional[int] = f"{sampling_rate}"
SCREAMING_SNAKE_CASE_: Any = "1"
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE_: Optional[Any] = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE_: int = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
SCREAMING_SNAKE_CASE_: Union[str, Any] = platform.system()
if system == "Linux":
SCREAMING_SNAKE_CASE_: Tuple = "alsa"
SCREAMING_SNAKE_CASE_: str = "default"
elif system == "Darwin":
SCREAMING_SNAKE_CASE_: Any = "avfoundation"
SCREAMING_SNAKE_CASE_: List[str] = ":0"
elif system == "Windows":
SCREAMING_SNAKE_CASE_: Tuple = "dshow"
SCREAMING_SNAKE_CASE_: str = "default"
SCREAMING_SNAKE_CASE_: str = [
"ffmpeg",
"-f",
format_,
"-i",
input_,
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-fflags",
"nobuffer",
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
SCREAMING_SNAKE_CASE_: Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
SCREAMING_SNAKE_CASE_: Optional[int] = _ffmpeg_stream(A__ , A__ )
for item in iterator:
yield item
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "f32le" , ):
if stream_chunk_s is not None:
SCREAMING_SNAKE_CASE_: List[str] = stream_chunk_s
else:
SCREAMING_SNAKE_CASE_: Optional[Any] = chunk_length_s
SCREAMING_SNAKE_CASE_: Optional[int] = ffmpeg_microphone(A__ , A__ , format_for_conversion=A__ )
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE_: str = np.intaa
SCREAMING_SNAKE_CASE_: Tuple = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE_: Dict = np.floataa
SCREAMING_SNAKE_CASE_: Dict = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
if stride_length_s is None:
SCREAMING_SNAKE_CASE_: Dict = chunk_length_s / 6
SCREAMING_SNAKE_CASE_: Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(A__ , (int, float) ):
SCREAMING_SNAKE_CASE_: Optional[int] = [stride_length_s, stride_length_s]
SCREAMING_SNAKE_CASE_: Optional[int] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
SCREAMING_SNAKE_CASE_: Union[str, Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
SCREAMING_SNAKE_CASE_: str = datetime.datetime.now()
SCREAMING_SNAKE_CASE_: Optional[int] = datetime.timedelta(seconds=A__ )
for item in chunk_bytes_iter(A__ , A__ , stride=(stride_left, stride_right) , stream=A__ ):
# Put everything back in numpy scale
SCREAMING_SNAKE_CASE_: Tuple = np.frombuffer(item["raw"] , dtype=A__ )
SCREAMING_SNAKE_CASE_: int = (
item["stride"][0] // size_of_sample,
item["stride"][1] // size_of_sample,
)
SCREAMING_SNAKE_CASE_: int = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = B""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}" )
SCREAMING_SNAKE_CASE_: Optional[Any] = 0
for raw in iterator:
acc += raw
if stream and len(A__ ) < chunk_len:
SCREAMING_SNAKE_CASE_: Any = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(A__ ) >= chunk_len:
# We are flushing the accumulator
SCREAMING_SNAKE_CASE_: Optional[int] = (_stride_left, stride_right)
SCREAMING_SNAKE_CASE_: str = {"raw": acc[:chunk_len], "stride": stride}
if stream:
SCREAMING_SNAKE_CASE_: str = False
yield item
SCREAMING_SNAKE_CASE_: str = stride_left
SCREAMING_SNAKE_CASE_: Tuple = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(A__ ) > stride_left:
SCREAMING_SNAKE_CASE_: Union[str, Any] = {"raw": acc, "stride": (_stride_left, 0)}
if stream:
SCREAMING_SNAKE_CASE_: List[str] = False
yield item
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = 2**24 # 16Mo
try:
with subprocess.Popen(A__ , stdout=subprocess.PIPE , bufsize=A__ ) as ffmpeg_process:
while True:
SCREAMING_SNAKE_CASE_: Union[str, Any] = ffmpeg_process.stdout.read(A__ )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to stream audio files from filename" ) from error
| 671 |
'''simple docstring'''
def __a ( A__ , A__ ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def __a ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 649 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = XLMRobertaTokenizer
_lowerCAmelCase : Tuple = XLMRobertaTokenizerFast
_lowerCAmelCase : int = True
_lowerCAmelCase : Tuple = True
def lowerCAmelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ = XLMRobertaTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = "<pad>"
SCREAMING_SNAKE_CASE__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(UpperCAmelCase__ ) , 1002 )
def lowerCAmelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = XLMRobertaTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCAmelCase__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def lowerCAmelCase__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
SCREAMING_SNAKE_CASE__ = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ = tokenizer_r.save_pretrained(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
SCREAMING_SNAKE_CASE__ = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE__ = tokenizer_r.from_pretrained(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase__ )
# Save tokenizer rust, legacy_format=True
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ = tokenizer_r.save_pretrained(UpperCAmelCase__ , legacy_format=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE__ = tokenizer_r.from_pretrained(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
shutil.rmtree(UpperCAmelCase__ )
# Save tokenizer rust, legacy_format=False
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ = tokenizer_r.save_pretrained(UpperCAmelCase__ , legacy_format=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.save_pretrained(UpperCAmelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE__ = tokenizer_r.from_pretrained(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer_p.from_pretrained(UpperCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
shutil.rmtree(UpperCAmelCase__ )
@cached_property
def lowerCAmelCase__ ( self ):
return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base" )
def lowerCAmelCase__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCAmelCase__ , f.name )
SCREAMING_SNAKE_CASE__ = XLMRobertaTokenizer(f.name , keep_accents=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = pickle.dumps(UpperCAmelCase__ )
pickle.loads(UpperCAmelCase__ )
def lowerCAmelCase__ ( self ):
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ = "I was born in 92000, and this is falsé."
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ = tokenizer.encode(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
@slow
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = "Hello World!"
SCREAMING_SNAKE_CASE__ = [0, 3_5378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@slow
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
SCREAMING_SNAKE_CASE__ = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
17_9459,
12_4850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
1_0114,
711,
152,
20,
6,
5,
2_2376,
642,
1221,
1_5190,
3_4153,
450,
5608,
959,
1119,
5_7702,
136,
186,
47,
1098,
2_9367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
5_0901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@slow
def lowerCAmelCase__ ( self ):
# fmt: off
SCREAMING_SNAKE_CASE__ = {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name="xlm-roberta-base" , revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3" , )
| 704 |
"""simple docstring"""
import heapq
def __lowercase ( lowerCamelCase_ : dict ):
SCREAMING_SNAKE_CASE__ = []
# 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
SCREAMING_SNAKE_CASE__ = 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
SCREAMING_SNAKE_CASE__ = 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]:
SCREAMING_SNAKE_CASE__ = 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()
_lowerCamelCase = {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)}""")
| 112 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 65 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a : List[str] = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : int = ['''MaskFormerFeatureExtractor''']
__a : str = ['''MaskFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Dict = [
'''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MaskFormerForInstanceSegmentation''',
'''MaskFormerModel''',
'''MaskFormerPreTrainedModel''',
]
__a : Optional[int] = [
'''MaskFormerSwinBackbone''',
'''MaskFormerSwinModel''',
'''MaskFormerSwinPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
__a : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure) | 397 | 0 |
"""simple docstring"""
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_UpperCamelCase = 'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Dict=None ):
'''simple docstring'''
if subparsers is not None:
__lowerCamelCase : Dict =subparsers.add_parser('''tpu-config''' , description=_description )
else:
__lowerCamelCase : str =argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
__lowerCamelCase : List[str] =parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=SCREAMING_SNAKE_CASE , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=SCREAMING_SNAKE_CASE , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
__lowerCamelCase : Union[str, Any] =parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=SCREAMING_SNAKE_CASE , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=SCREAMING_SNAKE_CASE )
return parser
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : Optional[int] =None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE ):
__lowerCamelCase : int =load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
__lowerCamelCase : str =defaults.command_file
if not args.command and defaults.commands is not None:
__lowerCamelCase : Optional[int] =defaults.commands
if not args.tpu_name:
__lowerCamelCase : List[str] =defaults.tpu_name
if not args.tpu_zone:
__lowerCamelCase : Tuple =defaults.tpu_zone
if args.accelerate_version == "dev":
__lowerCamelCase : Tuple ='''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
__lowerCamelCase : Optional[Any] ='''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , SCREAMING_SNAKE_CASE ):
__lowerCamelCase : Optional[Any] =F'accelerate=={args.accelerate_version}'
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
__lowerCamelCase : Any =[f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , SCREAMING_SNAKE_CASE ):
__lowerCamelCase : List[str] =[line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
__lowerCamelCase : str =['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [F'pip install {args.accelerate_version}']
new_cmd += args.command
__lowerCamelCase : List[str] ='''; '''.join(SCREAMING_SNAKE_CASE )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
__lowerCamelCase : Optional[Any] =['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F'Running {" ".join(SCREAMING_SNAKE_CASE )}' )
return
subprocess.run(SCREAMING_SNAKE_CASE )
print('''Successfully setup pod.''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__lowerCamelCase : Optional[int] =tpu_command_parser()
__lowerCamelCase : List[Any] =parser.parse_args()
tpu_command_launcher(SCREAMING_SNAKE_CASE )
| 700 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Tuple , __lowercase :Any ):
__lowerCamelCase : str =data
__lowerCamelCase : Any =None
def __repr__( self :Optional[Any] ):
return f'Node({self.data})'
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Optional[int] ):
__lowerCamelCase : Union[str, Any] =None
def __iter__( self :Optional[int] ):
__lowerCamelCase : Optional[Any] =self.head
while node:
yield node.data
__lowerCamelCase : str =node.next
def __len__( self :Any ):
return sum(1 for _ in self )
def __repr__( self :Any ):
return "->".join([str(__lowercase ) for item in self] )
def __getitem__( self :Dict , __lowercase :int ):
if not 0 <= index < len(self ):
raise ValueError('''list index out of range.''' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self :Dict , __lowercase :int , __lowercase :Any ):
if not 0 <= index < len(self ):
raise ValueError('''list index out of range.''' )
__lowerCamelCase : Optional[Any] =self.head
for _ in range(__lowercase ):
__lowerCamelCase : List[str] =current.next
__lowerCamelCase : Optional[int] =data
def __lowercase ( self :Tuple , __lowercase :Any ):
self.insert_nth(len(self ) , __lowercase )
def __lowercase ( self :int , __lowercase :Any ):
self.insert_nth(0 , __lowercase )
def __lowercase ( self :Dict , __lowercase :int , __lowercase :Any ):
if not 0 <= index <= len(self ):
raise IndexError('''list index out of range''' )
__lowerCamelCase : List[Any] =Node(__lowercase )
if self.head is None:
__lowerCamelCase : List[Any] =new_node
elif index == 0:
__lowerCamelCase : Optional[Any] =self.head # link new_node to head
__lowerCamelCase : Optional[Any] =new_node
else:
__lowerCamelCase : str =self.head
for _ in range(index - 1 ):
__lowerCamelCase : Any =temp.next
__lowerCamelCase : List[Any] =temp.next
__lowerCamelCase : List[Any] =new_node
def __lowercase ( self :Tuple ): # print every node data
print(self )
def __lowercase ( self :List[str] ):
return self.delete_nth(0 )
def __lowercase ( self :Any ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def __lowercase ( self :Dict , __lowercase :int = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('''List index out of range.''' )
__lowerCamelCase : List[Any] =self.head # default first node
if index == 0:
__lowerCamelCase : int =self.head.next
else:
__lowerCamelCase : int =self.head
for _ in range(index - 1 ):
__lowerCamelCase : str =temp.next
__lowerCamelCase : int =temp.next
__lowerCamelCase : Optional[Any] =temp.next.next
return delete_node.data
def __lowercase ( self :Any ):
return self.head is None
def __lowercase ( self :Tuple ):
__lowerCamelCase : Any =None
__lowerCamelCase : Optional[int] =self.head
while current:
# Store the current node's next node.
__lowerCamelCase : Any =current.next
# Make the current node's next point backwards
__lowerCamelCase : Optional[Any] =prev
# Make the previous node be the current node
__lowerCamelCase : List[str] =current
# Make the current node the next node (to progress iteration)
__lowerCamelCase : Tuple =next_node
# Return prev in order to put the head at the end
__lowerCamelCase : List[str] =prev
def lowerCAmelCase_ ( ):
'''simple docstring'''
__lowerCamelCase : Tuple =LinkedList()
assert linked_list.is_empty() is True
assert str(SCREAMING_SNAKE_CASE ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(SCREAMING_SNAKE_CASE ) == i
linked_list.insert_nth(SCREAMING_SNAKE_CASE , i + 1 )
assert str(SCREAMING_SNAKE_CASE ) == "->".join(str(SCREAMING_SNAKE_CASE ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(SCREAMING_SNAKE_CASE ) == "->".join(str(SCREAMING_SNAKE_CASE ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(SCREAMING_SNAKE_CASE ) == 9
assert str(SCREAMING_SNAKE_CASE ) == "->".join(str(SCREAMING_SNAKE_CASE ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
__lowerCamelCase : List[Any] =-i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(SCREAMING_SNAKE_CASE ) == "->".join(str(SCREAMING_SNAKE_CASE ) for i in range(-8 , 1 ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__lowerCamelCase : Any =[
-9,
100,
Node(77345112 ),
'''dlrow olleH''',
7,
5555,
0,
-192.55_555,
'''Hello, world!''',
77.9,
Node(10 ),
None,
None,
12.20,
]
__lowerCamelCase : str =LinkedList()
for i in test_input:
linked_list.insert_tail(SCREAMING_SNAKE_CASE )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(SCREAMING_SNAKE_CASE ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
__lowerCamelCase : str =linked_list.delete_head()
assert result == -9
assert (
str(SCREAMING_SNAKE_CASE ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
__lowerCamelCase : Union[str, Any] =linked_list.delete_tail()
assert result == 12.2
assert (
str(SCREAMING_SNAKE_CASE ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
__lowerCamelCase : Union[str, Any] =linked_list.delete_nth(10 )
assert result is None
assert (
str(SCREAMING_SNAKE_CASE ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('''Hello again, world!''' ) )
assert (
str(SCREAMING_SNAKE_CASE )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(SCREAMING_SNAKE_CASE )
assert (
str(SCREAMING_SNAKE_CASE )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(SCREAMING_SNAKE_CASE )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowerCAmelCase_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
__lowerCamelCase : Optional[Any] =LinkedList()
linked_list.insert_head(input('''Inserting 1st at head ''' ).strip() )
linked_list.insert_head(input('''Inserting 2nd at head ''' ).strip() )
print('''\nPrint list:''' )
linked_list.print_list()
linked_list.insert_tail(input('''\nInserting 1st at tail ''' ).strip() )
linked_list.insert_tail(input('''Inserting 2nd at tail ''' ).strip() )
print('''\nPrint list:''' )
linked_list.print_list()
print('''\nDelete head''' )
linked_list.delete_head()
print('''Delete tail''' )
linked_list.delete_tail()
print('''\nPrint list:''' )
linked_list.print_list()
print('''\nReverse linked list''' )
linked_list.reverse()
print('''\nPrint list:''' )
linked_list.print_list()
print('''\nString representation of linked list:''' )
print(SCREAMING_SNAKE_CASE )
print('''\nReading/changing Node data using indexing:''' )
print(F'Element at Position 1: {linked_list[1]}' )
__lowerCamelCase : Any =input('''Enter New Value: ''' ).strip()
print('''New list:''' )
print(SCREAMING_SNAKE_CASE )
print(F'length of linked_list is : {len(SCREAMING_SNAKE_CASE )}' )
if __name__ == "__main__":
main()
| 363 | 0 |
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