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 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __lowerCAmelCase ( *a_ , a_ = None , a_=True , a_=2 ) -> List[str]:
'''simple docstring'''
from .. import __version__
SCREAMING_SNAKE_CASE : Dict = take_from
SCREAMING_SNAKE_CASE : int = ()
if not isinstance(args[0] , a_ ):
SCREAMING_SNAKE_CASE : Dict = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(a_ ).base_version ) >= version.parse(a_ ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
SCREAMING_SNAKE_CASE : int = None
if isinstance(a_ , a_ ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(a_ ),)
SCREAMING_SNAKE_CASE : Any = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(a_ , a_ ):
values += (getattr(a_ , a_ ),)
SCREAMING_SNAKE_CASE : Optional[Any] = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
SCREAMING_SNAKE_CASE : Optional[int] = warning + ' ' if standard_warn else ''
warnings.warn(warning + message , a_ , stacklevel=a_ )
if isinstance(a_ , a_ ) and len(a_ ) > 0:
SCREAMING_SNAKE_CASE : List[Any] = inspect.getouterframes(inspect.currentframe() )[1]
SCREAMING_SNAKE_CASE : Any = call_frame.filename
SCREAMING_SNAKE_CASE : Dict = call_frame.lineno
SCREAMING_SNAKE_CASE : int = call_frame.function
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(a_ ) == 0:
return
elif len(a_ ) == 1:
return values[0]
return values
| 251 | '''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCAmelCase ( a_ ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = int(number**0.5 )
return number == sq * sq
def __lowerCAmelCase ( a_ , a_ , a_ , a_ , a_ , a_ ) -> tuple[int, int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
SCREAMING_SNAKE_CASE : int = x_den * y_den * z_den
SCREAMING_SNAKE_CASE : int = gcd(a_ , a_ )
top //= hcf
bottom //= hcf
return top, bottom
def __lowerCAmelCase ( a_ = 35 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : set = set()
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : Fraction = Fraction(0 )
SCREAMING_SNAKE_CASE : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
SCREAMING_SNAKE_CASE : Optional[Any] = x_num * y_den + x_den * y_num
SCREAMING_SNAKE_CASE : Optional[int] = x_den * y_den
SCREAMING_SNAKE_CASE : str = gcd(a_ , a_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE : List[Any] = add_three(
a_ , a_ , a_ , a_ , a_ , a_ )
unique_s.add(a_ )
# n=2
SCREAMING_SNAKE_CASE : List[str] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
SCREAMING_SNAKE_CASE : Optional[int] = x_den * x_den * y_den * y_den
if is_sq(a_ ) and is_sq(a_ ):
SCREAMING_SNAKE_CASE : Dict = int(sqrt(a_ ) )
SCREAMING_SNAKE_CASE : int = int(sqrt(a_ ) )
SCREAMING_SNAKE_CASE : Any = gcd(a_ , a_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE : Dict = add_three(
a_ , a_ , a_ , a_ , a_ , a_ )
unique_s.add(a_ )
# n=-1
SCREAMING_SNAKE_CASE : Any = x_num * y_num
SCREAMING_SNAKE_CASE : List[str] = x_den * y_num + x_num * y_den
SCREAMING_SNAKE_CASE : List[Any] = gcd(a_ , a_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE : Optional[int] = add_three(
a_ , a_ , a_ , a_ , a_ , a_ )
unique_s.add(a_ )
# n=2
SCREAMING_SNAKE_CASE : Any = x_num * x_num * y_num * y_num
SCREAMING_SNAKE_CASE : Union[str, Any] = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(a_ ) and is_sq(a_ ):
SCREAMING_SNAKE_CASE : str = int(sqrt(a_ ) )
SCREAMING_SNAKE_CASE : int = int(sqrt(a_ ) )
SCREAMING_SNAKE_CASE : Dict = gcd(a_ , a_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE : Optional[int] = add_three(
a_ , a_ , a_ , a_ , a_ , a_ )
unique_s.add(a_ )
for num, den in unique_s:
total += Fraction(a_ , a_ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"""{solution() = }""")
| 251 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : List[str] , _A : int=1 , _A : Tuple=0 , _A : Optional[Any]=2 , _A : str=512 , _A : int="cls" , _A : Tuple=False , _A : Dict=True , **_A : Tuple , ):
"""simple docstring"""
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
__SCREAMING_SNAKE_CASE : Tuple = project_dim
__SCREAMING_SNAKE_CASE : Tuple = pooler_fn
__SCREAMING_SNAKE_CASE : Optional[int] = learn_encoder
__SCREAMING_SNAKE_CASE : Any = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = [r'''pooler''', r'''logit_scale''']
lowerCAmelCase_ = [r'''position_ids''', r'''predictions.decoder.bias''']
lowerCAmelCase_ = '''roberta'''
lowerCAmelCase_ = RobertaSeriesConfig
def __init__( self : str , _A : Optional[Any] ):
"""simple docstring"""
super().__init__(_A )
__SCREAMING_SNAKE_CASE : str = XLMRobertaModel(_A )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Linear(config.hidden_size , config.project_dim )
__SCREAMING_SNAKE_CASE : List[Any] = getattr(_A , '''has_pre_transformation''' , _A )
if self.has_pre_transformation:
__SCREAMING_SNAKE_CASE : str = nn.Linear(config.hidden_size , config.project_dim )
__SCREAMING_SNAKE_CASE : Optional[int] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def UpperCAmelCase__ ( self : Union[str, Any] , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , _A : Optional[bool] = None , _A : Optional[bool] = None , _A : Optional[bool] = None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE : Tuple = self.base_model(
input_ids=_A , attention_mask=_A , token_type_ids=_A , position_ids=_A , head_mask=_A , inputs_embeds=_A , encoder_hidden_states=_A , encoder_attention_mask=_A , output_attentions=_A , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_A , )
if self.has_pre_transformation:
__SCREAMING_SNAKE_CASE : Optional[int] = outputs['''hidden_states'''][-2]
__SCREAMING_SNAKE_CASE : str = self.pre_LN(_A )
__SCREAMING_SNAKE_CASE : Dict = self.transformation_pre(_A )
return TransformationModelOutput(
projection_state=_A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=_A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 131 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=_A ).to(_A )
__SCREAMING_SNAKE_CASE : Dict = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE : List[str] = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
__SCREAMING_SNAKE_CASE : int = model(input_ids.to(_A ) , labels=labels.to(_A ) ).loss
__SCREAMING_SNAKE_CASE : Optional[int] = -(labels.shape[-1] * loss.item())
__SCREAMING_SNAKE_CASE : int = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 131 | 1 |
'''simple docstring'''
import random
def a__ ( lowercase : List[str], lowercase : str, lowercase : Dict ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = a[left_index]
_UpperCamelCase = left_index + 1
for j in range(left_index + 1, lowercase ):
if a[j] < pivot:
_UpperCamelCase , _UpperCamelCase = a[i], a[j]
i += 1
_UpperCamelCase , _UpperCamelCase = a[i - 1], a[left_index]
return i - 1
def a__ ( lowercase : int, lowercase : Optional[int], lowercase : str ) -> Any:
"""simple docstring"""
if left < right:
_UpperCamelCase = random.randint(lowercase, right - 1 )
_UpperCamelCase , _UpperCamelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCamelCase = partition(lowercase, lowercase, lowercase )
quick_sort_random(
lowercase, lowercase, lowercase ) # recursive quicksort to the left of the pivot point
quick_sort_random(
lowercase, pivot_index + 1, lowercase ) # recursive quicksort to the right of the pivot point
def a__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCamelCase = [int(lowercase ) for item in user_input.split(''',''' )]
quick_sort_random(lowercase, 0, len(lowercase ) )
print(lowercase )
if __name__ == "__main__":
main()
| 98 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Tuple = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__lowercase : int = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
__lowercase : Dict = {"""facebook/blenderbot-3B""": 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase_ ( ):
lowerCamelCase_ = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowerCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(_lowerCamelCase ) for n in cs]
return dict(zip(_lowerCamelCase , _lowerCamelCase ) )
def lowerCamelCase_ ( _lowerCamelCase : Any ):
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :Any = VOCAB_FILES_NAMES
__lowercase :List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase :Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase :Dict = ["input_ids", "attention_mask"]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="replace" , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<s>" , UpperCamelCase__="<unk>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> Any:
'''simple docstring'''
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else bos_token
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else eos_token
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else sep_token
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else cls_token
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else unk_token
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
super().__init__(
errors=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , **UpperCamelCase__ , )
with open(UpperCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ = json.load(UpperCamelCase__ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCamelCase__ , encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return len(self.encoder )
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(UpperCamelCase__ )
lowerCamelCase_ = get_pairs(UpperCamelCase__ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(UpperCamelCase__ , key=lambda UpperCamelCase__ : self.bpe_ranks.get(UpperCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ , lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(UpperCamelCase__ ):
try:
lowerCamelCase_ = word.index(UpperCamelCase__ , UpperCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(UpperCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(UpperCamelCase__ )
lowerCamelCase_ = new_word
if len(UpperCamelCase__ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(UpperCamelCase__ )
lowerCamelCase_ = ''' '''.join(UpperCamelCase__ )
lowerCamelCase_ = word
return word
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , UpperCamelCase__ ):
lowerCamelCase_ = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
return self.encoder.get(UpperCamelCase__ , self.encoder.get(self.unk_token ) )
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.decoder.get(UpperCamelCase__ )
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = ''''''.join(UpperCamelCase__ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(UpperCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase__ , ensure_ascii=UpperCamelCase__ ) + '''\n''' )
lowerCamelCase_ = 0
with open(UpperCamelCase__ , '''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 UpperCamelCase__ : 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(UpperCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1]
return [1] + ([0] * len(UpperCamelCase__ )) + [1, 1] + ([0] * len(UpperCamelCase__ )) + [1]
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
'''simple docstring'''
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]
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ''' ''' + text
return (text, kwargs)
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[str]:
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(UpperCamelCase__ )
lowerCamelCase_ = ''' '''.join(UpperCamelCase__ )
lowerCamelCase_ = self.encode(UpperCamelCase__ )
if len(UpperCamelCase__ ) > self.model_max_length:
lowerCamelCase_ = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids | 142 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __snake_case :
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=10 , __UpperCamelCase=3 , __UpperCamelCase=2 , __UpperCamelCase=2 , __UpperCamelCase=2 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=32 , __UpperCamelCase=5 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=10 , __UpperCamelCase=0.0_2 , __UpperCamelCase=0.9 , __UpperCamelCase=None , ) -> int:
'''simple docstring'''
snake_case__ : List[Any] = parent
snake_case__ : Union[str, Any] = batch_size
snake_case__ : Any = image_size
snake_case__ : Union[str, Any] = num_channels
snake_case__ : Union[str, Any] = patch_size
snake_case__ : List[Any] = tubelet_size
snake_case__ : List[str] = num_frames
snake_case__ : Dict = is_training
snake_case__ : Optional[Any] = use_labels
snake_case__ : Dict = hidden_size
snake_case__ : Optional[Any] = num_hidden_layers
snake_case__ : int = num_attention_heads
snake_case__ : Tuple = intermediate_size
snake_case__ : Dict = hidden_act
snake_case__ : Dict = hidden_dropout_prob
snake_case__ : Dict = attention_probs_dropout_prob
snake_case__ : Optional[Any] = type_sequence_label_size
snake_case__ : Optional[int] = initializer_range
snake_case__ : List[str] = mask_ratio
snake_case__ : Tuple = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
snake_case__ : str = (image_size // patch_size) ** 2
snake_case__ : Dict = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
snake_case__ : List[Any] = int(mask_ratio * self.seq_length )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Union[str, Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
snake_case__ : Union[str, Any] = None
if self.use_labels:
snake_case__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case__ : List[Any] = self.get_config()
return config, pixel_values, labels
def __a ( self ) -> Optional[int]:
'''simple docstring'''
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
'''simple docstring'''
snake_case__ : Any = VideoMAEModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case__ : Dict = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
snake_case__ : Union[str, Any] = VideoMAEForPreTraining(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
snake_case__ : Any = torch.ones((self.num_masks,) )
snake_case__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
snake_case__ : Union[str, Any] = mask.expand(self.batch_size , -1 ).bool()
snake_case__ : str = model(__UpperCamelCase , __UpperCamelCase )
# model only returns predictions for masked patches
snake_case__ : str = mask.sum().item()
snake_case__ : Dict = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def __a ( self ) -> Any:
'''simple docstring'''
snake_case__ : List[Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ : Optional[int] = config_and_inputs
snake_case__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( _lowerCamelCase ,_lowerCamelCase ,unittest.TestCase ):
__lowerCamelCase = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
__lowerCamelCase = (
{"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : Optional[Any] = VideoMAEModelTester(self )
snake_case__ : Optional[Any] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : List[Any] = copy.deepcopy(__UpperCamelCase )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
snake_case__ : int = torch.ones((self.model_tester.num_masks,) )
snake_case__ : Optional[int] = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
snake_case__ : List[Any] = mask.expand(self.model_tester.batch_size , -1 ).bool()
snake_case__ : List[str] = bool_masked_pos.to(__UpperCamelCase )
if return_labels:
if model_class in [
*get_values(__UpperCamelCase ),
]:
snake_case__ : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCamelCase )
return inputs_dict
def __a ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def __a ( self ) -> str:
'''simple docstring'''
pass
def __a ( self ) -> int:
'''simple docstring'''
snake_case__ , snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Dict = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ , snake_case__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : str = model_class(__UpperCamelCase )
snake_case__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : str = [*signature.parameters.keys()]
snake_case__ : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
@slow
def __a ( self ) -> List[str]:
'''simple docstring'''
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Optional[int] = VideoMAEModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __a ( self ) -> int:
'''simple docstring'''
if not self.has_attentions:
pass
else:
snake_case__ , snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[str] = True
for model_class in self.all_model_classes:
snake_case__ : Optional[int] = self.model_tester.seq_length - self.model_tester.num_masks
snake_case__ : int = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
snake_case__ : List[Any] = True
snake_case__ : Dict = False
snake_case__ : Dict = True
snake_case__ : Tuple = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case__ : List[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"]
snake_case__ : Union[str, Any] = True
snake_case__ : Union[str, Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case__ : Optional[Any] = 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] , )
snake_case__ : Dict = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case__ : int = True
snake_case__ : Optional[Any] = True
snake_case__ : List[str] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCamelCase ) )
snake_case__ : Union[str, 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 __a ( self ) -> Optional[int]:
'''simple docstring'''
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case__ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case__ : Optional[Any] = outputs.hidden_states
snake_case__ : Optional[int] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case__ : Tuple = self.model_tester.seq_length - self.model_tester.num_masks
snake_case__ : Optional[int] = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
snake_case__ , snake_case__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : List[str] = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : str = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __a ( self ) -> List[str]:
'''simple docstring'''
pass
def UpperCamelCase__ ( ) -> int:
snake_case__ : List[str] = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
snake_case__ : Optional[Any] = np.load(A__ )
return list(A__ )
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __a ( self ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Optional[Any] = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
__UpperCamelCase )
snake_case__ : str = self.default_image_processor
snake_case__ : Optional[int] = prepare_video()
snake_case__ : Optional[Any] = image_processor(__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case__ : Optional[int] = model(**__UpperCamelCase )
# verify the logits
snake_case__ : List[Any] = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case__ : List[Any] = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __a ( self ) -> List[str]:
'''simple docstring'''
snake_case__ : Dict = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(__UpperCamelCase )
snake_case__ : str = self.default_image_processor
snake_case__ : Optional[int] = prepare_video()
snake_case__ : Tuple = image_processor(__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# add boolean mask, indicating which patches to mask
snake_case__ : Optional[Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
snake_case__ : Dict = torch.load(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case__ : Tuple = model(**__UpperCamelCase )
# verify the logits
snake_case__ : List[str] = torch.Size([1, 1408, 1536] )
snake_case__ : List[Any] = torch.tensor(
[[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] , device=__UpperCamelCase )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
snake_case__ : int = torch.tensor([0.5_1_4_2] , device=__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.loss , __UpperCamelCase , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
snake_case__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=__UpperCamelCase ).to(
__UpperCamelCase )
with torch.no_grad():
snake_case__ : Dict = model(**__UpperCamelCase )
snake_case__ : List[Any] = torch.tensor(torch.tensor([0.6_4_6_9] ) , device=__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.loss , __UpperCamelCase , atol=1E-4 ) )
| 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ) -> None:
'''simple docstring'''
warnings.warn(
'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PerceiverImageProcessor instead.' , __UpperCamelCase , )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
| 699 | 1 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
UpperCAmelCase = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
"""name""",
"""cache_dir""",
"""data_dir""",
"""all_configs""",
"""save_infos""",
"""ignore_verifications""",
"""force_redownload""",
"""clear_cache""",
],
defaults=[None, None, None, False, False, False, False, False],
)
def _snake_case ( __snake_case : int , __snake_case : str ):
"""simple docstring"""
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def _snake_case ( __snake_case : Tuple ):
"""simple docstring"""
_lowerCamelCase : Tuple = _TestCommandArgs(dataset=__snake_case , all_configs=__snake_case , save_infos=__snake_case )
_lowerCamelCase : Union[str, Any] = TestCommand(*__snake_case )
test_command.run()
_lowerCamelCase : List[str] = os.path.join(__snake_case , """README.md""" )
assert os.path.exists(__snake_case )
_lowerCamelCase : Dict = DatasetInfosDict.from_directory(__snake_case )
_lowerCamelCase : int = DatasetInfosDict(
{
"""default""": DatasetInfo(
features=Features(
{
"""tokens""": Sequence(Value("""string""" ) ),
"""ner_tags""": Sequence(
ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""] ) ),
"""langs""": Sequence(Value("""string""" ) ),
"""spans""": Sequence(Value("""string""" ) ),
} ) , splits=[
{
"""name""": """train""",
"""num_bytes""": 2351563,
"""num_examples""": 10000,
},
{
"""name""": """validation""",
"""num_bytes""": 238418,
"""num_examples""": 1000,
},
] , download_size=3940680 , dataset_size=2589981 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
_lowerCamelCase , _lowerCamelCase : Any = getattr(dataset_infos["""default"""] , __snake_case ), getattr(expected_dataset_infos["""default"""] , __snake_case )
if key == "num_bytes":
assert is_apercent_close(__snake_case , __snake_case )
elif key == "splits":
assert list(__snake_case ) == list(__snake_case )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 88 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase(a : int = 100_0000 , a : int = 10 ) -> int:
_SCREAMING_SNAKE_CASE =defaultdict(a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_SCREAMING_SNAKE_CASE =max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_SCREAMING_SNAKE_CASE =1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(a , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f"{solution() = }")
| 255 | 0 |
"""simple docstring"""
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
UpperCamelCase = """tiny-wmt19-en-ru"""
# Build
# borrowed from a test
UpperCamelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
UpperCamelCase = dict(zip(vocab, range(len(vocab))))
UpperCamelCase = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = Path(tmpdirname)
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""merges_file"""]
with open(src_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, """w""") as fp:
fp.write("""\n""".join(merges))
UpperCamelCase = FSMTTokenizer(
langs=["""en""", """ru"""],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
UpperCamelCase = FSMTConfig(
langs=["""ru""", """en"""],
src_vocab_size=10_00,
tgt_vocab_size=10_00,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
UpperCamelCase = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
UpperCamelCase = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
UpperCamelCase = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 701 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """scipy"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["torch", "scipy"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "scipy"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "scipy"])
| 120 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
A : str = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
A : int = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight", "encoder.layernorm.weight"),
("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
("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"),
]
)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = state_dict.pop(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = val
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
SCREAMING_SNAKE_CASE_ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
SCREAMING_SNAKE_CASE_ = value
else:
SCREAMING_SNAKE_CASE_ = value
return new_state_dict
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = """"""
# 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)
SCREAMING_SNAKE_CASE_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
SCREAMING_SNAKE_CASE_ = 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
SCREAMING_SNAKE_CASE_ = in_proj_weight[:2_5_6, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[:2_5_6]
SCREAMING_SNAKE_CASE_ = in_proj_weight[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE_ = in_proj_weight[-2_5_6:, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[-2_5_6:]
# 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
SCREAMING_SNAKE_CASE_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
SCREAMING_SNAKE_CASE_ = 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
SCREAMING_SNAKE_CASE_ = in_proj_weight[:2_5_6, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[:2_5_6]
SCREAMING_SNAKE_CASE_ = in_proj_weight[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE_ = in_proj_weight[-2_5_6:, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
SCREAMING_SNAKE_CASE_ = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
SCREAMING_SNAKE_CASE_ = 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
SCREAMING_SNAKE_CASE_ = in_proj_weight_cross_attn[:2_5_6, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias_cross_attn[:2_5_6]
SCREAMING_SNAKE_CASE_ = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias_cross_attn[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE_ = in_proj_weight_cross_attn[-2_5_6:, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias_cross_attn[-2_5_6:]
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ = max(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
SCREAMING_SNAKE_CASE_ = target_max_size / current_max_size
SCREAMING_SNAKE_CASE_ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = F.to_tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = F.normalize(__UpperCamelCase , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
logger.info("Converting model..." )
# load original state dict
SCREAMING_SNAKE_CASE_ = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = rename_backbone_keys(__UpperCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(__UpperCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
SCREAMING_SNAKE_CASE_ = """model."""
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
SCREAMING_SNAKE_CASE_ = state_dict.pop(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = val
# create HuggingFace model and load state dict
SCREAMING_SNAKE_CASE_ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
SCREAMING_SNAKE_CASE_ = 1_5
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = {0: """table""", 1: """table rotated"""}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
else:
SCREAMING_SNAKE_CASE_ = 1_2_5
SCREAMING_SNAKE_CASE_ = 6
SCREAMING_SNAKE_CASE_ = {
0: """table""",
1: """table column""",
2: """table row""",
3: """table column header""",
4: """table projected row header""",
5: """table spanning cell""",
}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = DetrImageProcessor(
format="coco_detection" , max_size=8_0_0 if "detection" in checkpoint_url else 1_0_0_0 )
SCREAMING_SNAKE_CASE_ = TableTransformerForObjectDetection(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
model.eval()
# verify our conversion
SCREAMING_SNAKE_CASE_ = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
SCREAMING_SNAKE_CASE_ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = Image.open(__UpperCamelCase ).convert("RGB" )
SCREAMING_SNAKE_CASE_ = normalize(resize(__UpperCamelCase , __UpperCamelCase ) ).unsqueeze(0 )
SCREAMING_SNAKE_CASE_ = model(__UpperCamelCase )
if "detection" in checkpoint_url:
SCREAMING_SNAKE_CASE_ = (1, 1_5, 3)
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
SCREAMING_SNAKE_CASE_ = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
SCREAMING_SNAKE_CASE_ = (1, 1_2_5, 7)
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
SCREAMING_SNAKE_CASE_ = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __UpperCamelCase , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __UpperCamelCase , 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(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
SCREAMING_SNAKE_CASE_ = (
"""microsoft/table-transformer-detection"""
if """detection""" in checkpoint_url
else """microsoft/table-transformer-structure-recognition"""
)
model.push_to_hub(__UpperCamelCase )
image_processor.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
type=str,
choices=[
"https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
"https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
],
help="URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub."
)
A : Any = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 140 |
"""simple docstring"""
lowercase_ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowercase_ = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase_ = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 470 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class snake_case_ ( _a ):
"""simple docstring"""
def A__ ( self ):
__lowerCAmelCase = tempfile.mkdtemp()
__lowerCAmelCase = 5
# Realm tok
__lowerCAmelCase = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'test',
'question',
'this',
'is',
'the',
'first',
'second',
'third',
'fourth',
'fifth',
'record',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__lowerCAmelCase = os.path.join(self.tmpdirname , 'realm_tokenizer' )
os.makedirs(_A , exist_ok=_A )
__lowerCAmelCase = os.path.join(_A , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
__lowerCAmelCase = os.path.join(self.tmpdirname , 'realm_block_records' )
os.makedirs(_A , exist_ok=_A )
def A__ ( self ):
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'realm_tokenizer' ) )
def A__ ( self ):
shutil.rmtree(self.tmpdirname )
def A__ ( self ):
__lowerCAmelCase = RealmConfig(num_block_records=self.num_block_records )
return config
def A__ ( self ):
__lowerCAmelCase = Dataset.from_dict(
{
'id': ['0', '1'],
'question': ['foo', 'bar'],
'answers': [['Foo', 'Bar'], ['Bar']],
} )
return dataset
def A__ ( self ):
__lowerCAmelCase = np.array(
[
b'This is the first record',
b'This is the second record',
b'This is the third record',
b'This is the fourth record',
b'This is the fifth record',
b'This is a longer longer longer record',
] , dtype=_A , )
return block_records
def A__ ( self ):
__lowerCAmelCase = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def A__ ( self ):
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = self.get_dummy_retriever()
__lowerCAmelCase = retriever.tokenizer
__lowerCAmelCase = np.array([0, 3] , dtype='long' )
__lowerCAmelCase = tokenizer(['Test question'] ).input_ids
__lowerCAmelCase = tokenizer(
['the fourth'] , add_special_tokens=_A , return_token_type_ids=_A , return_attention_mask=_A , ).input_ids
__lowerCAmelCase = config.reader_seq_len
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = retriever(
_A , _A , answer_ids=_A , max_length=_A , return_tensors='np' )
self.assertEqual(len(_A ) , 2 )
self.assertEqual(len(_A ) , 2 )
self.assertEqual(len(_A ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 1_0) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 1_0) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 1_0) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 1_0) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]'] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]'] , )
def A__ ( self ):
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = self.get_dummy_retriever()
__lowerCAmelCase = retriever.tokenizer
__lowerCAmelCase = np.array([0, 3, 5] , dtype='long' )
__lowerCAmelCase = tokenizer(['Test question'] ).input_ids
__lowerCAmelCase = tokenizer(
['the fourth', 'longer longer'] , add_special_tokens=_A , return_token_type_ids=_A , return_attention_mask=_A , ).input_ids
__lowerCAmelCase = config.reader_seq_len
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = retriever(
_A , _A , answer_ids=_A , max_length=_A , return_tensors='np' )
self.assertEqual([False, True, True] , _A )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , _A )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , _A )
def A__ ( self ):
__lowerCAmelCase = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , 'realm_block_records' ) )
# Test local path
__lowerCAmelCase = retriever.from_pretrained(os.path.join(self.tmpdirname , 'realm_block_records' ) )
self.assertEqual(retriever.block_records[0] , b'This is the first record' )
# Test mocked remote path
with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
__lowerCAmelCase = os.path.join(
os.path.join(self.tmpdirname , 'realm_block_records' ) , _REALM_BLOCK_RECORDS_FILENAME )
__lowerCAmelCase = RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )
self.assertEqual(retriever.block_records[0] , b'This is the first record' )
| 102 |
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
assert x is not None
assert y is not None
__lowerCAmelCase = len(UpperCAmelCase__ )
__lowerCAmelCase = len(UpperCAmelCase__ )
# declaring the array for storing the dp values
__lowerCAmelCase = [[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1 , m + 1 ):
for j in range(1 , n + 1 ):
__lowerCAmelCase = 1 if x[i - 1] == y[j - 1] else 0
__lowerCAmelCase = max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
__lowerCAmelCase = ''
__lowerCAmelCase, __lowerCAmelCase = m, n
while i > 0 and j > 0:
__lowerCAmelCase = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
__lowerCAmelCase = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
lowerCamelCase = '''AGGTAB'''
lowerCamelCase = '''GXTXAYB'''
lowerCamelCase = 4
lowerCamelCase = '''GTAB'''
lowerCamelCase , lowerCamelCase = longest_common_subsequence(a, b)
print('''len =''', ln, ''', sub-sequence =''', subseq)
import doctest
doctest.testmod()
| 102 | 1 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod() | 108 |
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 SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
a_ : List[str] = '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
a_ : Dict = dict(zip(SCREAMING_SNAKE_CASE__ , range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
a_ : int = {
'unk_token': '<unk>',
'bos_token': '<s>',
'eos_token': '</s>',
}
a_ : Optional[int] = {
'feature_size': 1,
'padding_value': 0.0,
'sampling_rate': 1_6_0_0_0,
'return_attention_mask': False,
'do_normalize': True,
}
a_ : List[str] = tempfile.mkdtemp()
a_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
a_ : List[str] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + '\n' )
with open(self.feature_extraction_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + '\n' )
# load decoder from hub
a_ : int = 'hf-internal-testing/ngram-beam-search-decoder'
def SCREAMING_SNAKE_CASE ( self : List[Any] , **SCREAMING_SNAKE_CASE__ : Optional[int] ) -> List[Any]:
a_ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(SCREAMING_SNAKE_CASE__ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , **SCREAMING_SNAKE_CASE__ : Any ) -> Any:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple , **SCREAMING_SNAKE_CASE__ : List[Any] ) -> str:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
a_ : Optional[int] = self.get_tokenizer()
a_ : Optional[Any] = self.get_feature_extractor()
a_ : Any = self.get_decoder()
a_ : List[str] = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(self.tmpdirname )
a_ : Optional[int] = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , SCREAMING_SNAKE_CASE__ )
# 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 , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
a_ : Optional[Any] = 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
a_ : Optional[Any] = 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 SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
a_ : List[Any] = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['xx'] )
with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__ , 'include' ):
WavaVecaProcessorWithLM(
tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
a_ : Any = self.get_feature_extractor()
a_ : Tuple = self.get_tokenizer()
a_ : Any = self.get_decoder()
a_ : str = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = floats_list((3, 1_0_0_0) )
a_ : List[str] = feature_extractor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
a_ : Optional[int] = processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
a_ : List[Any] = self.get_feature_extractor()
a_ : Dict = self.get_tokenizer()
a_ : Optional[Any] = self.get_decoder()
a_ : Tuple = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : Dict = 'This is a test string'
a_ : Tuple = processor(text=SCREAMING_SNAKE_CASE__ )
a_ : str = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : List[Any]=(2, 1_0, 1_6) , SCREAMING_SNAKE_CASE__ : int=7_7 ) -> Optional[int]:
np.random.seed(SCREAMING_SNAKE_CASE__ )
return np.random.rand(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
a_ : Union[str, Any] = self.get_feature_extractor()
a_ : Tuple = self.get_tokenizer()
a_ : Optional[Any] = self.get_decoder()
a_ : List[Any] = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : Tuple = self._get_dummy_logits(shape=(1_0, 1_6) , seed=1_3 )
a_ : Union[str, Any] = processor.decode(SCREAMING_SNAKE_CASE__ )
a_ : int = decoder.decode_beams(SCREAMING_SNAKE_CASE__ )[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 SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Dict:
a_ : Any = self.get_feature_extractor()
a_ : Union[str, Any] = self.get_tokenizer()
a_ : str = self.get_decoder()
a_ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : str = 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:
a_ : Optional[int] = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
else:
with get_context(SCREAMING_SNAKE_CASE__ ).Pool() as pool:
a_ : Dict = processor.batch_decode(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : List[str] = list(SCREAMING_SNAKE_CASE__ )
with get_context('fork' ).Pool() as p:
a_ : List[Any] = decoder.decode_beams_batch(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ , a_ , a_ : List[str] = [], [], []
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(SCREAMING_SNAKE_CASE__ , decoded_processor.text )
self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] , decoded_processor.text )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , decoded_processor.logit_score )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , decoded_processor.lm_score )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
a_ : List[Any] = self.get_feature_extractor()
a_ : Optional[int] = self.get_tokenizer()
a_ : Optional[Any] = self.get_decoder()
a_ : List[Any] = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : str = self._get_dummy_logits()
a_ : List[str] = 1_5
a_ : Tuple = -20.0
a_ : Dict = -4.0
a_ : Optional[int] = processor.batch_decode(
SCREAMING_SNAKE_CASE__ , beam_width=SCREAMING_SNAKE_CASE__ , beam_prune_logp=SCREAMING_SNAKE_CASE__ , token_min_logp=SCREAMING_SNAKE_CASE__ , )
a_ : Union[str, Any] = decoded_processor_out.text
a_ : Any = list(SCREAMING_SNAKE_CASE__ )
with get_context('fork' ).Pool() as pool:
a_ : List[str] = decoder.decode_beams_batch(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , beam_width=SCREAMING_SNAKE_CASE__ , beam_prune_logp=SCREAMING_SNAKE_CASE__ , token_min_logp=SCREAMING_SNAKE_CASE__ , )
a_ : Any = [d[0][0] for d in decoded_decoder_out]
a_ : List[Any] = [d[0][2] for d in decoded_decoder_out]
a_ : int = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] , SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE__ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE__ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
a_ : Any = self.get_feature_extractor()
a_ : List[Any] = self.get_tokenizer()
a_ : str = self.get_decoder()
a_ : int = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
a_ : List[str] = self._get_dummy_logits()
a_ : int = 2.0
a_ : Tuple = 5.0
a_ : List[str] = -20.0
a_ : Optional[Any] = True
a_ : int = processor.batch_decode(
SCREAMING_SNAKE_CASE__ , alpha=SCREAMING_SNAKE_CASE__ , beta=SCREAMING_SNAKE_CASE__ , unk_score_offset=SCREAMING_SNAKE_CASE__ , lm_score_boundary=SCREAMING_SNAKE_CASE__ , )
a_ : str = decoded_processor_out.text
a_ : Dict = list(SCREAMING_SNAKE_CASE__ )
decoder.reset_params(
alpha=SCREAMING_SNAKE_CASE__ , beta=SCREAMING_SNAKE_CASE__ , unk_score_offset=SCREAMING_SNAKE_CASE__ , lm_score_boundary=SCREAMING_SNAKE_CASE__ , )
with get_context('fork' ).Pool() as pool:
a_ : Optional[int] = decoder.decode_beams_batch(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
a_ : int = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] , SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = 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 , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
a_ : Any = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
a_ : int = processor.decoder.model_container[processor.decoder._model_key]
a_ : Optional[Any] = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
a_ : Dict = os.listdir(SCREAMING_SNAKE_CASE__ )
a_ : Dict = ['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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
a_ : List[Any] = snapshot_download('hf-internal-testing/processor_with_lm' )
a_ : Dict = WavaVecaProcessorWithLM.from_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = processor.decoder.model_container[processor.decoder._model_key]
a_ : List[Any] = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
a_ : List[str] = os.listdir(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = os.listdir(SCREAMING_SNAKE_CASE__ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
a_ : str = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
a_ : Any = AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )
a_ : int = floats_list((3, 1_0_0_0) )
a_ : Tuple = processor_wavaveca(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
a_ : List[str] = processor_auto(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
a_ : Dict = self._get_dummy_logits()
a_ : Dict = processor_wavaveca.batch_decode(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = processor_auto.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
a_ : int = self.get_feature_extractor()
a_ : Dict = self.get_tokenizer()
a_ : Tuple = self.get_decoder()
a_ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
@staticmethod
def SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[int]:
a_ : Tuple = [d[key] for d in offsets]
return retrieved_list
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
a_ : List[str] = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
a_ : str = self._get_dummy_logits()[0]
a_ : str = processor.decode(SCREAMING_SNAKE_CASE__ , output_word_offsets=SCREAMING_SNAKE_CASE__ )
# 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
a_ : str = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
a_ : Dict = self._get_dummy_logits()
a_ : List[Any] = processor.batch_decode(SCREAMING_SNAKE_CASE__ , output_word_offsets=SCREAMING_SNAKE_CASE__ )
# 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(
[' '.join(self.get_from_offsets(SCREAMING_SNAKE_CASE__ , '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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
import torch
a_ : Optional[Any] = load_dataset('common_voice' , 'en' , split='train' , streaming=SCREAMING_SNAKE_CASE__ )
a_ : Dict = ds.cast_column('audio' , datasets.Audio(sampling_rate=1_6_0_0_0 ) )
a_ : str = iter(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = next(SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
a_ : Optional[Any] = 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
a_ : Optional[Any] = processor(sample['audio']['array'] , return_tensors='pt' ).input_values
with torch.no_grad():
a_ : Any = model(SCREAMING_SNAKE_CASE__ ).logits.cpu().numpy()
a_ : List[Any] = processor.decode(logits[0] , output_word_offsets=SCREAMING_SNAKE_CASE__ )
a_ : Tuple = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
a_ : int = [
{
'start_time': d['start_offset'] * time_offset,
'end_time': d['end_offset'] * time_offset,
'word': d['word'],
}
for d in output['word_offsets']
]
a_ : Union[str, Any] = '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(SCREAMING_SNAKE_CASE__ , 'word' ) ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(' '.join(self.get_from_offsets(SCREAMING_SNAKE_CASE__ , 'word' ) ) , output.text )
# output times
a_ : Optional[Any] = torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE__ , 'start_time' ) )
a_ : Any = torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE__ , 'end_time' ) )
# fmt: off
a_ : Optional[int] = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
a_ : Optional[int] = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=0.01 ) )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=0.01 ) )
| 570 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class A__ ( _snake_case ):
lowercase = "camembert"
def __init__( self , UpperCamelCase__=30522 , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-1_2 , UpperCamelCase__=1 , UpperCamelCase__=0 , UpperCamelCase__=2 , UpperCamelCase__="absolute" , UpperCamelCase__=True , UpperCamelCase__=None , **UpperCamelCase__ , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = hidden_act
A_ = intermediate_size
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = initializer_range
A_ = layer_norm_eps
A_ = position_embedding_type
A_ = use_cache
A_ = classifier_dropout
class A__ ( _snake_case ):
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 667 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAmelCase__ ) - 1:
A_ = SYMBOLS[numerals[index]]
A_ = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
A_ = """"""
A_ = num // 10_00
numerals += m_count * "M"
num %= 10_00
A_ = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
A_ = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCAmelCase__ ( UpperCAmelCase__ = "/p089_roman.txt" ) -> int:
A_ = 0
with open(os.path.dirname(UpperCAmelCase__ ) + roman_numerals_filename ) as filea:
A_ = filea.readlines()
for line in lines:
A_ = line.strip()
A_ = parse_roman_numerals(UpperCAmelCase__ )
A_ = generate_roman_numerals(UpperCAmelCase__ )
savings += len(UpperCAmelCase__ ) - len(UpperCAmelCase__ )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 667 | 1 |
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 = logging.get_logger(__name__)
__snake_case = """▁"""
__snake_case = {"""vocab_file""": """sentencepiece.bpe.model"""}
__snake_case = {
"""vocab_file""": {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
),
}
}
__snake_case = {
"""xlm-roberta-base""": 5_12,
"""xlm-roberta-large""": 5_12,
"""xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
"""xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
"""xlm-roberta-large-finetuned-conll03-english""": 5_12,
"""xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class UpperCAmelCase_ ( lowercase ):
"""simple docstring"""
UpperCamelCase_ : Tuple =VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int =['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase :int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
UpperCamelCase :List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase :Union[str, Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase :Union[str, Any] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase :int = 1
UpperCamelCase :List[str] = len(self.sp_model ) + self.fairseq_offset
UpperCamelCase :List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Any:
UpperCamelCase :Any = self.__dict__.copy()
UpperCamelCase :Any = None
UpperCamelCase :Tuple = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE_ ) -> int:
UpperCamelCase :List[Any] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCamelCase :str = {}
UpperCamelCase :str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase :Union[str, Any] = [self.cls_token_id]
UpperCamelCase :int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
UpperCamelCase :Tuple = [self.sep_token_id]
UpperCamelCase :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 UpperCAmelCase ( self ) -> Optional[Any]:
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :Tuple = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> List[str]:
return self.sp_model.encode(SCREAMING_SNAKE_CASE_ , out_type=SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> List[str]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase :List[Any] = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> int:
UpperCamelCase :List[Any] = ''''''.join(SCREAMING_SNAKE_CASE_ ).replace(SCREAMING_SNAKE_CASE_ , ''' ''' ).strip()
return out_string
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase :int = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_ , '''wb''' ) as fi:
UpperCamelCase :Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 658 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@parameterized.expand([(None,), ('''foo.json''',)] )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCamelCase :int = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> str:
UpperCamelCase :Optional[Any] = AutoConfig.from_pretrained('''gpt2''' )
UpperCamelCase :Union[str, Any] = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Optional[Any] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :List[str] = GenerationConfig()
UpperCamelCase :List[str] = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
UpperCamelCase :Dict = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Any = generation_config.update(**SCREAMING_SNAKE_CASE_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(SCREAMING_SNAKE_CASE_ , {'''foo''': '''bar'''} )
def UpperCAmelCase ( self ) -> Optional[Any]:
UpperCamelCase :List[Any] = GenerationConfig()
UpperCamelCase :Tuple = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Any = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
UpperCamelCase :Union[str, Any] = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
assert not hasattr(SCREAMING_SNAKE_CASE_ , '''foo''' ) # no new kwargs should be initialized if from config
def UpperCAmelCase ( self ) -> Any:
UpperCamelCase :Dict = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(default_config.num_beams , 1 )
UpperCamelCase :Tuple = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Tuple = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def UpperCAmelCase ( cls ) -> Optional[Any]:
UpperCamelCase :List[str] = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE_ )
@classmethod
def UpperCAmelCase ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def UpperCAmelCase ( self ) -> Any:
UpperCamelCase :Optional[Any] = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
UpperCamelCase :List[Any] = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='''test-generation-config''' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase :Any = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :List[str] = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
UpperCamelCase :Any = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase :Tuple = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
| 658 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCamelCase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class lowerCamelCase__ :
"""simple docstring"""
_UpperCamelCase : Any = PegasusConfig
_UpperCamelCase : Optional[int] = {}
_UpperCamelCase : List[str] = 'gelu'
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=False , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case=0.1 , snake_case=0.1 , snake_case=20 , snake_case=2 , snake_case=1 , snake_case=0 , ):
'''simple docstring'''
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = seq_length
UpperCamelCase__ = is_training
UpperCamelCase__ = use_labels
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = eos_token_id
UpperCamelCase__ = pad_token_id
UpperCamelCase__ = bos_token_id
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
UpperCamelCase__ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
UpperCamelCase__ = np.concatenate([input_ids, eos_tensor] , axis=1 )
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCamelCase__ = prepare_pegasus_inputs_dict(snake_case , snake_case , snake_case )
return config, inputs_dict
def snake_case__ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
UpperCamelCase__ = 20
UpperCamelCase__ = model_class_name(snake_case )
UpperCamelCase__ = model.encode(inputs_dict["input_ids"] )
UpperCamelCase__, UpperCamelCase__ = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCamelCase__ = model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case )
UpperCamelCase__ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
UpperCamelCase__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase__ = model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCamelCase__ = model.decode(
decoder_input_ids[:, -1:] , snake_case , decoder_attention_mask=snake_case , past_key_values=outputs_cache.past_key_values , decoder_position_ids=snake_case , )
UpperCamelCase__ = model.decode(snake_case , snake_case )
UpperCamelCase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
def snake_case__ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
UpperCamelCase__ = 20
UpperCamelCase__ = model_class_name(snake_case )
UpperCamelCase__ = model.encode(inputs_dict["input_ids"] )
UpperCamelCase__, UpperCamelCase__ = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCamelCase__ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase__ = model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case )
UpperCamelCase__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase__ = model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCamelCase__ = model.decode(
decoder_input_ids[:, -1:] , snake_case , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase__ = model.decode(snake_case , snake_case , decoder_attention_mask=snake_case )
UpperCamelCase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
def UpperCamelCase_( _A :Any , _A :Union[str, Any] , _A :Tuple , _A :Optional[Any]=None , _A :List[Any]=None , )-> int:
if attention_mask is None:
UpperCamelCase__ = np.not_equal(_A , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCamelCase__ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class lowerCamelCase__ ( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : Any = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_UpperCamelCase : str = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_UpperCamelCase : Optional[int] = True
_UpperCamelCase : int = False
_UpperCamelCase : Any = False
_UpperCamelCase : List[Any] = False
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__ = FlaxPegasusModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=snake_case )
def snake_case__ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__, UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(snake_case , snake_case , snake_case )
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__, UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(snake_case , snake_case , snake_case )
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__, UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase__ = self._prepare_for_class(snake_case , snake_case )
UpperCamelCase__ = model_class(snake_case )
@jax.jit
def encode_jitted(snake_case , snake_case=None , **snake_case ):
return model.encode(input_ids=snake_case , attention_mask=snake_case )
with self.subTest("JIT Enabled" ):
UpperCamelCase__ = encode_jitted(**snake_case ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCamelCase__ = encode_jitted(**snake_case ).to_tuple()
self.assertEqual(len(snake_case ) , len(snake_case ) )
for jitted_output, output in zip(snake_case , snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__, UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase__ = model_class(snake_case )
UpperCamelCase__ = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
UpperCamelCase__ = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(snake_case , snake_case , snake_case ):
return model.decode(
decoder_input_ids=snake_case , decoder_attention_mask=snake_case , encoder_outputs=snake_case , )
with self.subTest("JIT Enabled" ):
UpperCamelCase__ = decode_jitted(**snake_case ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCamelCase__ = decode_jitted(**snake_case ).to_tuple()
self.assertEqual(len(snake_case ) , len(snake_case ) )
for jitted_output, output in zip(snake_case , snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def snake_case__ ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCamelCase__ = model_class_name.from_pretrained("google/pegasus-large" , from_pt=snake_case )
UpperCamelCase__ = np.ones((1, 1) )
UpperCamelCase__ = model(snake_case )
self.assertIsNotNone(snake_case )
@slow
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__ = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
UpperCamelCase__ = PegasusTokenizer.from_pretrained("google/pegasus-xsum" )
UpperCamelCase__ = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
UpperCamelCase__ = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
UpperCamelCase__ = tokenizer(snake_case , return_tensors="np" , truncation=snake_case , max_length=512 , padding=snake_case )
UpperCamelCase__ = model.generate(**snake_case , num_beams=2 ).sequences
UpperCamelCase__ = tokenizer.batch_decode(snake_case , skip_special_tokens=snake_case )
assert tgt_text == decoded
| 185 |
def UpperCamelCase_( _A :str )-> str:
UpperCamelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase_( _A :str )-> dict[str, str]:
UpperCamelCase__ = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
UpperCamelCase__ = remove_duplicates(key.upper() )
UpperCamelCase__ = len(_A )
# First fill cipher with key characters
UpperCamelCase__ = {alphabet[i]: char for i, char in enumerate(_A )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_A ) , 26 ):
UpperCamelCase__ = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
UpperCamelCase__ = alphabet[i - offset]
UpperCamelCase__ = char
return cipher_alphabet
def UpperCamelCase_( _A :str , _A :dict[str, str] )-> str:
return "".join(cipher_map.get(_A , _A ) for ch in message.upper() )
def UpperCamelCase_( _A :str , _A :dict[str, str] )-> str:
UpperCamelCase__ = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_A , _A ) for ch in message.upper() )
def UpperCamelCase_( )-> None:
UpperCamelCase__ = input("Enter message to encode or decode: " ).strip()
UpperCamelCase__ = input("Enter keyword: " ).strip()
UpperCamelCase__ = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
UpperCamelCase__ = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
UpperCamelCase__ = create_cipher_map(_A )
print(func(_A , _A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 185 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : str = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
"""tokenization_deberta""": ["""DebertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Any = ["""DebertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : List[str] = [
"""DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DebertaForMaskedLM""",
"""DebertaForQuestionAnswering""",
"""DebertaForSequenceClassification""",
"""DebertaForTokenClassification""",
"""DebertaModel""",
"""DebertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Tuple = [
"""TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDebertaForMaskedLM""",
"""TFDebertaForQuestionAnswering""",
"""TFDebertaForSequenceClassification""",
"""TFDebertaForTokenClassification""",
"""TFDebertaModel""",
"""TFDebertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
lowerCamelCase__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 33 |
def a ( A__ ) -> str:
'''simple docstring'''
return "".join([hex(A__ )[2:].zfill(2 ).upper() for byte in list(A__ )] )
def a ( A__ ) -> bytes:
'''simple docstring'''
if (len(A__ ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(A__ ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 1_6 ) for i in range(0 , len(A__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : int ) -> List[Any]:
for i in range(0, _UpperCamelCase ):
for _ in range(0, n - i - 1 ): # printing spaces
print(''' ''', end='''''' )
for _ in range(0, i + 1 ): # printing stars
print('''* ''', end='''''' )
print()
def _UpperCAmelCase ( _UpperCamelCase : Dict ) -> str:
for i in range(_UpperCamelCase, 0, -1 ):
for _ in range(_UpperCamelCase, 0, -1 ): # printing stars
print('''* ''', end='''''' )
print()
for _ in range(n - i + 1, 0, -1 ): # printing spaces
print(''' ''', end='''''' )
def _UpperCAmelCase ( _UpperCamelCase : List[str] ) -> int:
if n <= 0:
print(''' ... .... nothing printing :(''' )
return
floyd(_UpperCamelCase ) # upper half
reverse_floyd(_UpperCamelCase ) # lower half
if __name__ == "__main__":
print(R'| /\ | |- | |- |--| |\ /| |-')
print(R'|/ \| |- |_ |_ |__| | \/ | |_')
__snake_case : List[str] = 1
while K:
__snake_case : List[str] = int(input('enter the number and , and see the magic : '))
print()
pretty_print(user_number)
__snake_case : Any = int(input('press 0 to exit... and 1 to continue...'))
print('Good Bye...')
| 720 | '''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Tuple = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__snake_case : List[str] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def _UpperCAmelCase ( _UpperCamelCase : Optional[Any], _UpperCamelCase : Union[str, Any], _UpperCamelCase : str, _UpperCamelCase : Tuple, _UpperCamelCase : List[Any] ) -> 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
elif weight_type == "running_mean":
A_ = value
elif weight_type == "running_var":
A_ = value
elif weight_type == "num_batches_tracked":
A_ = value
elif weight_type == "inv_freq":
A_ = value
else:
A_ = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def _UpperCAmelCase ( _UpperCamelCase : List[Any], _UpperCamelCase : Tuple, _UpperCamelCase : Dict ) -> List[Any]:
A_ = []
A_ = fairseq_model.state_dict()
A_ = hf_model.wavaveca_conformer.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_ = '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
A_ = True
if "*" in mapped_key:
A_ = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
A_ = mapped_key.replace('''*''', _UpperCamelCase )
if "pos_bias_u" in name:
A_ = None
elif "pos_bias_v" in name:
A_ = None
elif "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'''
elif "running_mean" in name:
A_ = '''running_mean'''
elif "inv_freq" in name:
A_ = '''inv_freq'''
elif "running_var" in name:
A_ = '''running_var'''
elif "num_batches_tracked" in name:
A_ = '''num_batches_tracked'''
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 _UpperCAmelCase ( _UpperCamelCase : Any, _UpperCamelCase : Dict, _UpperCamelCase : Union[str, Any], _UpperCamelCase : str, _UpperCamelCase : Optional[Any] ) -> List[Any]:
A_ = full_name.split('''conv_layers.''' )[-1]
A_ = name.split('''.''' )
A_ = int(items[0] )
A_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
A_ = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
A_ = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
A_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
A_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def _UpperCAmelCase ( _UpperCamelCase : str, _UpperCamelCase : Dict, _UpperCamelCase : Any=None, _UpperCamelCase : Any=None, _UpperCamelCase : Dict=True ) -> Union[str, Any]:
if config_path is not None:
A_ = WavaVecaConformerConfig.from_pretrained(_UpperCamelCase, hidden_act='''swish''' )
else:
A_ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
A_ = '''rotary'''
if is_finetuned:
if dict_path:
A_ = Dictionary.load(_UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ = target_dict.pad_index
A_ = target_dict.bos_index
A_ = target_dict.eos_index
A_ = len(target_dict.symbols )
A_ = os.path.join(_UpperCamelCase, '''vocab.json''' )
if not os.path.isdir(_UpperCamelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase ) )
return
os.makedirs(_UpperCamelCase, exist_ok=_UpperCamelCase )
A_ = target_dict.indices
# fairseq has the <pad> and <s> switched
A_ = 0
A_ = 1
with open(_UpperCamelCase, '''w''', encoding='''utf-8''' ) as vocab_handle:
json.dump(_UpperCamelCase, _UpperCamelCase )
A_ = WavaVecaCTCTokenizer(
_UpperCamelCase, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token='''|''', do_lower_case=_UpperCamelCase, )
A_ = True if config.feat_extract_norm == '''layer''' else False
A_ = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=1_60_00, padding_value=0, do_normalize=_UpperCamelCase, return_attention_mask=_UpperCamelCase, )
A_ = WavaVecaProcessor(feature_extractor=_UpperCamelCase, tokenizer=_UpperCamelCase )
processor.save_pretrained(_UpperCamelCase )
A_ = WavaVecaConformerForCTC(_UpperCamelCase )
else:
A_ = WavaVecaConformerForPreTraining(_UpperCamelCase )
if is_finetuned:
A_ ,A_ ,A_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
A_ = argparse.Namespace(task='''audio_pretraining''' )
A_ = fairseq.tasks.setup_task(_UpperCamelCase )
A_ ,A_ ,A_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=_UpperCamelCase )
A_ = model[0].eval()
recursively_load_weights(_UpperCamelCase, _UpperCamelCase, not is_finetuned )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__snake_case : 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('--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'
)
__snake_case : List[str] = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 174 | 0 |
'''simple docstring'''
lowercase : List[Any] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE__ ( __A ) -> int:
_snake_case = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_snake_case = Stack()
_snake_case = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCamelCase_ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowerCamelCase_ )
elif i == ")":
# RULE 4
_snake_case = operator_stack.peek()
operator_stack.pop()
_snake_case = operand_stack.peek()
operand_stack.pop()
_snake_case = operand_stack.peek()
operand_stack.pop()
_snake_case = operators[opr](lowerCamelCase_ , lowerCamelCase_ )
operand_stack.push(lowerCamelCase_ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
lowercase : Optional[int] = '''(5 + ((4 * 2) * (2 + 3)))'''
# answer = 45
print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 495 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_ )
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_case__ ):
super().__init__(*snake_case__ ,**snake_case__ )
self.check_model_type(snake_case__ )
def snake_case ( self ,snake_case__=None ,snake_case__=None ,snake_case__=None ,**snake_case__ ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = {}, {}
if padding is not None:
SCREAMING_SNAKE_CASE_ : Any = padding
if truncation is not None:
SCREAMING_SNAKE_CASE_ : Tuple = truncation
if top_k is not None:
SCREAMING_SNAKE_CASE_ : int = top_k
return preprocess_params, {}, postprocess_params
def __call__( self ,snake_case__ ,snake_case__ = None ,**snake_case__ ):
if isinstance(snake_case__ ,(Image.Image, str) ) and isinstance(snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = {'image': image, 'question': question}
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = image
SCREAMING_SNAKE_CASE_ : List[Any] = super().__call__(snake_case__ ,**snake_case__ )
return results
def snake_case ( self ,snake_case__ ,snake_case__=False ,snake_case__=False ):
SCREAMING_SNAKE_CASE_ : List[str] = load_image(inputs['image'] )
SCREAMING_SNAKE_CASE_ : Dict = self.tokenizer(
inputs['question'] ,return_tensors=self.framework ,padding=snake_case__ ,truncation=snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = self.image_processor(images=snake_case__ ,return_tensors=self.framework )
model_inputs.update(snake_case__ )
return model_inputs
def snake_case ( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = self.model(**snake_case__ )
return model_outputs
def snake_case ( self ,snake_case__ ,snake_case__=5 ):
if top_k > self.model.config.num_labels:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.model.config.num_labels
if self.framework == "pt":
SCREAMING_SNAKE_CASE_ : Any = model_outputs.logits.sigmoid()[0]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = probs.topk(snake_case__ )
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
SCREAMING_SNAKE_CASE_ : Optional[int] = scores.tolist()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__ ,snake_case__ )]
| 105 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _UpperCamelCase ):
def UpperCAmelCase ( self : Optional[int] , a_ : float ) -> float:
'''simple docstring'''
return 0.0
def lowercase__ ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : int ) -> tuple[int | float, int | float]:
'''simple docstring'''
a__ : Optional[int] = min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1] )] )
a__ : Union[str, Any] = max([2_0, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase__ ( lowerCAmelCase__ : FilterType , lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
a__ : Optional[int] = 5_1_2
a__ : List[str] = [1] + [0] * (size - 1)
a__ : str = [filter_type.process(lowerCAmelCase__ ) for item in inputs]
a__ : List[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
a__ : Union[str, Any] = np.abs(np.fft.fft(lowerCAmelCase__ ) )
a__ : List[Any] = 2_0 * np.logaa(lowerCAmelCase__ )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(2_4 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
a__ : Tuple = get_bounds(lowerCAmelCase__ , lowerCAmelCase__ )
plt.ylim(max([-8_0, bounds[0]] ) , min([8_0, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(lowerCAmelCase__ )
plt.show()
def lowercase__ ( lowerCAmelCase__ : FilterType , lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
a__ : Dict = 5_1_2
a__ : Dict = [1] + [0] * (size - 1)
a__ : str = [filter_type.process(lowerCAmelCase__ ) for item in inputs]
a__ : Dict = [0] * (samplerate - size) # zero-padding
outputs += filler
a__ : str = np.angle(np.fft.fft(lowerCAmelCase__ ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(2_4 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(lowerCAmelCase__ , -2 * pi ) )
plt.show() | 251 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __UpperCAmelCase :
def __init__( self : Union[str, Any] , a_ : Any ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] = data
a__ : Dict = [0x67452301, 0xefcdab89, 0x98badcfe, 0x10325476, 0xc3d2e1f0]
@staticmethod
def UpperCAmelCase ( a_ : Tuple , a_ : Any ) -> Tuple:
'''simple docstring'''
return ((n << b) | (n >> (32 - b))) & 0xffffffff
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
a__ : int = B"\x80" + B"\x00" * (63 - (len(self.data ) + 8) % 64)
a__ : Tuple = self.data + padding + struct.pack(">Q" , 8 * len(self.data ) )
return padded_data
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def UpperCAmelCase ( self : str , a_ : str ) -> Any:
'''simple docstring'''
a__ : str = list(struct.unpack(">16L" , a_ ) ) + [0] * 64
for i in range(16 , 80 ):
a__ : Tuple = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def UpperCAmelCase ( self : Any ) -> Dict:
'''simple docstring'''
a__ : Any = self.padding()
a__ : Dict = self.split_blocks()
for block in self.blocks:
a__ : str = self.expand_block(a_ )
a__ , a__ , a__ , a__ , a__ : Any = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
a__ : Union[str, Any] = (b & c) | ((~b) & d)
a__ : Dict = 0x5a827999
elif 20 <= i < 40:
a__ : Optional[Any] = b ^ c ^ d
a__ : Tuple = 0x6ed9eba1
elif 40 <= i < 60:
a__ : int = (b & c) | (b & d) | (c & d)
a__ : Union[str, Any] = 0x8f1bbcdc
elif 60 <= i < 80:
a__ : Tuple = b ^ c ^ d
a__ : List[str] = 0xca62c1d6
a__ , a__ , a__ , a__ , a__ : List[str] = (
self.rotate(a_ , 5 ) + f + e + k + expanded_block[i] & 0xffffffff,
a,
self.rotate(a_ , 30 ),
c,
d,
)
a__ : str = (
self.h[0] + a & 0xffffffff,
self.h[1] + b & 0xffffffff,
self.h[2] + c & 0xffffffff,
self.h[3] + d & 0xffffffff,
self.h[4] + e & 0xffffffff,
)
return ("{:08x}" * 5).format(*self.h )
def lowercase__ ( ) -> Any:
'''simple docstring'''
a__ : Union[str, Any] = b"Test String"
assert SHAaHash(lowerCAmelCase__ ).final_hash() == hashlib.shaa(lowerCAmelCase__ ).hexdigest() # noqa: S324
def lowercase__ ( ) -> List[str]:
'''simple docstring'''
a__ : Optional[Any] = argparse.ArgumentParser(description="Process some strings or files" )
parser.add_argument(
"--string" , dest="input_string" , default="Hello World!! Welcome to Cryptography" , help="Hash the string" , )
parser.add_argument("--file" , dest="input_file" , help="Hash contents of a file" )
a__ : Tuple = parser.parse_args()
a__ : Tuple = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , "rb" ) as f:
a__ : str = f.read()
else:
a__ : int = bytes(lowerCAmelCase__ , "utf-8" )
print(SHAaHash(lowerCAmelCase__ ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod() | 251 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 154 |
'''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
a__ : Tuple = logging.get_logger(__name__)
def __snake_case ( SCREAMING_SNAKE_CASE_ : List[str] ) -> List[List[ImageInput]]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(SCREAMING_SNAKE_CASE_ ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["pixel_values"]
def __init__( self : int , a__ : bool = True , a__ : Dict[str, int] = None , a__ : PILImageResampling = PILImageResampling.BILINEAR , a__ : bool = True , a__ : Dict[str, int] = None , a__ : bool = True , a__ : Union[int, float] = 1 / 255 , a__ : bool = True , a__ : bool = True , a__ : Optional[Union[float, List[float]]] = None , a__ : Optional[Union[float, List[float]]] = None , **a__ : Union[str, Any] , ):
super().__init__(**a__ )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase = get_size_dict(a__ , default_to_square=a__ )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(a__ , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = resample
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = offset
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __snake_case ( self : Dict , a__ : np.ndarray , a__ : Dict[str, int] , a__ : PILImageResampling = PILImageResampling.BILINEAR , a__ : Optional[Union[str, ChannelDimension]] = None , **a__ : Optional[int] , ):
UpperCAmelCase = get_size_dict(a__ , default_to_square=a__ )
if "shortest_edge" in size:
UpperCAmelCase = get_resize_output_image_size(a__ , size['''shortest_edge'''] , default_to_square=a__ )
elif "height" in size and "width" in size:
UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(a__ , size=a__ , resample=a__ , data_format=a__ , **a__ )
def __snake_case ( self : Union[str, Any] , a__ : np.ndarray , a__ : Dict[str, int] , a__ : Optional[Union[str, ChannelDimension]] = None , **a__ : Any , ):
UpperCAmelCase = get_size_dict(a__ )
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(a__ , size=(size['''height'''], size['''width''']) , data_format=a__ , **a__ )
def __snake_case ( self : List[str] , a__ : np.ndarray , a__ : Union[int, float] , a__ : bool = True , a__ : Optional[Union[str, ChannelDimension]] = None , **a__ : Dict , ):
UpperCAmelCase = image.astype(np.floataa )
if offset:
UpperCAmelCase = image - (scale / 2)
return rescale(a__ , scale=a__ , data_format=a__ , **a__ )
def __snake_case ( self : int , a__ : np.ndarray , a__ : Union[float, List[float]] , a__ : Union[float, List[float]] , a__ : Optional[Union[str, ChannelDimension]] = None , **a__ : Any , ):
return normalize(a__ , mean=a__ , std=a__ , data_format=a__ , **a__ )
def __snake_case ( self : Any , a__ : ImageInput , a__ : bool = None , a__ : Dict[str, int] = None , a__ : PILImageResampling = None , a__ : bool = None , a__ : Dict[str, int] = None , a__ : bool = None , a__ : float = None , a__ : bool = None , a__ : bool = None , a__ : Optional[Union[float, List[float]]] = None , a__ : Optional[Union[float, List[float]]] = None , a__ : Optional[ChannelDimension] = 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.
UpperCAmelCase = to_numpy_array(a__ )
if do_resize:
UpperCAmelCase = self.resize(image=a__ , size=a__ , resample=a__ )
if do_center_crop:
UpperCAmelCase = self.center_crop(a__ , size=a__ )
if do_rescale:
UpperCAmelCase = self.rescale(image=a__ , scale=a__ , offset=a__ )
if do_normalize:
UpperCAmelCase = self.normalize(image=a__ , mean=a__ , std=a__ )
UpperCAmelCase = to_channel_dimension_format(a__ , a__ )
return image
def __snake_case ( self : List[Any] , a__ : ImageInput , a__ : bool = None , a__ : Dict[str, int] = None , a__ : PILImageResampling = None , a__ : bool = None , a__ : Dict[str, int] = None , a__ : bool = None , a__ : float = None , a__ : bool = None , a__ : bool = None , a__ : Optional[Union[float, List[float]]] = None , a__ : Optional[Union[float, List[float]]] = None , a__ : Optional[Union[str, TensorType]] = None , a__ : ChannelDimension = ChannelDimension.FIRST , **a__ : Any , ):
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = offset if offset is not None else self.offset
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(a__ , default_to_square=a__ )
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(a__ , param_name='''crop_size''' )
if not valid_images(a__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
UpperCAmelCase = make_batched(a__ )
UpperCAmelCase = [
[
self._preprocess_image(
image=a__ , do_resize=a__ , size=a__ , resample=a__ , do_center_crop=a__ , crop_size=a__ , do_rescale=a__ , rescale_factor=a__ , offset=a__ , do_normalize=a__ , image_mean=a__ , image_std=a__ , data_format=a__ , )
for img in video
]
for video in videos
]
UpperCAmelCase = {'''pixel_values''': videos}
return BatchFeature(data=a__ , tensor_type=a__ )
| 51 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , a , a=7 , a=3 , a=18 , a=30 , a=4_00 , a=True , a=None , a=True , a=None , a=True , ):
UpperCamelCase__ = size if size is not None else {"shortest_edge": 20}
UpperCamelCase__ = crop_size if crop_size is not None else {"height": 18, "width": 18}
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = image_size
UpperCamelCase__ = min_resolution
UpperCamelCase__ = max_resolution
UpperCamelCase__ = do_resize
UpperCamelCase__ = size
UpperCamelCase__ = do_center_crop
UpperCamelCase__ = crop_size
UpperCamelCase__ = do_flip_channel_order
def __a ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class lowercase_ ( a__ , unittest.TestCase ):
__UpperCAmelCase = MobileViTImageProcessor if is_vision_available() else None
def __a ( self ):
UpperCamelCase__ = MobileViTImageProcessingTester(self )
@property
def __a ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self ):
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , "do_resize" ) )
self.assertTrue(hasattr(a , "size" ) )
self.assertTrue(hasattr(a , "do_center_crop" ) )
self.assertTrue(hasattr(a , "center_crop" ) )
self.assertTrue(hasattr(a , "do_flip_channel_order" ) )
def __a ( self ):
UpperCamelCase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
UpperCamelCase__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def __a ( self ):
pass
def __a ( self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
UpperCamelCase__ = 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
UpperCamelCase__ = image_processing(a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __a ( self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a , numpify=a )
for image in image_inputs:
self.assertIsInstance(a , np.ndarray )
# Test not batched input
UpperCamelCase__ = 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
UpperCamelCase__ = image_processing(a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __a ( self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a , torchify=a )
for image in image_inputs:
self.assertIsInstance(a , torch.Tensor )
# Test not batched input
UpperCamelCase__ = 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
UpperCamelCase__ = image_processing(a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 710 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
a__ : int = datasets.logging.get_logger(__name__)
a__ : Union[str, Any] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n booktitle = {Proceedings of the Fifth Conference on Machine Translation},\n month = {November},\n year = {2020},\n address = {Online},\n publisher = {Association for Computational Linguistics},\n pages = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n title = "{COMET}: A Neural Framework for {MT} Evaluation",\n author = "Rei, Ricardo and\n Stewart, Craig and\n Farinha, Ana C and\n Lavie, Alon",\n booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n month = nov,\n year = "2020",\n address = "Online",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n pages = "2685--2702",\n}\n'
a__ : Optional[int] = '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'
a__ : str = '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n `scores`: List of scores.\n\nExamples:\n\n >>> comet_metric = datasets.load_metric(\'comet\')\n >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use\n >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n >>> print([round(v, 2) for v in results["scores"]])\n [0.19, 0.92]\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def __a ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://unbabel.github.io/COMET/html/index.html" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"sources": datasets.Value("string" , id="sequence" ),
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/Unbabel/COMET"] , reference_urls=[
"https://github.com/Unbabel/COMET",
"https://www.aclweb.org/anthology/2020.emnlp-main.213/",
"http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
] , )
def __a ( self , a ):
if self.config_name == "default":
UpperCamelCase__ = comet.load_from_checkpoint(comet.download_model("wmt20-comet-da" ) )
else:
UpperCamelCase__ = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def __a ( self , a , a , a , a=None , a=False ):
if gpus is None:
UpperCamelCase__ = 1 if torch.cuda.is_available() else 0
UpperCamelCase__ = {"src": sources, "mt": predictions, "ref": references}
UpperCamelCase__ = [dict(zip(a , a ) ) for t in zip(*data.values() )]
UpperCamelCase__ , UpperCamelCase__ = self.scorer.predict(a , gpus=a , progress_bar=a )
return {"mean_score": mean_score, "scores": scores}
| 223 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE_:Dict = NewType("""DataClass""", Any)
SCREAMING_SNAKE_CASE_:Dict = NewType("""DataClassType""", Any)
def __UpperCamelCase ( _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def __UpperCamelCase ( _lowerCAmelCase ) -> Callable[[str], Any]:
"""simple docstring"""
A : int = {str(_lowerCAmelCase ): choice for choice in choices}
return lambda _lowerCAmelCase : str_to_choice.get(_lowerCAmelCase , _lowerCAmelCase )
def __UpperCamelCase ( *,
_lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = dataclasses.MISSING , _lowerCAmelCase = dataclasses.MISSING , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
A : Tuple = {}
if aliases is not None:
A : Dict = aliases
if help is not None:
A : Any = help
return dataclasses.field(metadata=_lowerCAmelCase , default=_lowerCAmelCase , default_factory=_lowerCAmelCase , **_lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Iterable[DataClassType]
def __init__( self, lowerCamelCase__, **lowerCamelCase__ ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
A : Tuple = ArgumentDefaultsHelpFormatter
super().__init__(**lowerCamelCase__ )
if dataclasses.is_dataclass(lowerCamelCase__ ):
A : int = [dataclass_types]
A : Any = list(lowerCamelCase__ )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(lowerCamelCase__ )
@staticmethod
def _lowerCAmelCase ( lowerCamelCase__, lowerCamelCase__ ):
A : Union[str, Any] = f'''--{field.name}'''
A : str = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type, lowerCamelCase__ ):
raise RuntimeError(
"""Unresolved type detected, which should have been done with the help of """
"""`typing.get_type_hints` method by default""" )
A : Optional[int] = kwargs.pop("""aliases""", [] )
if isinstance(lowerCamelCase__, lowerCamelCase__ ):
A : Optional[Any] = [aliases]
A : List[Any] = getattr(field.type, """__origin__""", field.type )
if origin_type is Union or (hasattr(lowerCamelCase__, """UnionType""" ) and isinstance(lowerCamelCase__, types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(lowerCamelCase__ ) not in field.type.__args__
):
raise ValueError(
"""Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
""" the argument parser only supports one type per argument."""
f''' Problem encountered in field \'{field.name}\'.''' )
if type(lowerCamelCase__ ) not in field.type.__args__:
# filter `str` in Union
A : Optional[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
A : List[str] = getattr(field.type, """__origin__""", field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
A : List[Any] = (
field.type.__args__[0] if isinstance(lowerCamelCase__, field.type.__args__[1] ) else field.type.__args__[1]
)
A : Optional[Any] = getattr(field.type, """__origin__""", field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
A : Tuple = {}
if origin_type is Literal or (isinstance(field.type, lowerCamelCase__ ) and issubclass(field.type, lowerCamelCase__ )):
if origin_type is Literal:
A : int = field.type.__args__
else:
A : Union[str, Any] = [x.value for x in field.type]
A : Optional[Any] = make_choice_type_function(kwargs["""choices"""] )
if field.default is not dataclasses.MISSING:
A : Optional[Any] = field.default
else:
A : Optional[int] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
A : Optional[Any] = copy(lowerCamelCase__ )
# Hack because type=bool in argparse does not behave as we want.
A : Optional[int] = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
A : Any = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
A : Tuple = default
# This tells argparse we accept 0 or 1 value after --field_name
A : Union[str, Any] = """?"""
# This is the value that will get picked if we do --field_name (without value)
A : Optional[int] = True
elif isclass(lowerCamelCase__ ) and issubclass(lowerCamelCase__, lowerCamelCase__ ):
A : Any = field.type.__args__[0]
A : List[str] = """+"""
if field.default_factory is not dataclasses.MISSING:
A : str = field.default_factory()
elif field.default is dataclasses.MISSING:
A : Optional[int] = True
else:
A : Tuple = field.type
if field.default is not dataclasses.MISSING:
A : Tuple = field.default
elif field.default_factory is not dataclasses.MISSING:
A : str = field.default_factory()
else:
A : Tuple = True
parser.add_argument(lowerCamelCase__, *lowerCamelCase__, **lowerCamelCase__ )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
A : Union[str, Any] = False
parser.add_argument(f'''--no_{field.name}''', action="""store_false""", dest=field.name, **lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
if hasattr(lowerCamelCase__, """_argument_group_name""" ):
A : Optional[int] = self.add_argument_group(dtype._argument_group_name )
else:
A : Optional[int] = self
try:
A : Dict[str, type] = get_type_hints(lowerCamelCase__ )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"""removing line of `from __future__ import annotations` which opts in Postponed """
"""Evaluation of Annotations (PEP 563)""" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowerCamelCase__ ):
A : int = """.""".join(map(lowerCamelCase__, sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"""line of `from __future__ import annotations` which opts in union types as """
"""`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
"""support Python versions that lower than 3.10, you need to use """
"""`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
"""`X | None`.""" ) from ex
raise
for field in dataclasses.fields(lowerCamelCase__ ):
if not field.init:
continue
A : List[str] = type_hints[field.name]
self._parse_dataclass_field(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__=None, lowerCamelCase__=False, lowerCamelCase__=True, lowerCamelCase__=None, lowerCamelCase__=None, ):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
A : Optional[int] = []
if args_filename:
args_files.append(Path(lowerCamelCase__ ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(""".args""" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
A : Union[str, Any] = ArgumentParser()
args_file_parser.add_argument(lowerCamelCase__, type=lowerCamelCase__, action="""append""" )
# Use only remaining args for further parsing (remove the args_file_flag)
A , A : List[Any] = args_file_parser.parse_known_args(args=lowerCamelCase__ )
A : Dict = vars(lowerCamelCase__ ).get(args_file_flag.lstrip("""-""" ), lowerCamelCase__ )
if cmd_args_file_paths:
args_files.extend([Path(lowerCamelCase__ ) for p in cmd_args_file_paths] )
A : Any = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
A : Dict = file_args + args if args is not None else file_args + sys.argv[1:]
A , A : Tuple = self.parse_known_args(args=lowerCamelCase__ )
A : List[Any] = []
for dtype in self.dataclass_types:
A : Union[str, Any] = {f.name for f in dataclasses.fields(lowerCamelCase__ ) if f.init}
A : Any = {k: v for k, v in vars(lowerCamelCase__ ).items() if k in keys}
for k in keys:
delattr(lowerCamelCase__, lowerCamelCase__ )
A : str = dtype(**lowerCamelCase__ )
outputs.append(lowerCamelCase__ )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(lowerCamelCase__ )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = False ):
A : str = set(args.keys() )
A : Tuple = []
for dtype in self.dataclass_types:
A : List[str] = {f.name for f in dataclasses.fields(lowerCamelCase__ ) if f.init}
A : Optional[int] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
A : Optional[int] = dtype(**lowerCamelCase__ )
outputs.append(lowerCamelCase__ )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(lowerCamelCase__ )}''' )
return tuple(lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = False ):
with open(Path(lowerCamelCase__ ), encoding="""utf-8""" ) as open_json_file:
A : Optional[Any] = json.loads(open_json_file.read() )
A : Tuple = self.parse_dict(lowerCamelCase__, allow_extra_keys=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = False ):
A : List[str] = self.parse_dict(yaml.safe_load(Path(lowerCamelCase__ ).read_text() ), allow_extra_keys=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 662 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:int = """Hello, World!"""
SCREAMING_SNAKE_CASE_:List[Any] = """en_XX"""
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Any:
"""simple docstring"""
A : Optional[int] = Path("""data_bin""" )
A : Optional[Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowerCAmelCase ).parent ) , checkpoint_file=Path(_lowerCAmelCase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowerCAmelCase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowerCAmelCase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowerCAmelCase )
A : Any = xmod.model.encoder.sentence_encoder
A : Optional[int] = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
A : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowerCAmelCase )
A : int = XmodForSequenceClassification(_lowerCAmelCase ) if classification_head else XmodForMaskedLM(_lowerCAmelCase )
model.eval()
# Now let's copy all the weights.
# Embeddings
A : Any = xmod_sent_encoder.embed_tokens.weight
A : int = xmod_sent_encoder.embed_positions.weight
A : str = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
A : Dict = xmod_sent_encoder.layernorm_embedding.weight
A : int = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
A : str = model.roberta.encoder.layer[i]
A : Tuple = xmod_sent_encoder.layers[i]
# self attention
A : Optional[int] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
A : List[str] = xmod_layer.self_attn.q_proj.weight
A : Optional[int] = xmod_layer.self_attn.q_proj.bias
A : List[Any] = xmod_layer.self_attn.k_proj.weight
A : Union[str, Any] = xmod_layer.self_attn.k_proj.bias
A : Optional[int] = xmod_layer.self_attn.v_proj.weight
A : Dict = xmod_layer.self_attn.v_proj.bias
# self-attention output
A : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
A : Optional[Any] = xmod_layer.self_attn.out_proj.weight
A : Dict = xmod_layer.self_attn.out_proj.bias
A : Union[str, Any] = xmod_layer.self_attn_layer_norm.weight
A : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
A : str = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
A : Optional[int] = xmod_layer.fca.weight
A : Optional[int] = xmod_layer.fca.bias
# output
A : Dict = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
A : Union[str, Any] = xmod_layer.fca.weight
A : int = xmod_layer.fca.bias
A : List[str] = xmod_layer.final_layer_norm.weight
A : Optional[Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
A : str = xmod_layer.adapter_layer_norm.weight
A : str = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
A : Optional[int] = bert_output.adapter_modules[lang_code]
A : int = xmod_layer.adapter_modules[lang_code]
A : Optional[Any] = from_adapter.fca.weight
A : Optional[Any] = from_adapter.fca.bias
A : List[str] = from_adapter.fca.weight
A : Any = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
A : Dict = xmod_sent_encoder.layer_norm.weight
A : int = xmod_sent_encoder.layer_norm.bias
if classification_head:
A : int = xmod.model.classification_heads["""mnli"""].dense.weight
A : Optional[Any] = xmod.model.classification_heads["""mnli"""].dense.bias
A : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight
A : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
A : Any = xmod.model.encoder.lm_head.dense.weight
A : Tuple = xmod.model.encoder.lm_head.dense.bias
A : Any = xmod.model.encoder.lm_head.layer_norm.weight
A : List[str] = xmod.model.encoder.lm_head.layer_norm.bias
A : Union[str, Any] = xmod.model.encoder.lm_head.weight
A : Tuple = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
A : Optional[int] = xmod.encode(_lowerCAmelCase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowerCAmelCase )
A : List[str] = model(_lowerCAmelCase )[0]
if classification_head:
A : Dict = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowerCAmelCase ) )
else:
A : Optional[Any] = xmod.model(_lowerCAmelCase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
A : str = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
A : Optional[Any] = torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowerCAmelCase ).mkdir(parents=_lowerCAmelCase , exist_ok=_lowerCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_:Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
SCREAMING_SNAKE_CASE_:Optional[Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 662 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( A_ , unittest.TestCase ):
__a = DDIMPipeline
__a = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__a = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
__a = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
__a = False
def lowercase ( self : Optional[Any] ):
torch.manual_seed(0 )
_snake_case = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
_snake_case = DDIMScheduler()
_snake_case = {'''unet''': unet, '''scheduler''': scheduler}
return components
def lowercase ( self : Dict , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple=0 ):
if str(_lowerCamelCase ).startswith('''mps''' ):
_snake_case = torch.manual_seed(_lowerCamelCase )
else:
_snake_case = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
_snake_case = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def lowercase ( self : int ):
_snake_case = '''cpu'''
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
_snake_case = self.get_dummy_inputs(_lowerCamelCase )
_snake_case = pipe(**_lowerCamelCase ).images
_snake_case = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
_snake_case = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
_snake_case = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowerCamelCase , 1e-3 )
def lowercase ( self : Optional[Any] ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowercase ( self : Optional[int] ):
super().test_save_load_local(expected_max_difference=3e-3 )
def lowercase ( self : int ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def lowercase ( self : Union[str, Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase ( self : int ):
_snake_case = '''google/ddpm-cifar10-32'''
_snake_case = UNetaDModel.from_pretrained(_lowerCamelCase )
_snake_case = DDIMScheduler()
_snake_case = DDIMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
ddim.to(_lowerCamelCase )
ddim.set_progress_bar_config(disable=_lowerCamelCase )
_snake_case = torch.manual_seed(0 )
_snake_case = ddim(generator=_lowerCamelCase , eta=0.0 , output_type='''numpy''' ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_snake_case = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Tuple ):
_snake_case = '''google/ddpm-ema-bedroom-256'''
_snake_case = UNetaDModel.from_pretrained(_lowerCamelCase )
_snake_case = DDIMScheduler.from_pretrained(_lowerCamelCase )
_snake_case = DDIMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
ddpm.to(_lowerCamelCase )
ddpm.set_progress_bar_config(disable=_lowerCamelCase )
_snake_case = torch.manual_seed(0 )
_snake_case = ddpm(generator=_lowerCamelCase , output_type='''numpy''' ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_snake_case = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 430 |
"""simple docstring"""
from statistics import mean
import numpy as np
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : int ) -> list:
_snake_case = 0
# Number of processes finished
_snake_case = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
_snake_case = [0] * no_of_process
# List to include calculation results
_snake_case = [0] * no_of_process
# Sort by arrival time.
_snake_case = [burst_time[i] for i in np.argsort(__lowerCamelCase )]
_snake_case = [process_name[i] for i in np.argsort(__lowerCamelCase )]
arrival_time.sort()
while no_of_process > finished_process_count:
_snake_case = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
_snake_case = arrival_time[i]
_snake_case = 0
# Index showing the location of the process being performed
_snake_case = 0
# Saves the current response ratio.
_snake_case = 0
for i in range(0 , __lowerCamelCase ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
_snake_case = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
_snake_case = temp
_snake_case = i
# Calculate the turn around time
_snake_case = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
_snake_case = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : int ) -> list:
_snake_case = [0] * no_of_process
for i in range(0 , __lowerCamelCase ):
_snake_case = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
UpperCAmelCase__ = 5
UpperCAmelCase__ = ['A', 'B', 'C', 'D', 'E']
UpperCAmelCase__ = [1, 2, 3, 4, 5]
UpperCAmelCase__ = [1, 2, 3, 4, 5]
UpperCAmelCase__ = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
UpperCAmelCase__ = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
for i in range(0, no_of_process):
print(
F"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
F"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
)
print(F"average waiting time : {mean(waiting_time):.5f}")
print(F"average turn around time : {mean(turn_around_time):.5f}")
| 430 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def _snake_case ( A , A , A , A , A ) -> Optional[int]:
for attribute in key.split('''.''' ):
lowerCAmelCase__ = getattr(A , A )
if weight_type is not None:
lowerCAmelCase__ = getattr(A , A ).shape
else:
lowerCAmelCase__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
lowerCAmelCase__ = value
elif weight_type == "weight_g":
lowerCAmelCase__ = value
elif weight_type == "weight_v":
lowerCAmelCase__ = value
elif weight_type == "bias":
lowerCAmelCase__ = value
elif weight_type == "running_mean":
lowerCAmelCase__ = value
elif weight_type == "running_var":
lowerCAmelCase__ = value
elif weight_type == "num_batches_tracked":
lowerCAmelCase__ = value
elif weight_type == "inv_freq":
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _snake_case ( A , A , A ) -> int:
lowerCAmelCase__ = []
lowerCAmelCase__ = fairseq_model.state_dict()
lowerCAmelCase__ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowerCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == '''group''' , )
lowerCAmelCase__ = True
else:
for key, mapped_key in MAPPING.items():
lowerCAmelCase__ = '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowerCAmelCase__ = True
if "*" in mapped_key:
lowerCAmelCase__ = name.split(A )[0].split('''.''' )[-2]
lowerCAmelCase__ = mapped_key.replace('''*''' , A )
if "pos_bias_u" in name:
lowerCAmelCase__ = None
elif "pos_bias_v" in name:
lowerCAmelCase__ = None
elif "weight_g" in name:
lowerCAmelCase__ = '''weight_g'''
elif "weight_v" in name:
lowerCAmelCase__ = '''weight_v'''
elif "bias" in name:
lowerCAmelCase__ = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCAmelCase__ = '''weight'''
elif "running_mean" in name:
lowerCAmelCase__ = '''running_mean'''
elif "inv_freq" in name:
lowerCAmelCase__ = '''inv_freq'''
elif "running_var" in name:
lowerCAmelCase__ = '''running_var'''
elif "num_batches_tracked" in name:
lowerCAmelCase__ = '''num_batches_tracked'''
else:
lowerCAmelCase__ = None
set_recursively(A , A , A , A , A )
continue
if not is_used:
unused_weights.append(A )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _snake_case ( A , A , A , A , A ) -> Optional[Any]:
lowerCAmelCase__ = full_name.split('''conv_layers.''' )[-1]
lowerCAmelCase__ = name.split('''.''' )
lowerCAmelCase__ = int(items[0] )
lowerCAmelCase__ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
lowerCAmelCase__ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A )
@torch.no_grad()
def _snake_case ( A , A , A=None , A=None , A=True ) -> str:
if config_path is not None:
lowerCAmelCase__ = WavaVecaConformerConfig.from_pretrained(A , hidden_act='''swish''' )
else:
lowerCAmelCase__ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowerCAmelCase__ = '''rotary'''
if is_finetuned:
if dict_path:
lowerCAmelCase__ = Dictionary.load(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCAmelCase__ = target_dict.pad_index
lowerCAmelCase__ = target_dict.bos_index
lowerCAmelCase__ = target_dict.eos_index
lowerCAmelCase__ = len(target_dict.symbols )
lowerCAmelCase__ = os.path.join(A , '''vocab.json''' )
if not os.path.isdir(A ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A ) )
return
os.makedirs(A , exist_ok=A )
lowerCAmelCase__ = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
with open(A , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(A , A )
lowerCAmelCase__ = WavaVecaCTCTokenizer(
A , 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=A , )
lowerCAmelCase__ = True if config.feat_extract_norm == '''layer''' else False
lowerCAmelCase__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
lowerCAmelCase__ = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
lowerCAmelCase__ = WavaVecaConformerForCTC(A )
else:
lowerCAmelCase__ = WavaVecaConformerForPreTraining(A )
if is_finetuned:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowerCAmelCase__ = argparse.Namespace(task='''audio_pretraining''' )
lowerCAmelCase__ = fairseq.tasks.setup_task(A )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A )
lowerCAmelCase__ = model[0].eval()
recursively_load_weights(A , A , not is_finetuned )
hf_wavavec.save_pretrained(A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
) | 90 |
"""simple docstring"""
_UpperCamelCase = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCamelCase = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = from_type.lower().strip("""s""" )
UpperCAmelCase = to_type.lower().strip("""s""" )
UpperCAmelCase = UNIT_SYMBOL.get(_snake_case , _snake_case )
UpperCAmelCase = UNIT_SYMBOL.get(_snake_case , _snake_case )
if from_sanitized not in METRIC_CONVERSION:
UpperCAmelCase = (
F'''Invalid \'from_type\' value: {from_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
)
raise ValueError(_snake_case )
if to_sanitized not in METRIC_CONVERSION:
UpperCAmelCase = (
F'''Invalid \'to_type\' value: {to_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
)
raise ValueError(_snake_case )
UpperCAmelCase = METRIC_CONVERSION[from_sanitized]
UpperCAmelCase = METRIC_CONVERSION[to_sanitized]
UpperCAmelCase = 1
if from_exponent > to_exponent:
UpperCAmelCase = from_exponent - to_exponent
else:
UpperCAmelCase = -(to_exponent - from_exponent)
return value * pow(10 , _snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 341 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class lowercase__ ( unittest.TestCase ):
def __init__( self : str , snake_case__ : Optional[Any] , snake_case__ : str=7 , snake_case__ : int=3 , snake_case__ : Dict=18 , snake_case__ : List[str]=30 , snake_case__ : int=400 , snake_case__ : int=True , snake_case__ : List[str]=32 , snake_case__ : Optional[Any]=True , ):
lowerCamelCase_ : Any =parent
lowerCamelCase_ : Any =batch_size
lowerCamelCase_ : List[Any] =num_channels
lowerCamelCase_ : Optional[int] =image_size
lowerCamelCase_ : List[str] =min_resolution
lowerCamelCase_ : str =max_resolution
lowerCamelCase_ : Tuple =do_resize
lowerCamelCase_ : Optional[Any] =size_divisor
lowerCamelCase_ : str =do_rescale
def UpperCAmelCase__ ( self : Any ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class lowercase__ ( snake_case__, unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = GLPNImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : List[str] ):
lowerCamelCase_ : Union[str, Any] =GLPNImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Dict ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : List[Any] ):
lowerCamelCase_ : Optional[int] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "size_divisor" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "resample" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "do_rescale" ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
pass
def UpperCAmelCase__ ( self : List[str] ):
lowerCamelCase_ : List[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase_ : List[Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCamelCase_ : Any =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase__ ( self : int ):
lowerCamelCase_ : str =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase_ : Any =prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCamelCase_ : str =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase__ ( self : List[str] ):
lowerCamelCase_ : Tuple =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase_ : 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 (GLPNImageProcessor doesn't support batching)
lowerCamelCase_ : str =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 707 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :Dict = "naver-clova-ix/donut-base-finetuned-docvqa"
_UpperCAmelCase :str = (
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
_UpperCAmelCase :Union[str, Any] = "document_qa"
_UpperCAmelCase :Union[str, Any] = AutoProcessor
_UpperCAmelCase :List[Any] = VisionEncoderDecoderModel
_UpperCAmelCase :Union[str, Any] = ["image", "text"]
_UpperCAmelCase :Dict = ["text"]
def __init__( self : Optional[int] , *snake_case__ : Optional[Any] , **snake_case__ : Tuple ):
if not is_vision_available():
raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool." )
super().__init__(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self : str , snake_case__ : "Image" , snake_case__ : str ):
lowerCamelCase_ : Union[str, Any] ="<s_docvqa><s_question>{user_input}</s_question><s_answer>"
lowerCamelCase_ : List[Any] =task_prompt.replace("{user_input}" , snake_case__ )
lowerCamelCase_ : int =self.pre_processor.tokenizer(
snake_case__ , add_special_tokens=snake_case__ , return_tensors="pt" ).input_ids
lowerCamelCase_ : Optional[int] =self.pre_processor(snake_case__ , return_tensors="pt" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def UpperCAmelCase__ ( self : int , snake_case__ : Union[str, Any] ):
return self.model.generate(
inputs["pixel_values"].to(self.device ) , decoder_input_ids=inputs["decoder_input_ids"].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=snake_case__ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=snake_case__ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=snake_case__ , ).sequences
def UpperCAmelCase__ ( self : Any , snake_case__ : Dict ):
lowerCamelCase_ : Optional[Any] =self.pre_processor.batch_decode(snake_case__ )[0]
lowerCamelCase_ : int =sequence.replace(self.pre_processor.tokenizer.eos_token , "" )
lowerCamelCase_ : Union[str, Any] =sequence.replace(self.pre_processor.tokenizer.pad_token , "" )
lowerCamelCase_ : Dict =re.sub(r"<.*?>" , "" , snake_case__ , count=1 ).strip() # remove first task start token
lowerCamelCase_ : List[str] =self.pre_processor.tokenajson(snake_case__ )
return sequence["answer"]
| 244 | 0 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def _UpperCAmelCase ( __A : Any ):
a_ : List[str] = fname.split(os.path.sep )[-1]
return re.search(R'''^(.*)_\d+\.jpg$''' , __A ).groups()[0]
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Any=None ) -> Optional[Any]:
a_ : List[str] = file_names
a_ : str = image_transform
a_ : Optional[Any] = label_to_id
def __len__( self : List[str] ) -> Dict:
return len(self.file_names )
def __getitem__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
a_ : Union[str, Any] = self.file_names[idx]
a_ : Union[str, Any] = PIL.Image.open(__SCREAMING_SNAKE_CASE )
a_ : int = raw_image.convert('''RGB''' )
if self.image_transform is not None:
a_ : str = self.image_transform(__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = extract_label(__SCREAMING_SNAKE_CASE )
if self.label_to_id is not None:
a_ : Any = self.label_to_id[label]
return {"image": image, "label": label}
def _UpperCAmelCase ( __A : List[Any] , __A : Any ):
# Initialize accelerator
if args.with_tracking:
a_ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
a_ : Optional[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
a_ : Dict = config['''lr''']
a_ : str = int(config['''num_epochs'''] )
a_ : Any = int(config['''seed'''] )
a_ : Any = int(config['''batch_size'''] )
a_ : Optional[Any] = config['''image_size''']
if not isinstance(__A , (list, tuple) ):
a_ : Union[str, Any] = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , '''isdigit''' ):
if args.checkpointing_steps == "epoch":
a_ : List[str] = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
a_ : List[str] = int(args.checkpointing_steps )
else:
raise ValueError(
f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
a_ : str = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
a_ : Dict = os.path.split(__A )[-1].split('''.''' )[0]
accelerator.init_trackers(__A , __A )
# Grab all the image filenames
a_ : str = [os.path.join(args.data_dir , __A ) for fname in os.listdir(args.data_dir ) if fname.endswith('''.jpg''' )]
# Build the label correspondences
a_ : Dict = [extract_label(__A ) for fname in file_names]
a_ : Optional[Any] = list(set(__A ) )
id_to_label.sort()
a_ : Any = {lbl: i for i, lbl in enumerate(__A )}
# Set the seed before splitting the data.
np.random.seed(__A )
torch.manual_seed(__A )
torch.cuda.manual_seed_all(__A )
# Split our filenames between train and validation
a_ : Optional[Any] = np.random.permutation(len(__A ) )
a_ : Dict = int(0.8 * len(__A ) )
a_ : Optional[Any] = random_perm[:cut]
a_ : Optional[int] = random_perm[cut:]
# For training we use a simple RandomResizedCrop
a_ : Optional[int] = Compose([RandomResizedCrop(__A , scale=(0.5, 1.0) ), ToTensor()] )
a_ : str = PetsDataset(
[file_names[i] for i in train_split] , image_transform=__A , label_to_id=__A )
# For evaluation, we use a deterministic Resize
a_ : Any = Compose([Resize(__A ), ToTensor()] )
a_ : Tuple = PetsDataset([file_names[i] for i in eval_split] , image_transform=__A , label_to_id=__A )
# Instantiate dataloaders.
a_ : Dict = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
a_ : str = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
a_ : Union[str, Any] = create_model('''resnet50d''' , pretrained=__A , num_classes=len(__A ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
a_ : Tuple = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
a_ : int = False
for param in model.get_classifier().parameters():
a_ : Tuple = True
# We normalize the batches of images to be a bit faster.
a_ : Union[str, Any] = torch.tensor(model.default_cfg['''mean'''] )[None, :, None, None].to(accelerator.device )
a_ : Union[str, Any] = torch.tensor(model.default_cfg['''std'''] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
a_ : str = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
a_ : Tuple = OneCycleLR(optimizer=__A , max_lr=__A , epochs=__A , steps_per_epoch=len(__A ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
a_ , a_ , a_ , a_ , a_ : Tuple = accelerator.prepare(
__A , __A , __A , __A , __A )
# We need to keep track of how many total steps we have iterated over
a_ : Dict = 0
# We also need to keep track of the starting epoch so files are named properly
a_ : Dict = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
accelerator.load_state(args.resume_from_checkpoint )
a_ : Optional[Any] = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
a_ : List[str] = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
a_ : Tuple = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
a_ : List[str] = os.path.splitext(__A )[0]
if "epoch" in training_difference:
a_ : Tuple = int(training_difference.replace('''epoch_''' , '''''' ) ) + 1
a_ : str = None
else:
a_ : List[Any] = int(training_difference.replace('''step_''' , '''''' ) )
a_ : Union[str, Any] = resume_step // len(__A )
resume_step -= starting_epoch * len(__A )
# Now we train the model
for epoch in range(__A , __A ):
model.train()
if args.with_tracking:
a_ : List[str] = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
a_ : Union[str, Any] = accelerator.skip_first_batches(__A , __A )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
a_ : List[Any] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
a_ : str = {k: v.to(accelerator.device ) for k, v in batch.items()}
a_ : int = (batch['''image'''] - mean) / std
a_ : Any = model(__A )
a_ : List[Any] = torch.nn.functional.cross_entropy(__A , batch['''label'''] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__A )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__A , __A ):
a_ : int = f'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
a_ : Union[str, Any] = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
model.eval()
a_ : str = 0
a_ : List[Any] = 0
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
a_ : Optional[Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
a_ : List[Any] = (batch['''image'''] - mean) / std
with torch.no_grad():
a_ : Union[str, Any] = model(__A )
a_ : List[str] = outputs.argmax(dim=-1 )
a_ , a_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch['''label''']) )
a_ : Union[str, Any] = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
a_ : Any = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}: {1_00 * eval_metric:.2f}' )
if args.with_tracking:
accelerator.log(
{
'''accuracy''': 1_00 * eval_metric,
'''train_loss''': total_loss.item() / len(__A ),
'''epoch''': epoch,
} , step=__A , )
if checkpointing_steps == "epoch":
a_ : Dict = f'epoch_{epoch}'
if args.output_dir is not None:
a_ : int = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
if args.with_tracking:
accelerator.end_training()
def _UpperCAmelCase ( ):
a_ : Optional[int] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument('''--data_dir''' , required=__A , help='''The data folder on disk.''' )
parser.add_argument('''--fp16''' , action='''store_true''' , help='''If passed, will use FP16 training.''' )
parser.add_argument(
'''--mixed_precision''' , type=__A , default=__A , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--checkpointing_steps''' , type=__A , default=__A , help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' , )
parser.add_argument(
'''--output_dir''' , type=__A , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=__A , default=__A , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=__A , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
a_ : Union[str, Any] = parser.parse_args()
a_ : List[Any] = {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
training_function(__A , __A )
if __name__ == "__main__":
main()
| 466 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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 (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : str=13 , __SCREAMING_SNAKE_CASE : Optional[int]=7 , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : List[str]=99 , __SCREAMING_SNAKE_CASE : Optional[int]=32 , __SCREAMING_SNAKE_CASE : Tuple=2 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : int=37 , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , __SCREAMING_SNAKE_CASE : Tuple=512 , __SCREAMING_SNAKE_CASE : Optional[Any]=16 , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : List[Any]=4 , __SCREAMING_SNAKE_CASE : Dict=None , ) -> Tuple:
a_ : Any = parent
a_ : List[str] = 13
a_ : Dict = 7
a_ : List[str] = True
a_ : str = True
a_ : List[str] = True
a_ : Any = True
a_ : List[Any] = 99
a_ : Optional[Any] = 384
a_ : List[str] = 2
a_ : Optional[int] = 4
a_ : Optional[int] = 37
a_ : Union[str, Any] = '''gelu'''
a_ : List[str] = 0.1
a_ : str = 0.1
a_ : Optional[Any] = 512
a_ : Union[str, Any] = 16
a_ : Optional[Any] = 2
a_ : Optional[int] = 0.02
a_ : Union[str, Any] = 3
a_ : Union[str, Any] = 4
a_ : List[Any] = 128
a_ : Optional[Any] = 2
a_ : Any = 9
a_ : Optional[int] = 1
a_ : int = None
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
a_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ : int = None
if self.use_input_mask:
a_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
a_ : Optional[int] = None
if self.use_token_type_ids:
a_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a_ : Optional[Any] = None
a_ : Optional[Any] = None
a_ : Union[str, Any] = None
if self.use_labels:
a_ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a_ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
a_ : Union[str, Any] = ConvBertConfig(
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=__SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
a_ : Tuple = TFConvBertModel(config=__SCREAMING_SNAKE_CASE )
a_ : str = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
a_ : Optional[Any] = [input_ids, input_mask]
a_ : Tuple = model(__SCREAMING_SNAKE_CASE )
a_ : int = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict ) -> str:
a_ : List[Any] = TFConvBertForMaskedLM(config=__SCREAMING_SNAKE_CASE )
a_ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
a_ : str = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int ) -> Any:
a_ : Union[str, Any] = self.num_labels
a_ : List[str] = TFConvBertForSequenceClassification(config=__SCREAMING_SNAKE_CASE )
a_ : Optional[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
a_ : Tuple = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple ) -> Optional[int]:
a_ : Optional[Any] = self.num_choices
a_ : int = TFConvBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE )
a_ : List[str] = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
a_ : Tuple = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
a_ : List[Any] = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
a_ : Union[str, Any] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
a_ : Optional[int] = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple ) -> str:
a_ : Optional[Any] = self.num_labels
a_ : Optional[int] = TFConvBertForTokenClassification(config=__SCREAMING_SNAKE_CASE )
a_ : Tuple = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
a_ : Tuple = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
a_ : List[Any] = TFConvBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
a_ : Optional[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
a_ : Dict = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
a_ : List[Any] = self.prepare_config_and_inputs()
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) : List[Any] = config_and_inputs
a_ : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
snake_case__ = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = False
snake_case__ = False
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[Any]:
a_ : Optional[int] = TFConvBertModelTester(self )
a_ : Any = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
a_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
a_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
a_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
a_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
a_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
a_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
a_ , a_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
a_ : Any = True
a_ : Optional[int] = True
if hasattr(__SCREAMING_SNAKE_CASE , '''use_cache''' ):
a_ : Dict = True
a_ : Any = getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
a_ : List[Any] = getattr(self.model_tester , '''key_length''' , __SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
a_ : str = self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
a_ : Optional[int] = model_class(__SCREAMING_SNAKE_CASE )
a_ : Any = len(model(__SCREAMING_SNAKE_CASE ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__SCREAMING_SNAKE_CASE , saved_model=__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = os.path.join(__SCREAMING_SNAKE_CASE , '''saved_model''' , '''1''' )
a_ : Optional[int] = tf.keras.models.load_model(__SCREAMING_SNAKE_CASE )
a_ : Any = model(__SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
a_ : Optional[int] = outputs['''encoder_hidden_states''']
a_ : Dict = outputs['''encoder_attentions''']
else:
a_ : Union[str, Any] = outputs['''hidden_states''']
a_ : Tuple = outputs['''attentions''']
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
a_ : Tuple = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
a_ , a_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
a_ : Optional[Any] = True
a_ : Dict = getattr(self.model_tester , '''decoder_seq_length''' , self.model_tester.seq_length )
a_ : List[str] = getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
a_ : Dict = getattr(self.model_tester , '''key_length''' , __SCREAMING_SNAKE_CASE )
a_ : Any = getattr(self.model_tester , '''key_length''' , __SCREAMING_SNAKE_CASE )
def check_decoder_attentions_output(__SCREAMING_SNAKE_CASE : List[str] ):
a_ : int = len(__SCREAMING_SNAKE_CASE )
self.assertEqual(out_len % 2 , 0 )
a_ : Dict = outputs.decoder_attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(__SCREAMING_SNAKE_CASE : List[str] ):
a_ : Optional[int] = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
a_ : Tuple = True
a_ : Tuple = False
a_ : Optional[Any] = model_class(__SCREAMING_SNAKE_CASE )
a_ : List[Any] = model(self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
a_ : Union[str, Any] = len(__SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states , __SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(__SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
a_ : Any = model_class(__SCREAMING_SNAKE_CASE )
a_ : List[Any] = model(self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , __SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(__SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
a_ : Union[str, Any] = True
a_ : Union[str, Any] = model_class(__SCREAMING_SNAKE_CASE )
a_ : int = model(self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , __SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(__SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
a_ : Dict = True
a_ : str = True
a_ : int = model_class(__SCREAMING_SNAKE_CASE )
a_ : List[Any] = model(self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states , __SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(__SCREAMING_SNAKE_CASE )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : int ) -> Any:
a_ : int = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
a_ : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
a_ : Any = model(__SCREAMING_SNAKE_CASE )[0]
a_ : str = [1, 6, 768]
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE )
a_ : Dict = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 )
| 466 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class a ( __magic_name__ ):
def __init__( self : Union[str, Any], *SCREAMING_SNAKE_CASE_ : Tuple, **SCREAMING_SNAKE_CASE_ : Tuple ):
warnings.warn(
'''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use LayoutLMv2ImageProcessor instead.''', SCREAMING_SNAKE_CASE_, )
super().__init__(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
| 555 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase = {
"vocab_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
},
"merges_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
},
"tokenizer_file": {
"Salesforce/codegen-350M-mono": (
"https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase = {
"Salesforce/codegen-350M-mono": 2_048,
}
class a ( __magic_name__ ):
_snake_case = VOCAB_FILES_NAMES
_snake_case = PRETRAINED_VOCAB_FILES_MAP
_snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case = ['''input_ids''', '''attention_mask''']
_snake_case = CodeGenTokenizer
def __init__( self : Any, SCREAMING_SNAKE_CASE_ : Dict=None, SCREAMING_SNAKE_CASE_ : Optional[Any]=None, SCREAMING_SNAKE_CASE_ : Optional[Any]=None, SCREAMING_SNAKE_CASE_ : Optional[int]="<|endoftext|>", SCREAMING_SNAKE_CASE_ : List[Any]="<|endoftext|>", SCREAMING_SNAKE_CASE_ : List[str]="<|endoftext|>", SCREAMING_SNAKE_CASE_ : int=False, **SCREAMING_SNAKE_CASE_ : Optional[int], ):
super().__init__(
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, tokenizer_file=SCREAMING_SNAKE_CASE_, unk_token=SCREAMING_SNAKE_CASE_, bos_token=SCREAMING_SNAKE_CASE_, eos_token=SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
if kwargs.pop('''add_bos_token''', SCREAMING_SNAKE_CASE_ ):
snake_case : Dict = kwargs.pop('''name_or_path''', '''''' )
raise ValueError(
'''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
'''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
'''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
''' so that the fast tokenizer works correctly.''' )
snake_case : int = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''', SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
snake_case : Optional[Any] = getattr(SCREAMING_SNAKE_CASE_, pre_tok_state.pop('''type''' ) )
snake_case : Union[str, Any] = add_prefix_space
snake_case : Optional[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE_ )
snake_case : int = add_prefix_space
def __snake_case ( self : List[Any], *SCREAMING_SNAKE_CASE_ : Tuple, **SCREAMING_SNAKE_CASE_ : int ):
snake_case : Dict = kwargs.get('''is_split_into_words''', SCREAMING_SNAKE_CASE_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : List[str], *SCREAMING_SNAKE_CASE_ : str, **SCREAMING_SNAKE_CASE_ : Optional[int] ):
snake_case : Dict = kwargs.get('''is_split_into_words''', SCREAMING_SNAKE_CASE_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Tuple, SCREAMING_SNAKE_CASE_ : str, SCREAMING_SNAKE_CASE_ : Optional[str] = None ):
snake_case : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_, name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Dict, SCREAMING_SNAKE_CASE_ : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], SCREAMING_SNAKE_CASE_ : bool = False, SCREAMING_SNAKE_CASE_ : bool = None, SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None, **SCREAMING_SNAKE_CASE_ : Union[str, Any], ):
snake_case : Dict = super().decode(
token_ids=SCREAMING_SNAKE_CASE_, skip_special_tokens=SCREAMING_SNAKE_CASE_, clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
if truncate_before_pattern is not None and len(SCREAMING_SNAKE_CASE_ ) > 0:
snake_case : Optional[int] = self.truncate(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
return decoded_text
def __snake_case ( self : int, SCREAMING_SNAKE_CASE_ : Optional[Any], SCREAMING_SNAKE_CASE_ : Tuple ):
def find_re(SCREAMING_SNAKE_CASE_ : Optional[int], SCREAMING_SNAKE_CASE_ : Dict, SCREAMING_SNAKE_CASE_ : Optional[Any] ):
snake_case : Optional[Any] = pattern.search(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
return m.start() if m else -1
snake_case : Union[str, Any] = [re.compile(SCREAMING_SNAKE_CASE_, re.MULTILINE ) for pattern in truncate_before_pattern]
snake_case : Union[str, Any] = list(re.finditer('''^print''', SCREAMING_SNAKE_CASE_, re.MULTILINE ) )
if len(SCREAMING_SNAKE_CASE_ ) > 1:
snake_case : Tuple = completion[: prints[1].start()]
snake_case : List[str] = list(re.finditer('''^def''', SCREAMING_SNAKE_CASE_, re.MULTILINE ) )
if len(SCREAMING_SNAKE_CASE_ ) > 1:
snake_case : Optional[Any] = completion[: defs[1].start()]
snake_case : Tuple = 0
snake_case : List[Any] = [
pos for pos in [find_re(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) for terminal in terminals] if pos != -1
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
return completion[: min(SCREAMING_SNAKE_CASE_ )]
else:
return completion
| 555 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase__ = getLogger(__name__)
def _A( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple = 8 , UpperCamelCase__ : Union[str, Any] = 1024 , UpperCamelCase__ : List[Any]="val" , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : str="summarization" , UpperCamelCase__ : Any=None , UpperCamelCase__ : int=1 , UpperCamelCase__ : str = None , UpperCamelCase__ : Dict="" , **UpperCamelCase__ : Optional[int] , ) -> int:
'''simple docstring'''
__lowercase = str(UpperCamelCase__ )
assert local_rank is not None
torch.distributed.init_process_group(backend='''nccl''' , rank=UpperCamelCase__ )
__lowercase = Path(UpperCamelCase__ )
__lowercase = save_dir.joinpath(F'rank_{local_rank}_output.json' )
torch.cuda.set_device(UpperCamelCase__ )
__lowercase = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__ ).cuda()
if fpaa:
__lowercase = model.half()
# determine if we need to increase num_beams
use_task_specific_params(UpperCamelCase__ , UpperCamelCase__ ) # update config with task specific params
__lowercase = generate_kwargs.pop('''num_beams''' , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
__lowercase = num_return_sequences
__lowercase = AutoTokenizer.from_pretrained(UpperCamelCase__ )
logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' ) # if this is wrong, check config.model_type.
if max_source_length is None:
__lowercase = tokenizer.model_max_length
if prefix is None:
__lowercase = prefix or getattr(model.config , '''prefix''' , '''''' ) or """"""
__lowercase = SeqaSeqDataset(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , max_target_length=1024 , type_path=UpperCamelCase__ , n_obs=UpperCamelCase__ , prefix=UpperCamelCase__ , **UpperCamelCase__ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
__lowercase = ds.make_sortish_sampler(UpperCamelCase__ , distributed=UpperCamelCase__ , add_extra_examples=UpperCamelCase__ , shuffle=UpperCamelCase__ )
__lowercase = DataLoader(UpperCamelCase__ , sampler=UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=ds.collate_fn )
__lowercase = []
for batch in tqdm(UpperCamelCase__ ):
__lowercase = model.generate(
input_ids=batch['''input_ids'''].to(model.device ) , attention_mask=batch['''attention_mask'''].to(model.device ) , num_return_sequences=UpperCamelCase__ , num_beams=UpperCamelCase__ , **UpperCamelCase__ , )
__lowercase = tokenizer.batch_decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
__lowercase = batch["""ids"""]
if num_return_sequences > 1:
__lowercase = chunks(UpperCamelCase__ , UpperCamelCase__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(UpperCamelCase__ ):
results.append({'''pred''': pred, '''id''': ids[i].item()} )
save_json(UpperCamelCase__ , UpperCamelCase__ )
return results, sampler.num_replicas
def _A( ) -> Dict:
'''simple docstring'''
__lowercase = argparse.ArgumentParser(
epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate''' )
parser.add_argument('''--data_dir''' , type=UpperCamelCase__ , help='''like cnn_dm/test.source''' )
parser.add_argument(
'''--model_name''' , type=UpperCamelCase__ , help='''like facebook/bart-large-cnn,t5-base, etc.''' , default='''sshleifer/distilbart-xsum-12-3''' , )
parser.add_argument('''--save_dir''' , type=UpperCamelCase__ , help='''where to save''' , default='''tmp_gen''' )
parser.add_argument('''--max_source_length''' , type=UpperCamelCase__ , default=UpperCamelCase__ )
parser.add_argument(
'''--type_path''' , type=UpperCamelCase__ , default='''test''' , help='''which subset to evaluate typically train/val/test''' )
parser.add_argument('''--task''' , type=UpperCamelCase__ , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=UpperCamelCase__ , default=8 , required=UpperCamelCase__ , help='''batch size''' )
parser.add_argument(
'''--local_rank''' , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help='''should be passed by distributed.launch''' )
parser.add_argument(
'''--n_obs''' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , help='''How many observations. Defaults to all.''' )
parser.add_argument(
'''--num_return_sequences''' , type=UpperCamelCase__ , default=1 , required=UpperCamelCase__ , help='''How many sequences to return''' )
parser.add_argument(
'''--sync_timeout''' , type=UpperCamelCase__ , default=600 , required=UpperCamelCase__ , help='''How long should master process wait for other processes to finish.''' , )
parser.add_argument('''--src_lang''' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument('''--tgt_lang''' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument(
'''--prefix''' , type=UpperCamelCase__ , required=UpperCamelCase__ , default=UpperCamelCase__ , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--debug''' , action='''store_true''' )
__lowercase = time.time()
__lowercase = parser.parse_known_args()
__lowercase = parse_numeric_n_bool_cl_kwargs(UpperCamelCase__ )
if generate_kwargs and args.local_rank <= 0:
print(F'parsed the following generate kwargs: {generate_kwargs}' )
__lowercase = Path(args.save_dir + '''_tmp''' )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ ) # this handles locking.
__lowercase = list(json_save_dir.glob('''rank_*.json''' ) )
if intermediate_files:
raise ValueError(F'Found files at {json_save_dir} please move or remove them.' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
__lowercase = {}
if args.src_lang is not None:
__lowercase = args.src_lang
if args.tgt_lang is not None:
__lowercase = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=UpperCamelCase__ )
__lowercase = eval_data_dir(
args.data_dir , UpperCamelCase__ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=UpperCamelCase__ , **UpperCamelCase__ , )
if args.local_rank <= 0:
__lowercase = Path(args.save_dir )
save_dir.mkdir(exist_ok=UpperCamelCase__ )
__lowercase = gather_results_from_each_node(UpperCamelCase__ , UpperCamelCase__ , args.sync_timeout )
__lowercase = combine_partial_results(UpperCamelCase__ )
if args.num_return_sequences > 1:
__lowercase = save_dir.joinpath('''pseudolabel_results.json''' )
print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
save_json(UpperCamelCase__ , UpperCamelCase__ )
return
__lowercase = Path(args.data_dir ).joinpath(args.type_path + '''.target''' )
with open(UpperCamelCase__ ) as f:
__lowercase = [x.rstrip() for x in f.readlines()][: len(UpperCamelCase__ )]
# Calculate metrics, save metrics, and save _generations.txt
__lowercase = """translation""" in args.task
__lowercase = calculate_bleu if calc_bleu else calculate_rouge
__lowercase = """bleu""" if calc_bleu else """rouge"""
__lowercase = score_fn(UpperCamelCase__ , UpperCamelCase__ )
__lowercase = len(UpperCamelCase__ )
__lowercase = time.time() - start_time
__lowercase = round(runtime / metrics['''n_obs'''] , 4 )
__lowercase = num_replicas
# TODO(@stas00): add whatever metadata to metrics
__lowercase = save_dir.joinpath(F'{args.type_path}_{metric_name}.json' )
save_json(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ )
print(UpperCamelCase__ )
write_txt_file(UpperCamelCase__ , save_dir.joinpath(F'{args.type_path}_generations.txt' ) )
if args.debug:
write_txt_file(UpperCamelCase__ , save_dir.joinpath(F'{args.type_path}.target' ) )
else:
shutil.rmtree(UpperCamelCase__ )
def _A( UpperCamelCase__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__lowercase = []
for partial_result in partial_results:
records.extend(UpperCamelCase__ )
__lowercase = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x["id"] )
__lowercase = [x["""pred"""] for x in records]
return preds
def _A( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] ) -> Any:
'''simple docstring'''
__lowercase = time.time()
logger.info('''waiting for all nodes to finish''' )
__lowercase = None
while (time.time() - start_wait) < timeout:
__lowercase = list(save_dir.glob('''rank_*.json''' ) )
if len(UpperCamelCase__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
__lowercase = lmap(UpperCamelCase__ , UpperCamelCase__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError('''Rank 0 gave up on waiting for other processes''' )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 332 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] =ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
SCREAMING_SNAKE_CASE_: List[Any] =128
elif "12-12" in model_name:
SCREAMING_SNAKE_CASE_: Union[str, Any] =12
SCREAMING_SNAKE_CASE_: Optional[Any] =12
elif "14-14" in model_name:
SCREAMING_SNAKE_CASE_: List[Any] =14
SCREAMING_SNAKE_CASE_: Any =14
elif "16-16" in model_name:
SCREAMING_SNAKE_CASE_: Dict =16
SCREAMING_SNAKE_CASE_: Optional[int] =16
else:
raise ValueError("""Model not supported""" )
SCREAMING_SNAKE_CASE_: Optional[int] ="""huggingface/label-files"""
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE_: Tuple =35
SCREAMING_SNAKE_CASE_: Tuple ="""speech-commands-v2-id2label.json"""
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] =527
SCREAMING_SNAKE_CASE_: Optional[Any] ="""audioset-id2label.json"""
SCREAMING_SNAKE_CASE_: int =json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE_: Dict ={int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_: Union[str, Any] =idalabel
SCREAMING_SNAKE_CASE_: Optional[int] ={v: k for k, v in idalabel.items()}
return config
def __magic_name__ ( lowercase ):
if "module.v" in name:
SCREAMING_SNAKE_CASE_: Any =name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[Any] =name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
SCREAMING_SNAKE_CASE_: Any =name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE_: Dict =name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE_: Any =name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
SCREAMING_SNAKE_CASE_: Optional[int] =name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_: Optional[int] =name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE_: Union[str, Any] =name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE_: List[str] =name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE_: List[Any] =name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_: Any =name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_: List[str] =name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
SCREAMING_SNAKE_CASE_: Tuple =name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
SCREAMING_SNAKE_CASE_: List[str] =name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
SCREAMING_SNAKE_CASE_: List[str] =name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def __magic_name__ ( lowercase , lowercase ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_: Optional[Any] =orig_state_dict.pop(lowercase )
if "qkv" in key:
SCREAMING_SNAKE_CASE_: Any =key.split(""".""" )
SCREAMING_SNAKE_CASE_: Optional[int] =int(key_split[3] )
SCREAMING_SNAKE_CASE_: Any =config.hidden_size
if "weight" in key:
SCREAMING_SNAKE_CASE_: Optional[int] =val[:dim, :]
SCREAMING_SNAKE_CASE_: str =val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE_: int =val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_: Dict =val[:dim]
SCREAMING_SNAKE_CASE_: List[str] =val[dim : dim * 2]
SCREAMING_SNAKE_CASE_: List[Any] =val[-dim:]
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] =val
return orig_state_dict
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =[
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
@torch.no_grad()
def __magic_name__ ( lowercase , lowercase , lowercase=False ):
SCREAMING_SNAKE_CASE_: Union[str, Any] =get_audio_spectrogram_transformer_config(lowercase )
SCREAMING_SNAKE_CASE_: int ={
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
SCREAMING_SNAKE_CASE_: List[Any] =model_name_to_url[model_name]
SCREAMING_SNAKE_CASE_: str =torch.hub.load_state_dict_from_url(lowercase , map_location="""cpu""" )
# remove some keys
remove_keys(lowercase )
# rename some keys
SCREAMING_SNAKE_CASE_: Any =convert_state_dict(lowercase , lowercase )
# load 🤗 model
SCREAMING_SNAKE_CASE_: List[str] =ASTForAudioClassification(lowercase )
model.eval()
model.load_state_dict(lowercase )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
SCREAMING_SNAKE_CASE_: Optional[Any] =-4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
SCREAMING_SNAKE_CASE_: Dict =4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
SCREAMING_SNAKE_CASE_: Any =1024 if """speech-commands""" not in model_name else 128
SCREAMING_SNAKE_CASE_: Optional[Any] =ASTFeatureExtractor(mean=lowercase , std=lowercase , max_length=lowercase )
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE_: Dict =load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
SCREAMING_SNAKE_CASE_: Optional[int] =dataset[0]["""audio"""]["""array"""]
else:
SCREAMING_SNAKE_CASE_: Optional[Any] =hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =torchaudio.load(lowercase )
SCREAMING_SNAKE_CASE_: Any =waveform.squeeze().numpy()
SCREAMING_SNAKE_CASE_: Tuple =feature_extractor(lowercase , sampling_rate=1_6000 , return_tensors="""pt""" )
# forward pass
SCREAMING_SNAKE_CASE_: Tuple =model(**lowercase )
SCREAMING_SNAKE_CASE_: Optional[int] =outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
SCREAMING_SNAKE_CASE_: Optional[Any] =torch.tensor([-0.8_760, -7.0_042, -8.6_602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
SCREAMING_SNAKE_CASE_: Union[str, Any] =torch.tensor([-1.1_986, -7.0_903, -8.2_718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
SCREAMING_SNAKE_CASE_: List[Any] =torch.tensor([-2.6_128, -8.0_080, -9.4_344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
SCREAMING_SNAKE_CASE_: Any =torch.tensor([-1.5_080, -7.4_534, -8.8_917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
SCREAMING_SNAKE_CASE_: Tuple =torch.tensor([-0.5_050, -6.5_833, -8.0_843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
SCREAMING_SNAKE_CASE_: int =torch.tensor([-0.3_826, -7.0_336, -8.2_413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
SCREAMING_SNAKE_CASE_: List[str] =torch.tensor([-1.2_113, -6.9_101, -8.3_470] )
elif model_name == "ast-finetuned-speech-commands-v2":
SCREAMING_SNAKE_CASE_: Union[str, Any] =torch.tensor([6.1_589, -8.0_566, -8.7_984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , lowercase , atol=1e-4 ):
raise ValueError("""Logits don't match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_UpperCAmelCase = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 409 | 0 |
# flake8: noqa
# Lint as: python3
UpperCamelCase_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 142 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
a_ : Tuple = '''align_text_model'''
def __init__(self , UpperCAmelCase=3_0_5_2_2 , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=5_1_2 , UpperCAmelCase=2 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase=0 , UpperCAmelCase="absolute" , UpperCAmelCase=True , **UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase)
__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 =use_cache
__UpperCAmelCase =pad_token_id
@classmethod
def A__ (cls , UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
cls._set_token_in_kwargs(UpperCAmelCase)
__UpperCAmelCase , __UpperCAmelCase =cls.get_config_dict(UpperCAmelCase , **UpperCAmelCase)
# get the text config dict if we are loading from AlignConfig
if config_dict.get('''model_type''') == "align":
__UpperCAmelCase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''') and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")
return cls.from_dict(UpperCAmelCase , **UpperCAmelCase)
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
a_ : Dict = '''align_vision_model'''
def __init__(self , UpperCAmelCase = 3 , UpperCAmelCase = 6_0_0 , UpperCAmelCase = 2.0 , UpperCAmelCase = 3.1 , UpperCAmelCase = 8 , UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3] , UpperCAmelCase = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , UpperCAmelCase = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , UpperCAmelCase = [] , UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1] , UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1] , UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6] , UpperCAmelCase = 0.25 , UpperCAmelCase = "swish" , UpperCAmelCase = 2_5_6_0 , UpperCAmelCase = "mean" , UpperCAmelCase = 0.02 , UpperCAmelCase = 0.001 , UpperCAmelCase = 0.99 , UpperCAmelCase = 0.2 , **UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase)
__UpperCAmelCase =num_channels
__UpperCAmelCase =image_size
__UpperCAmelCase =width_coefficient
__UpperCAmelCase =depth_coefficient
__UpperCAmelCase =depth_divisor
__UpperCAmelCase =kernel_sizes
__UpperCAmelCase =in_channels
__UpperCAmelCase =out_channels
__UpperCAmelCase =depthwise_padding
__UpperCAmelCase =strides
__UpperCAmelCase =num_block_repeats
__UpperCAmelCase =expand_ratios
__UpperCAmelCase =squeeze_expansion_ratio
__UpperCAmelCase =hidden_act
__UpperCAmelCase =hidden_dim
__UpperCAmelCase =pooling_type
__UpperCAmelCase =initializer_range
__UpperCAmelCase =batch_norm_eps
__UpperCAmelCase =batch_norm_momentum
__UpperCAmelCase =drop_connect_rate
__UpperCAmelCase =sum(UpperCAmelCase) * 4
@classmethod
def A__ (cls , UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
cls._set_token_in_kwargs(UpperCAmelCase)
__UpperCAmelCase , __UpperCAmelCase =cls.get_config_dict(UpperCAmelCase , **UpperCAmelCase)
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('''model_type''') == "align":
__UpperCAmelCase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''') and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")
return cls.from_dict(UpperCAmelCase , **UpperCAmelCase)
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
a_ : int = '''align'''
a_ : Optional[Any] = True
def __init__(self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=6_4_0 , UpperCAmelCase=1.0 , UpperCAmelCase=0.02 , **UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase)
if text_config is None:
__UpperCAmelCase ={}
logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''')
if vision_config is None:
__UpperCAmelCase ={}
logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''')
__UpperCAmelCase =AlignTextConfig(**UpperCAmelCase)
__UpperCAmelCase =AlignVisionConfig(**UpperCAmelCase)
__UpperCAmelCase =projection_dim
__UpperCAmelCase =temperature_init_value
__UpperCAmelCase =initializer_range
@classmethod
def A__ (cls , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **UpperCAmelCase)
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =copy.deepcopy(self.__dict__)
__UpperCAmelCase =self.text_config.to_dict()
__UpperCAmelCase =self.vision_config.to_dict()
__UpperCAmelCase =self.__class__.model_type
return output
| 142 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}
class lowerCamelCase_ ( lowerCamelCase ):
a__ = '''luke'''
def __init__( self , __lowerCAmelCase=5_0_2_6_7 , __lowerCAmelCase=5_0_0_0_0_0 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=2_5_6 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=1 , __lowerCAmelCase=0 , __lowerCAmelCase=2 , **__lowerCAmelCase , ):
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
__magic_name__ :List[str] = vocab_size
__magic_name__ :int = entity_vocab_size
__magic_name__ :List[str] = hidden_size
__magic_name__ :Union[str, Any] = entity_emb_size
__magic_name__ :Tuple = num_hidden_layers
__magic_name__ :Dict = num_attention_heads
__magic_name__ :Optional[Any] = hidden_act
__magic_name__ :Tuple = intermediate_size
__magic_name__ :List[Any] = hidden_dropout_prob
__magic_name__ :List[Any] = attention_probs_dropout_prob
__magic_name__ :Dict = max_position_embeddings
__magic_name__ :Optional[Any] = type_vocab_size
__magic_name__ :int = initializer_range
__magic_name__ :str = layer_norm_eps
__magic_name__ :Union[str, Any] = use_entity_aware_attention
__magic_name__ :Tuple = classifier_dropout
| 0 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->int:
"""simple docstring"""
if not isinstance(_lowerCamelCase, _lowerCamelCase ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1, input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 575 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase : Optional[int] = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[Any] = [
"""LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongT5EncoderModel""",
"""LongT5ForConditionalGeneration""",
"""LongT5Model""",
"""LongT5PreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
"""FlaxLongT5ForConditionalGeneration""",
"""FlaxLongT5Model""",
"""FlaxLongT5PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
UpperCamelCase : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 715 | '''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase : int = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
UpperCamelCase : List[str] = """\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
UpperCamelCase : Tuple = """
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"precision\": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'precision@10': 1.0}
"""
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : Optional[Any] ) -> Dict:
"""simple docstring"""
return float((preds == labels).mean() )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : Union[str, Any] ) -> Tuple:
"""simple docstring"""
a : Optional[Any] = simple_accuracy(snake_case , snake_case )
a : Dict = float(fa_score(y_true=snake_case , y_pred=snake_case ) )
return {
"accuracy": acc,
"f1": fa,
}
def SCREAMING_SNAKE_CASE__ ( snake_case : Dict , snake_case : int ) -> Optional[int]:
"""simple docstring"""
a : Union[str, Any] = np.array(snake_case )
a : Any = np.array(snake_case )
a : Tuple = en_sentvecs.shape[0]
# mean centering
a : Tuple = en_sentvecs - np.mean(snake_case , axis=0 )
a : Optional[Any] = in_sentvecs - np.mean(snake_case , axis=0 )
a : Optional[int] = cdist(snake_case , snake_case , 'cosine' )
a : List[Any] = np.array(range(snake_case ) )
a : str = sim.argsort(axis=1 )[:, :10]
a : int = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self : str):
"""simple docstring"""
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
'references': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
}) , codebase_urls=[] , reference_urls=[] , format='numpy' if self.config_name != 'cvit-mkb-clsr' else None , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int):
"""simple docstring"""
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(UpperCAmelCase_ , UpperCAmelCase_)}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(UpperCAmelCase_ , UpperCAmelCase_)
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_)}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
| 610 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={'vocab_file': 'sentencepiece.bpe.model'}
UpperCamelCase__ ={
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
UpperCamelCase__ ={
'camembert-base': 512,
}
UpperCamelCase__ ='▁'
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 , __lowerCamelCase , __lowerCamelCase="<s>" , __lowerCamelCase="</s>" , __lowerCamelCase="</s>" , __lowerCamelCase="<s>" , __lowerCamelCase="<unk>" , __lowerCamelCase="<pad>" , __lowerCamelCase="<mask>" , __lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"] , __lowerCamelCase = None , **__lowerCamelCase , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
_SCREAMING_SNAKE_CASE : Union[str, Any] = {} 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 , )
_SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : str = 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>
_SCREAMING_SNAKE_CASE : Dict = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
_SCREAMING_SNAKE_CASE : Union[str, Any] = len(self.fairseq_tokens_to_ids )
_SCREAMING_SNAKE_CASE : Optional[Any] = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
_SCREAMING_SNAKE_CASE : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_SCREAMING_SNAKE_CASE : Optional[int] = [self.cls_token_id]
_SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None , __lowerCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> List[int]:
_SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
_SCREAMING_SNAKE_CASE : Dict = [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 UpperCamelCase_ ( self ) -> List[str]:
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE : Any = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self , __lowerCamelCase ) -> List[str]:
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Tuple:
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 UpperCamelCase_ ( self , __lowerCamelCase ) -> int:
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 UpperCamelCase_ ( self , __lowerCamelCase ) -> Any:
_SCREAMING_SNAKE_CASE : Any = []
_SCREAMING_SNAKE_CASE : List[str] = ""
_SCREAMING_SNAKE_CASE : Any = 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
_SCREAMING_SNAKE_CASE : Tuple = True
_SCREAMING_SNAKE_CASE : Union[str, Any] = []
else:
current_sub_tokens.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = False
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def __getstate__( self ) -> str:
_SCREAMING_SNAKE_CASE : str = self.__dict__.copy()
_SCREAMING_SNAKE_CASE : int = None
return state
def __setstate__( self , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE : Optional[int] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_SCREAMING_SNAKE_CASE : Optional[Any] = {}
_SCREAMING_SNAKE_CASE : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_SCREAMING_SNAKE_CASE : Optional[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:
_SCREAMING_SNAKE_CASE : Tuple = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,) | 249 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
UpperCamelCase__ =logging.get_logger(__name__)
@dataclass
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self , **__lowerCamelCase ) -> str:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
_SCREAMING_SNAKE_CASE : Optional[int] = deprecated_arg[3:]
setattr(self , __lowerCamelCase , not kwargs.pop(__lowerCamelCase ) )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
_SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("torchscript" , self.torchscript )
_SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop("torch_xla_tpu_print_metrics" , self.torch_xla_tpu_print_metrics )
_SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("fp16_opt_level" , self.fpaa_opt_level )
super().__init__(**__lowerCamelCase )
__snake_case = field(default=__lowercase , metadata={'help': 'Trace the models using torchscript'} )
__snake_case = field(default=__lowercase , metadata={'help': 'Print Xla/PyTorch tpu metrics'} )
__snake_case = field(
default='O1' , metadata={
'help': (
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
'See details at https://nvidia.github.io/apex/amp.html'
)
} , )
@cached_property
def UpperCamelCase_ ( self ) -> Tuple["torch.device", int]:
requires_backends(self , ["torch"] )
logger.info("PyTorch: setting up devices" )
if not self.cuda:
_SCREAMING_SNAKE_CASE : str = torch.device("cpu" )
_SCREAMING_SNAKE_CASE : Any = 0
elif is_torch_tpu_available():
_SCREAMING_SNAKE_CASE : Optional[int] = xm.xla_device()
_SCREAMING_SNAKE_CASE : List[str] = 0
else:
_SCREAMING_SNAKE_CASE : List[str] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.cuda.device_count()
return device, n_gpu
@property
def UpperCamelCase_ ( self ) -> Optional[Any]:
return is_torch_tpu_available() and self.tpu
@property
def UpperCamelCase_ ( self ) -> int:
requires_backends(self , ["torch"] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def UpperCamelCase_ ( self ) -> "torch.device":
requires_backends(self , ["torch"] )
return self._setup_devices[0]
@property
def UpperCamelCase_ ( self ) -> List[Any]:
requires_backends(self , ["torch"] )
return self._setup_devices[1]
@property
def UpperCamelCase_ ( self ) -> Union[str, Any]:
return self.n_gpu > 0 | 249 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _a ( unittest.TestCase ):
def _snake_case ( self ) -> int:
lowerCAmelCase : str = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
}
}
lowerCAmelCase : List[str] = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 128,
'''task_specific_params.summarization.min_length''': 12,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 142,
'''task_specific_params.summarization_cnn.min_length''': 56,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 62,
'''task_specific_params.summarization_xsum.min_length''': 11,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(UpperCamelCase__ ) , UpperCamelCase__ )
def _snake_case ( self ) -> str:
lowerCAmelCase : Tuple = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ ) , x.transpose() ) )
lowerCAmelCase : str = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _snake_case ( self ) -> Any:
lowerCAmelCase : Tuple = np.random.randn(3 , 4 )
lowerCAmelCase : str = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ ) , transpose(UpperCamelCase__ ).numpy() ) )
lowerCAmelCase : Optional[int] = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : Optional[int] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ , axes=(1, 2, 0) ) , transpose(UpperCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _snake_case ( self ) -> Any:
lowerCAmelCase : List[Any] = np.random.randn(3 , 4 )
lowerCAmelCase : Union[str, Any] = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ ) , transpose(UpperCamelCase__ ).numpy() ) )
lowerCAmelCase : Any = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : Dict = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ , axes=(1, 2, 0) ) , transpose(UpperCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase : List[str] = np.random.randn(3 , 4 )
lowerCAmelCase : List[str] = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ ) , np.asarray(transpose(UpperCamelCase__ ) ) ) )
lowerCAmelCase : Any = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : Any = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(transpose(UpperCamelCase__ , axes=(1, 2, 0) ) , np.asarray(transpose(UpperCamelCase__ , axes=(1, 2, 0) ) ) ) )
def _snake_case ( self ) -> Any:
lowerCAmelCase : Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (4, 3) ) , np.reshape(UpperCamelCase__ , (4, 3) ) ) )
lowerCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (12, 5) ) , np.reshape(UpperCamelCase__ , (12, 5) ) ) )
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase : Dict = np.random.randn(3 , 4 )
lowerCAmelCase : Union[str, Any] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (4, 3) ) , reshape(UpperCamelCase__ , (4, 3) ).numpy() ) )
lowerCAmelCase : int = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : Optional[int] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (12, 5) ) , reshape(UpperCamelCase__ , (12, 5) ).numpy() ) )
@require_tf
def _snake_case ( self ) -> Any:
lowerCAmelCase : Optional[int] = np.random.randn(3 , 4 )
lowerCAmelCase : Optional[int] = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (4, 3) ) , reshape(UpperCamelCase__ , (4, 3) ).numpy() ) )
lowerCAmelCase : Any = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : Optional[int] = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (12, 5) ) , reshape(UpperCamelCase__ , (12, 5) ).numpy() ) )
@require_flax
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase : Optional[int] = np.random.randn(3 , 4 )
lowerCAmelCase : Union[str, Any] = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (4, 3) ) , np.asarray(reshape(UpperCamelCase__ , (4, 3) ) ) ) )
lowerCAmelCase : int = np.random.randn(3 , 4 , 5 )
lowerCAmelCase : List[str] = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(reshape(UpperCamelCase__ , (12, 5) ) , np.asarray(reshape(UpperCamelCase__ , (12, 5) ) ) ) )
def _snake_case ( self ) -> str:
lowerCAmelCase : Dict = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ ) , np.squeeze(UpperCamelCase__ ) ) )
lowerCAmelCase : Dict = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ , axis=2 ) , np.squeeze(UpperCamelCase__ , axis=2 ) ) )
@require_torch
def _snake_case ( self ) -> int:
lowerCAmelCase : List[Any] = np.random.randn(1 , 3 , 4 )
lowerCAmelCase : List[str] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ ) , squeeze(UpperCamelCase__ ).numpy() ) )
lowerCAmelCase : Dict = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase : Optional[int] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ , axis=2 ) , squeeze(UpperCamelCase__ , axis=2 ).numpy() ) )
@require_tf
def _snake_case ( self ) -> int:
lowerCAmelCase : int = np.random.randn(1 , 3 , 4 )
lowerCAmelCase : Dict = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ ) , squeeze(UpperCamelCase__ ).numpy() ) )
lowerCAmelCase : List[Any] = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase : Optional[Any] = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ , axis=2 ) , squeeze(UpperCamelCase__ , axis=2 ).numpy() ) )
@require_flax
def _snake_case ( self ) -> str:
lowerCAmelCase : int = np.random.randn(1 , 3 , 4 )
lowerCAmelCase : str = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ ) , np.asarray(squeeze(UpperCamelCase__ ) ) ) )
lowerCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 )
lowerCAmelCase : Optional[int] = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(squeeze(UpperCamelCase__ , axis=2 ) , np.asarray(squeeze(UpperCamelCase__ , axis=2 ) ) ) )
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase : Optional[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(UpperCamelCase__ , axis=1 ) , np.expand_dims(UpperCamelCase__ , axis=1 ) ) )
@require_torch
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase : List[Any] = np.random.randn(3 , 4 )
lowerCAmelCase : Optional[Any] = torch.tensor(UpperCamelCase__ )
self.assertTrue(np.allclose(expand_dims(UpperCamelCase__ , axis=1 ) , expand_dims(UpperCamelCase__ , axis=1 ).numpy() ) )
@require_tf
def _snake_case ( self ) -> List[str]:
lowerCAmelCase : int = np.random.randn(3 , 4 )
lowerCAmelCase : str = tf.constant(UpperCamelCase__ )
self.assertTrue(np.allclose(expand_dims(UpperCamelCase__ , axis=1 ) , expand_dims(UpperCamelCase__ , axis=1 ).numpy() ) )
@require_flax
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase : Union[str, Any] = np.random.randn(3 , 4 )
lowerCAmelCase : Optional[int] = jnp.array(UpperCamelCase__ )
self.assertTrue(np.allclose(expand_dims(UpperCamelCase__ , axis=1 ) , np.asarray(expand_dims(UpperCamelCase__ , axis=1 ) ) ) )
| 712 |
import torch
from diffusers import DiffusionPipeline
class _a ( snake_case_ ):
def __init__( self , lowercase_ , lowercase_ ) -> int:
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
def __call__( self ) -> List[Any]:
lowerCAmelCase : Union[str, Any] = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
lowerCAmelCase : Union[str, Any] = 1
lowerCAmelCase : Dict = self.unet(lowercase_ , lowercase_ ).sample
lowerCAmelCase : str = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowerCAmelCase : Dict = scheduler_output - scheduler_output + torch.ones_like(lowercase_ )
return result
| 693 | 0 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : int = get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
__snake_case : Dict = json.load(f)
@require_torch
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple) -> Any:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: str) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = FSMTForConditionalGeneration.from_pretrained(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
])
@slow
def _SCREAMING_SNAKE_CASE ( self: Any , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: str) -> int:
"""simple docstring"""
__lowerCAmelCase : Any = F"""facebook/wmt19-{pair}"""
__lowerCAmelCase : List[str] = self.get_tokenizer(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = self.get_model(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = bleu_data[pair]["src"]
__lowerCAmelCase : Optional[int] = bleu_data[pair]["tgt"]
__lowerCAmelCase : List[Any] = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors="pt" , truncation=_SCREAMING_SNAKE_CASE , padding="longest").to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__lowerCAmelCase : str = tokenizer.batch_decode(
_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = calculate_bleu(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
print(_SCREAMING_SNAKE_CASE)
self.assertGreaterEqual(scores["bleu"] , _SCREAMING_SNAKE_CASE) | 293 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _lowercase ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) == 1
assert or_gate(1 ,1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1)) | 293 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
'uclanlp/visualbert-vqa-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
'uclanlp/visualbert-vcr-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class a ( __lowercase ):
"""simple docstring"""
__lowerCAmelCase = '''visual_bert'''
def __init__( self , snake_case_=3_0522 , snake_case_=768 , snake_case_=512 , 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_=0.0_2 , snake_case_=1e-1_2 , snake_case_=False , snake_case_=True , snake_case_=1 , snake_case_=0 , snake_case_=2 , **snake_case_ , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__UpperCAmelCase: List[Any] = vocab_size
__UpperCAmelCase: Tuple = max_position_embeddings
__UpperCAmelCase: Optional[int] = hidden_size
__UpperCAmelCase: Optional[int] = visual_embedding_dim
__UpperCAmelCase: Any = num_hidden_layers
__UpperCAmelCase: Optional[int] = num_attention_heads
__UpperCAmelCase: Dict = intermediate_size
__UpperCAmelCase: Dict = hidden_act
__UpperCAmelCase: Dict = hidden_dropout_prob
__UpperCAmelCase: Optional[int] = attention_probs_dropout_prob
__UpperCAmelCase: Dict = initializer_range
__UpperCAmelCase: Optional[Any] = type_vocab_size
__UpperCAmelCase: str = layer_norm_eps
__UpperCAmelCase: List[Any] = bypass_transformer
__UpperCAmelCase: int = special_visual_initialize | 720 | '''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
"""simple docstring"""
def __init__( self , snake_case_ = None ):
'''simple docstring'''
if components is None:
__UpperCAmelCase: List[Any] = []
__UpperCAmelCase: Tuple = list(snake_case_ )
def __len__( self ):
'''simple docstring'''
return len(self.__components )
def __str__( self ):
'''simple docstring'''
return "(" + ",".join(map(snake_case_ , self.__components ) ) + ")"
def __add__( self , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: int = len(self )
if size == len(snake_case_ ):
__UpperCAmelCase: str = [self.__components[i] + other.component(snake_case_ ) for i in range(snake_case_ )]
return Vector(snake_case_ )
else:
raise Exception("""must have the same size""" )
def __sub__( self , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: Dict = len(self )
if size == len(snake_case_ ):
__UpperCAmelCase: Dict = [self.__components[i] - other.component(snake_case_ ) for i in range(snake_case_ )]
return Vector(snake_case_ )
else: # error case
raise Exception("""must have the same size""" )
@overload
def __mul__( self , snake_case_ ):
'''simple docstring'''
...
@overload
def __mul__( self , snake_case_ ):
'''simple docstring'''
...
def __mul__( self , snake_case_ ):
'''simple docstring'''
if isinstance(snake_case_ , (float, int) ):
__UpperCAmelCase: str = [c * other for c in self.__components]
return Vector(snake_case_ )
elif isinstance(snake_case_ , snake_case_ ) and len(self ) == len(snake_case_ ):
__UpperCAmelCase: Dict = len(self )
__UpperCAmelCase: List[str] = [self.__components[i] * other.component(snake_case_ ) for i in range(snake_case_ )]
return sum(snake_case_ )
else: # error case
raise Exception("""invalid operand!""" )
def lowercase_ ( self ):
'''simple docstring'''
return Vector(self.__components )
def lowercase_ ( self , snake_case_ ):
'''simple docstring'''
if isinstance(snake_case_ , snake_case_ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("""index out of range""" )
def lowercase_ ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
assert -len(self.__components ) <= pos < len(self.__components )
__UpperCAmelCase: List[str] = value
def lowercase_ ( self ):
'''simple docstring'''
if len(self.__components ) == 0:
raise Exception("""Vector is empty""" )
__UpperCAmelCase: Optional[Any] = [c**2 for c in self.__components]
return math.sqrt(sum(snake_case_ ) )
def lowercase_ ( self , snake_case_ , snake_case_ = False ):
'''simple docstring'''
__UpperCAmelCase: List[Any] = self * other
__UpperCAmelCase: Any = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def UpperCamelCase__ ( _lowercase : int ) -> Vector:
assert isinstance(_lowercase , _lowercase )
return Vector([0] * dimension )
def UpperCamelCase__ ( _lowercase : int , _lowercase : int ) -> Vector:
assert isinstance(_lowercase , _lowercase ) and (isinstance(_lowercase , _lowercase ))
__UpperCAmelCase: int = [0] * dimension
__UpperCAmelCase: int = 1
return Vector(_lowercase )
def UpperCamelCase__ ( _lowercase : float , _lowercase : Vector , _lowercase : Vector ) -> Vector:
assert (
isinstance(_lowercase , _lowercase )
and isinstance(_lowercase , _lowercase )
and (isinstance(_lowercase , (int, float) ))
)
return x * scalar + y
def UpperCamelCase__ ( _lowercase : int , _lowercase : int , _lowercase : int ) -> Vector:
random.seed(_lowercase )
__UpperCAmelCase: str = [random.randint(_lowercase , _lowercase ) for _ in range(_lowercase )]
return Vector(_lowercase )
class a :
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: List[str] = matrix
__UpperCAmelCase: Dict = w
__UpperCAmelCase: Tuple = h
def __str__( self ):
'''simple docstring'''
__UpperCAmelCase: Optional[int] = """"""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , snake_case_ ):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
__UpperCAmelCase: List[str] = []
for i in range(self.__height ):
__UpperCAmelCase: List[Any] = [
self.__matrix[i][j] + other.component(snake_case_ , snake_case_ )
for j in range(self.__width )
]
matrix.append(snake_case_ )
return Matrix(snake_case_ , self.__width , self.__height )
else:
raise Exception("""matrix must have the same dimension!""" )
def __sub__( self , snake_case_ ):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
__UpperCAmelCase: int = []
for i in range(self.__height ):
__UpperCAmelCase: Any = [
self.__matrix[i][j] - other.component(snake_case_ , snake_case_ )
for j in range(self.__width )
]
matrix.append(snake_case_ )
return Matrix(snake_case_ , self.__width , self.__height )
else:
raise Exception("""matrices must have the same dimension!""" )
@overload
def __mul__( self , snake_case_ ):
'''simple docstring'''
...
@overload
def __mul__( self , snake_case_ ):
'''simple docstring'''
...
def __mul__( self , snake_case_ ):
'''simple docstring'''
if isinstance(snake_case_ , snake_case_ ): # matrix-vector
if len(snake_case_ ) == self.__width:
__UpperCAmelCase: Optional[Any] = zero_vector(self.__height )
for i in range(self.__height ):
__UpperCAmelCase: Dict = [
self.__matrix[i][j] * other.component(snake_case_ )
for j in range(self.__width )
]
ans.change_component(snake_case_ , sum(snake_case_ ) )
return ans
else:
raise Exception(
"""vector must have the same size as the """
"""number of columns of the matrix!""" )
elif isinstance(snake_case_ , (int, float) ): # matrix-scalar
__UpperCAmelCase: Dict = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(snake_case_ , self.__width , self.__height )
return None
def lowercase_ ( self ):
'''simple docstring'''
return self.__height
def lowercase_ ( self ):
'''simple docstring'''
return self.__width
def lowercase_ ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("""change_component: indices out of bounds""" )
def lowercase_ ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
__UpperCAmelCase: int = value
else:
raise Exception("""change_component: indices out of bounds""" )
def lowercase_ ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
__UpperCAmelCase: int = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(snake_case_ ) ):
__UpperCAmelCase: Optional[Any] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(snake_case_ , self.__width - 1 , self.__height - 1 ).determinant()
def lowercase_ ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(snake_case_ , snake_case_ )
else:
raise Exception("""Indices out of bounds""" )
def lowercase_ ( self ):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""" )
if self.__height < 1:
raise Exception("""Matrix has no element""" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__UpperCAmelCase: Any = [
self.__matrix[0][y] * self.cofactor(0 , snake_case_ ) for y in range(self.__width )
]
return sum(snake_case_ )
def UpperCamelCase__ ( _lowercase : int ) -> Matrix:
__UpperCAmelCase: list[list[float]] = [[0] * n for _ in range(_lowercase )]
return Matrix(_lowercase , _lowercase , _lowercase )
def UpperCamelCase__ ( _lowercase : int , _lowercase : int , _lowercase : int , _lowercase : int ) -> Matrix:
random.seed(_lowercase )
__UpperCAmelCase: list[list[float]] = [
[random.randint(_lowercase , _lowercase ) for _ in range(_lowercase )] for _ in range(_lowercase )
]
return Matrix(_lowercase , _lowercase , _lowercase ) | 466 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = BloomTokenizerFast
SCREAMING_SNAKE_CASE = BloomTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = 'tokenizer_file'
SCREAMING_SNAKE_CASE = {'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}
def _a (self ):
"""simple docstring"""
super().setUp()
UpperCAmelCase__ : Tuple = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def _a (self , **_lowerCamelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : int = self.get_rust_tokenizer()
UpperCAmelCase__ : List[str] = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
UpperCAmelCase__ : Union[str, Any] = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
UpperCAmelCase__ : List[Any] = tokenizer.batch_encode_plus(UpperCamelCase_ )["input_ids"]
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase__ : str = tokenizer.batch_decode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def _a (self , _lowerCamelCase=6 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
UpperCAmelCase__ : Any = "This is a simple input"
UpperCAmelCase__ : str = ["This is a simple input 1", "This is a simple input 2"]
UpperCAmelCase__ : Union[str, Any] = ("This is a simple input", "This is a pair")
UpperCAmelCase__ : Union[str, Any] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(UpperCamelCase_ , max_length=UpperCamelCase_ )
tokenizer_r.encode_plus(UpperCamelCase_ , max_length=UpperCamelCase_ )
tokenizer_r.batch_encode_plus(UpperCamelCase_ , max_length=UpperCamelCase_ )
tokenizer_r.encode(UpperCamelCase_ , max_length=UpperCamelCase_ )
tokenizer_r.batch_encode_plus(UpperCamelCase_ , max_length=UpperCamelCase_ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
UpperCAmelCase__ : Tuple = None # Hotfixing padding = None
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(
UpperCamelCase_ , tokenizer_r.batch_encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" , )
# Pair input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(
UpperCamelCase_ , tokenizer_r.batch_encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding="""max_length""" , )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : int = self.get_rust_tokenizer()
UpperCAmelCase__ : Union[str, Any] = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=UpperCamelCase_ )
UpperCAmelCase__ : Any = next(iter(UpperCamelCase_ ) )["premise"] # pick up one data
UpperCAmelCase__ : Optional[int] = list(sample_data.values() )
UpperCAmelCase__ : str = list(map(tokenizer.encode , UpperCamelCase_ ) )
UpperCAmelCase__ : Dict = [tokenizer.decode(UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ ) for x in output_tokens]
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def _a (self ):
"""simple docstring"""
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 182 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 0 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : int =logging.get_logger(__name__)
UpperCAmelCase__ : int ='''▁'''
UpperCAmelCase__ : Dict ={'''vocab_file''': '''prophetnet.tokenizer'''}
UpperCAmelCase__ : int ={
'''vocab_file''': {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
),
}
}
UpperCAmelCase__ : str ={
'''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}
UpperCAmelCase__ : List[str] ={
'''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}
def _lowercase ( _UpperCAmelCase ) -> List[str]:
lowerCamelCase =collections.OrderedDict()
with open(_UpperCAmelCase , """r""" , encoding="""utf-8""" ) as reader:
lowerCamelCase =reader.readlines()
for index, token in enumerate(_UpperCAmelCase ):
lowerCamelCase =token.rstrip("""\n""" )
lowerCamelCase =index
return vocab
class __A ( a ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_="[SEP]" , UpperCAmelCase_="[SEP]" , UpperCAmelCase_="[SEP]" , UpperCAmelCase_="[UNK]" , UpperCAmelCase_="[PAD]" , UpperCAmelCase_="[CLS]" , UpperCAmelCase_="[MASK]" , UpperCAmelCase_ = None , **UpperCAmelCase_ , ):
lowerCamelCase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCAmelCase_ , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
lowerCamelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCAmelCase_ ) )
lowerCamelCase =vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
lowerCamelCase ={"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}
for i in range(10 ):
lowerCamelCase =f"""[unused{i}]"""
lowerCamelCase =5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
lowerCamelCase =12
lowerCamelCase ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(UpperCAmelCase_ )
def __getstate__( self ):
lowerCamelCase =self.__dict__.copy()
lowerCamelCase =None
return state
def __setstate__( self , UpperCAmelCase_ ):
lowerCamelCase =d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCamelCase ={}
lowerCamelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
if token_ids_a is None:
return ([0] * len(UpperCAmelCase_ )) + [1]
return ([0] * len(UpperCAmelCase_ )) + [1] + ([0] * len(UpperCAmelCase_ )) + [1]
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
lowerCamelCase =[self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _snake_case ( self ):
return len(self.sp_model ) + self.fairseq_offset
def _snake_case ( self ):
lowerCamelCase ={self.convert_ids_to_tokens(UpperCAmelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _snake_case ( self , UpperCAmelCase_ ):
return self.sp_model.encode(UpperCAmelCase_ , out_type=UpperCAmelCase_ )
def _snake_case ( self , UpperCAmelCase_ ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowerCamelCase =self.sp_model.PieceToId(UpperCAmelCase_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _snake_case ( self , UpperCAmelCase_ ):
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 _snake_case ( self , UpperCAmelCase_ ):
lowerCamelCase ="""""".join(UpperCAmelCase_ ).replace(UpperCAmelCase_ , """ """ ).strip()
return out_string
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase =os.path.join(
UpperCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCAmelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCAmelCase_ , """wb""" ) as fi:
lowerCamelCase =self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase_ )
return (out_vocab_file,)
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
lowerCamelCase =[self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 719 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_=13 , UpperCAmelCase_=32 , UpperCAmelCase_=2 , UpperCAmelCase_=3 , UpperCAmelCase_=16 , UpperCAmelCase_=[32, 64, 128] , UpperCAmelCase_=[1, 2, 1] , UpperCAmelCase_=[2, 2, 4] , UpperCAmelCase_=2 , UpperCAmelCase_=2.0 , UpperCAmelCase_=True , UpperCAmelCase_=0.0 , UpperCAmelCase_=0.0 , UpperCAmelCase_=0.1 , UpperCAmelCase_="gelu" , UpperCAmelCase_=False , UpperCAmelCase_=True , UpperCAmelCase_=0.0_2 , UpperCAmelCase_=1E-5 , UpperCAmelCase_=True , UpperCAmelCase_=None , UpperCAmelCase_=True , UpperCAmelCase_=10 , UpperCAmelCase_=8 , UpperCAmelCase_=["stage1", "stage2"] , UpperCAmelCase_=[1, 2] , ):
lowerCamelCase =parent
lowerCamelCase =batch_size
lowerCamelCase =image_size
lowerCamelCase =patch_size
lowerCamelCase =num_channels
lowerCamelCase =embed_dim
lowerCamelCase =hidden_sizes
lowerCamelCase =depths
lowerCamelCase =num_heads
lowerCamelCase =window_size
lowerCamelCase =mlp_ratio
lowerCamelCase =qkv_bias
lowerCamelCase =hidden_dropout_prob
lowerCamelCase =attention_probs_dropout_prob
lowerCamelCase =drop_path_rate
lowerCamelCase =hidden_act
lowerCamelCase =use_absolute_embeddings
lowerCamelCase =patch_norm
lowerCamelCase =layer_norm_eps
lowerCamelCase =initializer_range
lowerCamelCase =is_training
lowerCamelCase =scope
lowerCamelCase =use_labels
lowerCamelCase =type_sequence_label_size
lowerCamelCase =encoder_stride
lowerCamelCase =out_features
lowerCamelCase =out_indices
def _snake_case ( self ):
lowerCamelCase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase =None
if self.use_labels:
lowerCamelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase =self.get_config()
return config, pixel_values, labels
def _snake_case ( self ):
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCamelCase =FocalNetModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =model(UpperCAmelCase_ )
lowerCamelCase =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowerCamelCase =int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCamelCase =FocalNetBackbone(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =model(UpperCAmelCase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
lowerCamelCase =None
lowerCamelCase =FocalNetBackbone(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =model(UpperCAmelCase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCamelCase =FocalNetForMaskedImageModeling(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =model(UpperCAmelCase_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCamelCase =1
lowerCamelCase =FocalNetForMaskedImageModeling(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase =model(UpperCAmelCase_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCamelCase =self.type_sequence_label_size
lowerCamelCase =FocalNetForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase =1
lowerCamelCase =FocalNetForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
lowerCamelCase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase =model(UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _snake_case ( self ):
lowerCamelCase =self.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase =config_and_inputs
lowerCamelCase ={"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __A ( a , a , unittest.TestCase ):
__A = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
__A = (
{"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def _snake_case ( self ):
lowerCamelCase =FocalNetModelTester(self )
lowerCamelCase =ConfigTester(self , config_class=UpperCAmelCase_ , embed_dim=37 , has_text_modality=UpperCAmelCase_ )
def _snake_case ( self ):
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 _snake_case ( self ):
return
def _snake_case ( self ):
lowerCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def _snake_case ( self ):
lowerCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCAmelCase_ )
def _snake_case ( self ):
lowerCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_ )
def _snake_case ( self ):
lowerCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@unittest.skip(reason="""FocalNet does not use inputs_embeds""" )
def _snake_case ( self ):
pass
@unittest.skip(reason="""FocalNet does not use feedforward chunking""" )
def _snake_case ( self ):
pass
def _snake_case ( self ):
lowerCamelCase , lowerCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
lowerCamelCase =model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCamelCase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ , nn.Linear ) )
def _snake_case ( self ):
lowerCamelCase , lowerCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
lowerCamelCase =model_class(UpperCAmelCase_ )
lowerCamelCase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase =[*signature.parameters.keys()]
lowerCamelCase =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_ )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCamelCase =model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
lowerCamelCase =model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
lowerCamelCase =outputs.hidden_states
lowerCamelCase =getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
# FocalNet has a different seq_length
lowerCamelCase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCamelCase =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
lowerCamelCase =outputs.reshaped_hidden_states
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase =reshaped_hidden_states[0].shape
lowerCamelCase =(
reshaped_hidden_states[0].view(UpperCAmelCase_ , UpperCAmelCase_ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _snake_case ( self ):
lowerCamelCase , lowerCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase =(
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
lowerCamelCase =True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase =True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def _snake_case ( self ):
lowerCamelCase , lowerCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase =3
lowerCamelCase =(
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowerCamelCase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCamelCase =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowerCamelCase =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
lowerCamelCase =True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase =True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , (padded_height, padded_width) )
@slow
def _snake_case ( self ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase =FocalNetModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def _snake_case ( self ):
lowerCamelCase , lowerCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase =_config_zero_init(UpperCAmelCase_ )
for model_class in self.all_model_classes:
lowerCamelCase =model_class(config=UpperCAmelCase_ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class __A ( unittest.TestCase ):
@cached_property
def _snake_case ( self ):
# TODO update organization
return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny""" ) if is_vision_available() else None
@slow
def _snake_case ( self ):
lowerCamelCase =FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny""" ).to(UpperCAmelCase_ )
lowerCamelCase =self.default_image_processor
lowerCamelCase =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase =image_processor(images=UpperCAmelCase_ , return_tensors="""pt""" ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
lowerCamelCase =model(**UpperCAmelCase_ )
# verify the logits
lowerCamelCase =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
lowerCamelCase =torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 281 )
@require_torch
class __A ( a , unittest.TestCase ):
__A = (FocalNetBackbone,) if is_torch_available() else ()
__A = FocalNetConfig
__A = False
def _snake_case ( self ):
lowerCamelCase =FocalNetModelTester(self )
| 269 | 0 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def SCREAMING_SNAKE_CASE__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ) == 0
assert xnor_gate(1, 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 387 | """simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _A :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=13 , __lowerCAmelCase=32 , __lowerCAmelCase=3 , __lowerCAmelCase=4 , __lowerCAmelCase=[10, 20, 30, 40] , __lowerCAmelCase=[2, 2, 3, 2] , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=37 , __lowerCAmelCase="gelu" , __lowerCAmelCase=10 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=["stage2", "stage3", "stage4"] , __lowerCAmelCase=[2, 3, 4] , __lowerCAmelCase=None , ):
"""simple docstring"""
lowercase = parent
lowercase = batch_size
lowercase = image_size
lowercase = num_channels
lowercase = num_stages
lowercase = hidden_sizes
lowercase = depths
lowercase = is_training
lowercase = use_labels
lowercase = intermediate_size
lowercase = hidden_act
lowercase = num_labels
lowercase = initializer_range
lowercase = out_features
lowercase = out_indices
lowercase = scope
def A__ ( self ):
"""simple docstring"""
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels
def A__ ( self ):
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
lowercase = ConvNextModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase = model(__lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
lowercase = ConvNextForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
lowercase = ConvNextBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase = model(__lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase = None
lowercase = ConvNextBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A__ ( self ):
"""simple docstring"""
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase = config_and_inputs
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
snake_case__ : List[str] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
snake_case__ : Optional[Any] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
snake_case__ : List[str] = True
snake_case__ : Dict = False
snake_case__ : Optional[int] = False
snake_case__ : Tuple = False
snake_case__ : List[str] = False
def A__ ( self ):
"""simple docstring"""
lowercase = ConvNextModelTester(self )
lowercase = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A__ ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self ):
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def A__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def A__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def A__ ( self ):
"""simple docstring"""
pass
def A__ ( self ):
"""simple docstring"""
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(__lowerCAmelCase )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowercase = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A__ ( self ):
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = ConvNextModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ) -> List[str]:
'''simple docstring'''
lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _A ( unittest.TestCase ):
@cached_property
def A__ ( self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def A__ ( self ):
"""simple docstring"""
lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(__lowerCAmelCase )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase = model(**__lowerCAmelCase )
# verify the logits
lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
lowercase = torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
@require_torch
class _A ( unittest.TestCase , lowerCAmelCase ):
snake_case__ : int = (ConvNextBackbone,) if is_torch_available() else ()
snake_case__ : Tuple = ConvNextConfig
snake_case__ : Dict = False
def A__ ( self ):
"""simple docstring"""
lowercase = ConvNextModelTester(self )
| 359 | 0 |
from collections.abc import Sequence
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase ):
return sum(c * (x**i) for i, c in enumerate(_lowerCamelCase ) )
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase ):
A : Optional[Any] = 0.0
for coeff in reversed(_lowerCamelCase ):
A : List[Any] = result * x + coeff
return result
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = (0.0, 0.0, 5.0, 9.3, 7.0)
__SCREAMING_SNAKE_CASE = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x)) | 17 |
from math import sqrt
def UpperCAmelCase ( _lowerCamelCase = 100_0000 ):
A : int = 0
A : int = 0
A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(_lowerCamelCase , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"""{solution() = }""") | 17 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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 BertGenerationDecoder, BertGenerationEncoder
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , A=1_3 , A=7 , A=True , A=True , A=9_9 , A=3_2 , A=5 , A=4 , A=3_7 , A="gelu" , A=0.1 , A=0.1 , A=5_0 , A=0.02 , A=True , A=None , ) -> Tuple:
_UpperCAmelCase : Any = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : Tuple = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : List[str] = use_input_mask
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : List[str] = num_hidden_layers
_UpperCAmelCase : int = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Optional[int] = hidden_act
_UpperCAmelCase : Tuple = hidden_dropout_prob
_UpperCAmelCase : int = attention_probs_dropout_prob
_UpperCAmelCase : Dict = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Tuple = use_labels
_UpperCAmelCase : Union[str, Any] = scope
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : int = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : str = self.get_config()
return config, input_ids, input_mask, token_labels
def __lowerCAmelCase ( self ) -> str:
return BertGenerationConfig(
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 , is_decoder=A , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ) -> str:
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : Optional[Any] = self.prepare_config_and_inputs()
_UpperCAmelCase : int = True
_UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , A , A , A , A , **A , ) -> Any:
_UpperCAmelCase : int = BertGenerationEncoder(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : Union[str, Any] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A , **A , ) -> Optional[Any]:
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Union[str, Any] = BertGenerationEncoder(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Dict = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_UpperCAmelCase : int = model(
A , attention_mask=A , encoder_hidden_states=A , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A , **A , ) -> Dict:
_UpperCAmelCase : Any = True
_UpperCAmelCase : int = True
_UpperCAmelCase : Union[str, Any] = BertGenerationDecoder(config=A ).to(A ).eval()
# first forward pass
_UpperCAmelCase : Tuple = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_UpperCAmelCase : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Optional[int] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_UpperCAmelCase : Dict = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat([input_mask, next_mask] , dim=-1 )
_UpperCAmelCase : Any = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_UpperCAmelCase : Optional[int] = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_UpperCAmelCase : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def __lowerCAmelCase ( self , A , A , A , A , *A , ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = BertGenerationDecoder(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self ) -> str:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = self.prepare_config_and_inputs()
_UpperCAmelCase : int = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _a ,_a ,_a ,unittest.TestCase ):
'''simple docstring'''
a__ =(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
a__ =(BertGenerationDecoder,) if is_torch_available() else ()
a__ =(
{'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
if is_torch_available()
else {}
)
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = BertGenerationEncoderTester(self )
_UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , hidden_size=3_7 )
def __lowerCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase : Union[str, Any] = '''bert'''
self.model_tester.create_and_check_model(A , A , A , A )
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*A )
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*A )
def __lowerCAmelCase ( self ) -> Dict:
# This regression test was failing with PyTorch < 1.3
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
_UpperCAmelCase : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(
A , A , A , A , A , A , )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*A )
@slow
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : Tuple = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
self.assertIsNotNone(A )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Any = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
_UpperCAmelCase : List[str] = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
_UpperCAmelCase : Optional[int] = model(A )[0]
_UpperCAmelCase : Tuple = torch.Size([1, 8, 1_0_2_4] )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : List[Any] = BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
_UpperCAmelCase : Dict = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
_UpperCAmelCase : Tuple = model(A )[0]
_UpperCAmelCase : int = torch.Size([1, 8, 5_0_3_5_8] )
self.assertEqual(output.shape , A )
_UpperCAmelCase : str = torch.tensor(
[[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
| 506 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , lowerCAmelCase : int = 16 , lowerCAmelCase : int = 88 , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : int = 1 , lowerCAmelCase : float = 0.0 , lowerCAmelCase : int = 32 , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : str = "geglu" , lowerCAmelCase : Optional[int] = None , ):
super().__init__()
lowerCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=lowerCAmelCase , attention_head_dim=lowerCAmelCase , in_channels=lowerCAmelCase , num_layers=lowerCAmelCase , dropout=lowerCAmelCase , norm_num_groups=lowerCAmelCase , cross_attention_dim=lowerCAmelCase , attention_bias=lowerCAmelCase , sample_size=lowerCAmelCase , num_vector_embeds=lowerCAmelCase , activation_fn=lowerCAmelCase , num_embeds_ada_norm=lowerCAmelCase , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
lowerCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
lowerCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
lowerCAmelCase = [1, 0]
def __lowercase ( self : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : Optional[int] , lowerCAmelCase : int=None , lowerCAmelCase : List[str]=None , lowerCAmelCase : Any=None , lowerCAmelCase : bool = True , ):
lowerCAmelCase = hidden_states
lowerCAmelCase = []
lowerCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
lowerCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
lowerCAmelCase = self.transformer_index_for_condition[i]
lowerCAmelCase = self.transformers[transformer_index](
lowerCAmelCase , encoder_hidden_states=lowerCAmelCase , timestep=lowerCAmelCase , cross_attention_kwargs=lowerCAmelCase , return_dict=lowerCAmelCase , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
lowerCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
lowerCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=lowerCAmelCase )
| 169 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Dict ={
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class __A ( UpperCamelCase__ ):
a__ : int = """convbert"""
def __init__(self : int , __a : Dict=30522 , __a : str=768 , __a : List[Any]=12 , __a : Optional[int]=12 , __a : Dict=3072 , __a : int="gelu" , __a : int=0.1 , __a : Dict=0.1 , __a : Tuple=512 , __a : Union[str, Any]=2 , __a : Any=0.02 , __a : List[str]=1E-12 , __a : str=1 , __a : List[Any]=0 , __a : Union[str, Any]=2 , __a : Union[str, Any]=768 , __a : int=2 , __a : int=9 , __a : Union[str, Any]=1 , __a : Dict=None , **__a : Dict , ):
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a , )
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_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = embedding_size
UpperCAmelCase_ = head_ratio
UpperCAmelCase_ = conv_kernel_size
UpperCAmelCase_ = num_groups
UpperCAmelCase_ = classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def _lowercase (self : List[Any] ):
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),
("token_type_ids", dynamic_axis),
] )
| 708 | '''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Optional[Any] ={
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class __A ( UpperCamelCase__ ):
a__ : Tuple = """deta"""
a__ : Dict = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__(self : List[str] , __a : Optional[int]=None , __a : List[str]=900 , __a : Optional[Any]=2048 , __a : Tuple=6 , __a : Dict=2048 , __a : Dict=8 , __a : List[Any]=6 , __a : Tuple=1024 , __a : int=8 , __a : Union[str, Any]=0.0 , __a : Dict=True , __a : Any="relu" , __a : Any=256 , __a : Optional[int]=0.1 , __a : Union[str, Any]=0.0 , __a : Optional[int]=0.0 , __a : Tuple=0.02 , __a : Dict=1.0 , __a : int=True , __a : List[str]=False , __a : Any="sine" , __a : Optional[int]=5 , __a : List[str]=4 , __a : Dict=4 , __a : int=True , __a : Tuple=300 , __a : int=True , __a : Tuple=True , __a : int=1 , __a : List[str]=5 , __a : str=2 , __a : Any=1 , __a : Optional[Any]=1 , __a : Optional[Any]=5 , __a : List[str]=2 , __a : Optional[Any]=0.1 , __a : Tuple=0.25 , **__a : Union[str, Any] , ):
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase_ = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
else:
if isinstance(__a , __a ):
UpperCAmelCase_ = backbone_config.pop("model_type" )
UpperCAmelCase_ = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase_ = config_class.from_dict(__a )
UpperCAmelCase_ = backbone_config
UpperCAmelCase_ = num_queries
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = d_model
UpperCAmelCase_ = encoder_ffn_dim
UpperCAmelCase_ = encoder_layers
UpperCAmelCase_ = encoder_attention_heads
UpperCAmelCase_ = decoder_ffn_dim
UpperCAmelCase_ = decoder_layers
UpperCAmelCase_ = decoder_attention_heads
UpperCAmelCase_ = dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = init_std
UpperCAmelCase_ = init_xavier_std
UpperCAmelCase_ = encoder_layerdrop
UpperCAmelCase_ = auxiliary_loss
UpperCAmelCase_ = position_embedding_type
# deformable attributes
UpperCAmelCase_ = num_feature_levels
UpperCAmelCase_ = encoder_n_points
UpperCAmelCase_ = decoder_n_points
UpperCAmelCase_ = two_stage
UpperCAmelCase_ = two_stage_num_proposals
UpperCAmelCase_ = with_box_refine
UpperCAmelCase_ = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("If two_stage is True, with_box_refine must be True." )
# Hungarian matcher
UpperCAmelCase_ = class_cost
UpperCAmelCase_ = bbox_cost
UpperCAmelCase_ = giou_cost
# Loss coefficients
UpperCAmelCase_ = mask_loss_coefficient
UpperCAmelCase_ = dice_loss_coefficient
UpperCAmelCase_ = bbox_loss_coefficient
UpperCAmelCase_ = giou_loss_coefficient
UpperCAmelCase_ = eos_coefficient
UpperCAmelCase_ = focal_alpha
super().__init__(is_encoder_decoder=__a , **__a )
@property
def _lowercase (self : Dict ):
return self.encoder_attention_heads
@property
def _lowercase (self : Any ):
return self.d_model
def _lowercase (self : int ):
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.backbone_config.to_dict()
UpperCAmelCase_ = self.__class__.model_type
return output
| 415 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCamelCase_ = Features({'''text''': Value('''string''' )} )
lowerCamelCase_ = Features({'''labels''': ClassLabel} )
lowerCamelCase_ = "text"
lowerCamelCase_ = "labels"
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , lowercase ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
A_ : int = copy.deepcopy(self )
A_ : int = self.label_schema.copy()
A_ : Dict = features[self.label_column]
A_ : List[Any] = label_schema
return task_template
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return {
self.text_column: "text",
self.label_column: "labels",
}
| 558 | import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_UpperCAmelCase = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def UpperCamelCase ( __lowercase : int ):
'''simple docstring'''
A_ : Tuple = numpy.dtype(numpy.uintaa ).newbyteorder('>' )
return numpy.frombuffer(bytestream.read(4 ) ,dtype=__lowercase )[0]
@deprecated(__lowercase ,'Please use tf.data to implement this functionality.' )
def UpperCamelCase ( __lowercase : List[str] ):
'''simple docstring'''
print('Extracting' ,f.name )
with gzip.GzipFile(fileobj=__lowercase ) as bytestream:
A_ : str = _readaa(__lowercase )
if magic != 20_51:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' % (magic, f.name) )
A_ : Optional[int] = _readaa(__lowercase )
A_ : List[Any] = _readaa(__lowercase )
A_ : Any = _readaa(__lowercase )
A_ : List[str] = bytestream.read(rows * cols * num_images )
A_ : Optional[int] = numpy.frombuffer(__lowercase ,dtype=numpy.uinta )
A_ : Union[str, Any] = data.reshape(__lowercase ,__lowercase ,__lowercase ,1 )
return data
@deprecated(__lowercase ,'Please use tf.one_hot on tensors.' )
def UpperCamelCase ( __lowercase : Optional[int] ,__lowercase : int ):
'''simple docstring'''
A_ : Dict = labels_dense.shape[0]
A_ : List[str] = numpy.arange(__lowercase ) * num_classes
A_ : Any = numpy.zeros((num_labels, num_classes) )
A_ : List[Any] = 1
return labels_one_hot
@deprecated(__lowercase ,'Please use tf.data to implement this functionality.' )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : Tuple=False ,__lowercase : List[str]=10 ):
'''simple docstring'''
print('Extracting' ,f.name )
with gzip.GzipFile(fileobj=__lowercase ) as bytestream:
A_ : int = _readaa(__lowercase )
if magic != 20_49:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' % (magic, f.name) )
A_ : List[Any] = _readaa(__lowercase )
A_ : Dict = bytestream.read(__lowercase )
A_ : List[str] = numpy.frombuffer(__lowercase ,dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__lowercase ,__lowercase )
return labels
class UpperCAmelCase :
'''simple docstring'''
@deprecated(
lowercase , 'Please use alternatives such as official/mnist/_DataSet.py'
' from tensorflow/models.' , )
def __init__( self , lowercase , lowercase , lowercase=False , lowercase=False , lowercase=dtypes.floataa , lowercase=True , lowercase=None , ):
"""simple docstring"""
A_ , A_ : Dict = random_seed.get_seed(lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
A_ : Optional[int] = dtypes.as_dtype(lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype )
if fake_data:
A_ : Optional[int] = 1_0_0_0_0
A_ : int = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
A_ : Dict = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
A_ : int = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
A_ : Dict = images.astype(numpy.floataa )
A_ : Optional[int] = numpy.multiply(lowercase , 1.0 / 255.0 )
A_ : Optional[int] = images
A_ : List[Any] = labels
A_ : str = 0
A_ : Union[str, Any] = 0
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return self._images
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return self._labels
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return self._num_examples
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return self._epochs_completed
def lowerCAmelCase_ ( self , lowercase , lowercase=False , lowercase=True ):
"""simple docstring"""
if fake_data:
A_ : List[Any] = [1] * 7_8_4
A_ : Dict = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(lowercase )],
[fake_label for _ in range(lowercase )],
)
A_ : int = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
A_ : Tuple = numpy.arange(self._num_examples )
numpy.random.shuffle(lowercase )
A_ : Dict = self.images[perma]
A_ : Optional[Any] = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
A_ : List[Any] = self._num_examples - start
A_ : Optional[int] = self._images[start : self._num_examples]
A_ : Optional[int] = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
A_ : Tuple = numpy.arange(self._num_examples )
numpy.random.shuffle(lowercase )
A_ : Dict = self.images[perm]
A_ : Any = self.labels[perm]
# Start next epoch
A_ : Any = 0
A_ : Optional[Any] = batch_size - rest_num_examples
A_ : Optional[int] = self._index_in_epoch
A_ : Dict = self._images[start:end]
A_ : Any = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
A_ : Tuple = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__lowercase ,'Please write your own downloading logic.' )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Optional[int] ,__lowercase : Any ):
'''simple docstring'''
if not gfile.Exists(__lowercase ):
gfile.MakeDirs(__lowercase )
A_ : Optional[Any] = os.path.join(__lowercase ,__lowercase )
if not gfile.Exists(__lowercase ):
urllib.request.urlretrieve(__lowercase ,__lowercase ) # noqa: S310
with gfile.GFile(__lowercase ) as f:
A_ : Optional[int] = f.size()
print('Successfully downloaded' ,__lowercase ,__lowercase ,'bytes.' )
return filepath
@deprecated(
__lowercase ,'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')' )
def UpperCamelCase ( __lowercase : Dict ,__lowercase : str=False ,__lowercase : int=False ,__lowercase : str=dtypes.floataa ,__lowercase : int=True ,__lowercase : List[Any]=50_00 ,__lowercase : Union[str, Any]=None ,__lowercase : Union[str, Any]=DEFAULT_SOURCE_URL ,):
'''simple docstring'''
if fake_data:
def fake():
return _DataSet(
[] ,[] ,fake_data=__lowercase ,one_hot=__lowercase ,dtype=__lowercase ,seed=__lowercase )
A_ : Union[str, Any] = fake()
A_ : Any = fake()
A_ : str = fake()
return _Datasets(train=__lowercase ,validation=__lowercase ,test=__lowercase )
if not source_url: # empty string check
A_ : Tuple = DEFAULT_SOURCE_URL
A_ : List[Any] = 'train-images-idx3-ubyte.gz'
A_ : Optional[int] = 'train-labels-idx1-ubyte.gz'
A_ : Optional[Any] = 't10k-images-idx3-ubyte.gz'
A_ : Optional[int] = 't10k-labels-idx1-ubyte.gz'
A_ : str = _maybe_download(
__lowercase ,__lowercase ,source_url + train_images_file )
with gfile.Open(__lowercase ,'rb' ) as f:
A_ : Optional[int] = _extract_images(__lowercase )
A_ : Optional[Any] = _maybe_download(
__lowercase ,__lowercase ,source_url + train_labels_file )
with gfile.Open(__lowercase ,'rb' ) as f:
A_ : Union[str, Any] = _extract_labels(__lowercase ,one_hot=__lowercase )
A_ : Any = _maybe_download(
__lowercase ,__lowercase ,source_url + test_images_file )
with gfile.Open(__lowercase ,'rb' ) as f:
A_ : str = _extract_images(__lowercase )
A_ : Union[str, Any] = _maybe_download(
__lowercase ,__lowercase ,source_url + test_labels_file )
with gfile.Open(__lowercase ,'rb' ) as f:
A_ : int = _extract_labels(__lowercase ,one_hot=__lowercase )
if not 0 <= validation_size <= len(__lowercase ):
A_ : Dict = (
'Validation size should be between 0 and '
f'''{len(__lowercase )}. Received: {validation_size}.'''
)
raise ValueError(__lowercase )
A_ : int = train_images[:validation_size]
A_ : List[str] = train_labels[:validation_size]
A_ : Tuple = train_images[validation_size:]
A_ : Dict = train_labels[validation_size:]
A_ : str = {'dtype': dtype, 'reshape': reshape, 'seed': seed}
A_ : Optional[int] = _DataSet(__lowercase ,__lowercase ,**__lowercase )
A_ : int = _DataSet(__lowercase ,__lowercase ,**__lowercase )
A_ : int = _DataSet(__lowercase ,__lowercase ,**__lowercase )
return _Datasets(train=__lowercase ,validation=__lowercase ,test=__lowercase )
| 558 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = []
self.adlist.append(
{'value': '', 'next_states': [], 'fail_state': 0, 'output': []} )
for keyword in keywords:
self.add_keyword(snake_case_ )
self.set_fail_transitions()
def _UpperCamelCase ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : int = 0
for character in keyword:
UpperCAmelCase_ : Tuple = self.find_next_state(snake_case_ , snake_case_ )
if next_state is None:
self.adlist.append(
{
'value': character,
'next_states': [],
'fail_state': 0,
'output': [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
UpperCAmelCase_ : Tuple = len(self.adlist ) - 1
else:
UpperCAmelCase_ : int = next_state
self.adlist[current_state]["output"].append(snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = deque()
for node in self.adlist[0]["next_states"]:
q.append(snake_case_ )
UpperCAmelCase_ : List[str] = 0
while q:
UpperCAmelCase_ : str = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(snake_case_ )
UpperCAmelCase_ : Any = self.adlist[r]['fail_state']
while (
self.find_next_state(snake_case_ , self.adlist[child]['value'] ) is None
and state != 0
):
UpperCAmelCase_ : Any = self.adlist[state]['fail_state']
UpperCAmelCase_ : str = self.find_next_state(
snake_case_ , self.adlist[child]['value'] )
if self.adlist[child]["fail_state"] is None:
UpperCAmelCase_ : List[str] = 0
UpperCAmelCase_ : str = (
self.adlist[child]['output']
+ self.adlist[self.adlist[child]['fail_state']]['output']
)
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = {} # returns a dict with keywords and list of its occurrences
UpperCAmelCase_ : List[str] = 0
for i in range(len(snake_case_ ) ):
while (
self.find_next_state(snake_case_ , string[i] ) is None
and current_state != 0
):
UpperCAmelCase_ : List[Any] = self.adlist[current_state]['fail_state']
UpperCAmelCase_ : List[str] = self.find_next_state(snake_case_ , string[i] )
if next_state is None:
UpperCAmelCase_ : Dict = 0
else:
UpperCAmelCase_ : List[str] = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
UpperCAmelCase_ : Any = []
result[key].append(i - len(snake_case_ ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 717 | '''simple docstring'''
import qiskit
def _lowerCamelCase ( lowerCamelCase_ : int = 2 ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = qubits
# Using Aer's simulator
UpperCAmelCase_ : str = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
UpperCAmelCase_ : List[str] = qiskit.QuantumCircuit(lowerCamelCase_ , lowerCamelCase_ )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , lowerCamelCase_ ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , lowerCamelCase_ )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(lowerCamelCase_ ) ) , list(range(lowerCamelCase_ ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
UpperCAmelCase_ : Dict = qiskit.execute(lowerCamelCase_ , lowerCamelCase_ , shots=1000 )
return job.result().get_counts(lowerCamelCase_ )
if __name__ == "__main__":
print(f'''Total count for various states are: {quantum_entanglement(3)}''')
| 389 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 3
class lowercase ( _UpperCAmelCase ):
pass
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
for shard in shards:
for i in range(A_ ):
yield {"i": i, "shard": shard}
def UpperCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase = int(os.environ["""RANK"""] )
lowerCAmelCase = int(os.environ["""WORLD_SIZE"""] )
lowerCAmelCase = ArgumentParser()
parser.add_argument("""--streaming""" , type=A_ )
parser.add_argument("""--local_rank""" , type=A_ )
parser.add_argument("""--num_workers""" , type=A_ , default=0 )
lowerCAmelCase = parser.parse_args()
lowerCAmelCase = args.streaming
lowerCAmelCase = args.num_workers
lowerCAmelCase = {"""shards""": [F'shard_{shard_idx}' for shard_idx in range(A_ )]}
lowerCAmelCase = IterableDataset.from_generator(A_ , gen_kwargs=A_ )
if not streaming:
lowerCAmelCase = Dataset.from_list(list(A_ ) )
lowerCAmelCase = split_dataset_by_node(A_ , rank=A_ , world_size=A_ )
lowerCAmelCase = torch.utils.data.DataLoader(A_ , num_workers=A_ )
lowerCAmelCase = NUM_SHARDS * NUM_ITEMS_PER_SHARD
lowerCAmelCase = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
lowerCAmelCase = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F'local_size {local_size} != expected_local_size {expected_local_size}' )
if __name__ == "__main__":
main()
| 532 | import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
a_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
a_ = F"https://www.google.com/search?q={query}&num=100"
a_ = requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
a_ = (
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
a_ = parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link)
| 221 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __lowerCamelCase ( *_lowerCAmelCase : List[Any] , **_lowerCAmelCase : str):
'''simple docstring'''
pass
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =np.array(_lowerCAmelCase )
__lowercase =npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowerCAmelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def __lowerCamelCase ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Any):
'''simple docstring'''
__lowercase =MaskGenerationPipeline(model=_lowerCAmelCase , image_processor=_lowerCAmelCase)
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Dict):
'''simple docstring'''
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF')
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@slow
@require_torch
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__lowercase =pipeline('mask-generation' , model='facebook/sam-vit-huge')
__lowercase =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=2_5_6)
# Shortening by hashing
__lowercase =[]
for i, o in enumerate(outputs['masks']):
new_outupt += [{"mask": mask_to_test_readable(_lowerCAmelCase), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
{'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
{'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
{'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
{'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
{'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
{'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
{'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
{'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
{'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
{'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
{'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
{'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
{'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
{'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
{'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
{'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
{'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase ='facebook/sam-vit-huge'
__lowercase =pipeline('mask-generation' , model=_lowerCAmelCase)
__lowercase =image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=2_5_6)
# Shortening by hashing
__lowercase =[]
for i, o in enumerate(outputs['masks']):
new_outupt += [{"mask": mask_to_test_readable(_lowerCAmelCase), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
] , )
| 454 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
__lowercase =str(abs(_lowerCAmelCase ) )
__lowercase =[list(_lowerCAmelCase ) for char in range(len(_lowerCAmelCase ) )]
for index in range(len(_lowerCAmelCase ) ):
num_transpositions[index].pop(_lowerCAmelCase )
return max(
int(''.join(list(_lowerCAmelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 454 | 1 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
A = datasets.load_iris()
A = np.array(data['data'])
A = np.array(data['target'])
A = data['target_names']
A , A , A , A = train_test_split(X, y)
def UpperCAmelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple):
return np.linalg.norm(np.array(UpperCAmelCase__) - np.array(UpperCAmelCase__))
def UpperCAmelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple=5):
lowerCamelCase : int = zip(UpperCAmelCase__ , UpperCAmelCase__)
# List of distances of all points from the point to be classified
lowerCamelCase : str = []
for data_point in data:
lowerCamelCase : Union[str, Any] = euclidean_distance(data_point[0] , UpperCAmelCase__)
distances.append((distance, data_point[1]))
# Choosing 'k' points with the least distances.
lowerCamelCase : Any = [i[1] for i in sorted(UpperCAmelCase__)[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
lowerCamelCase : str = Counter(UpperCAmelCase__).most_common(1)[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 320 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( UpperCamelCase="ro" , UpperCamelCase="en" , UpperCamelCase="wmt16" , UpperCamelCase=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
lowerCAmelCase__ : Optional[Any] = f"""{src_lang}-{tgt_lang}"""
print(f"""Converting {dataset}-{pair}""" )
lowerCAmelCase__ : Any = datasets.load_dataset(UpperCamelCase , UpperCamelCase )
if save_dir is None:
lowerCAmelCase__ : Optional[Any] = f"""{dataset}-{pair}"""
lowerCAmelCase__ : Optional[Any] = Path(UpperCamelCase )
save_dir.mkdir(exist_ok=UpperCamelCase )
for split in ds.keys():
print(f"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
lowerCAmelCase__ : str = """val""" if split == """validation""" else split
lowerCAmelCase__ : Optional[int] = save_dir.joinpath(f"""{fn}.source""" )
lowerCAmelCase__ : Any = save_dir.joinpath(f"""{fn}.target""" )
lowerCAmelCase__ : Union[str, Any] = src_path.open("""w+""" )
lowerCAmelCase__ : Optional[int] = tgt_path.open("""w+""" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
lowerCAmelCase__ : Optional[int] = x["""translation"""]
src_fp.write(ex[src_lang] + """\n""" )
tgt_fp.write(ex[tgt_lang] + """\n""" )
print(f"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 565 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase( A__ ):
"""simple docstring"""
__magic_name__ = ["""image_processor""", """tokenizer"""]
__magic_name__ = """CLIPImageProcessor"""
__magic_name__ = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , __magic_name__=None , __magic_name__=None , **__magic_name__ ):
"""simple docstring"""
A_ : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __magic_name__ , )
A_ : Union[str, Any] = kwargs.pop('''feature_extractor''' )
A_ : Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__magic_name__ , __magic_name__ )
def __call__( self , __magic_name__=None , __magic_name__=None , __magic_name__=None , **__magic_name__ ):
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
A_ : Tuple = self.tokenizer(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
if images is not None:
A_ : Tuple = self.image_processor(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
if text is not None and images is not None:
A_ : List[str] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__magic_name__ ) , tensor_type=__magic_name__ )
def UpperCAmelCase ( self , *__magic_name__ , **__magic_name__ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def UpperCAmelCase ( self , *__magic_name__ , **__magic_name__ ):
"""simple docstring"""
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __magic_name__ , )
return self.image_processor_class
@property
def UpperCAmelCase ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __magic_name__ , )
return self.image_processor
| 236 | import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : str = 10
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[Any] = [1, 2, 3, 4]
A_ : Union[str, Any] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
A_ : Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
A_ : Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__magic_name__ , self.block_size , 0 ) , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[Any] = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
A_ , A_ : Optional[Any] = process_story(__magic_name__ )
self.assertEqual(__magic_name__ , [] )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = ''''''
A_ , A_ : Union[str, Any] = process_story(__magic_name__ )
self.assertEqual(__magic_name__ , [] )
self.assertEqual(__magic_name__ , [] )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
A_ , A_ : Optional[int] = process_story(__magic_name__ )
A_ : List[str] = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(__magic_name__ , __magic_name__ )
A_ : Union[str, Any] = ['''It was the best of times.''']
self.assertEqual(__magic_name__ , __magic_name__ )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : List[str] = torch.tensor([1, 2, 3, 4] )
A_ : Any = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Any = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
A_ : str = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : Optional[int] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
A_ : Union[str, Any] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__magic_name__ , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase ( self ):
"""simple docstring"""
A_ : int = 101
A_ : List[str] = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
A_ : Tuple = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
A_ : Optional[Any] = compute_token_type_ids(__magic_name__ , __magic_name__ )
np.testing.assert_array_equal(__magic_name__ , __magic_name__ )
| 236 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=7 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=18 , __SCREAMING_SNAKE_CASE=30 , __SCREAMING_SNAKE_CASE=400 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=None , ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : int =size if size is not None else {'''shortest_edge''': 20}
snake_case__ : Union[str, Any] =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case__ : Union[str, Any] =parent
snake_case__ : str =batch_size
snake_case__ : Union[str, Any] =num_channels
snake_case__ : Union[str, Any] =image_size
snake_case__ : Optional[Any] =min_resolution
snake_case__ : int =max_resolution
snake_case__ : List[str] =do_resize
snake_case__ : Dict =size
snake_case__ : Dict =do_center_crop
snake_case__ : str =crop_size
def UpperCAmelCase ( self ) -> Dict:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowerCAmelCase ( __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ =MobileNetVaImageProcessor if is_vision_available() else None
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Optional[int] =MobileNetVaImageProcessingTester(self )
@property
def UpperCAmelCase ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : List[str] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_resize''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''size''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_center_crop''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''crop_size''' ) )
def UpperCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Any =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
snake_case__ : List[str] =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def UpperCAmelCase ( self ) -> str:
"""simple docstring"""
pass
def UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : List[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ : Dict =prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
snake_case__ : Union[str, 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(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCAmelCase ( self ) -> List[str]:
"""simple docstring"""
snake_case__ : Any =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ : Union[str, Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , np.ndarray )
# 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__ : Any =image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : str =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ : int =prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
snake_case__ : 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[int] =image_processing(__SCREAMING_SNAKE_CASE , 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'''],
) , )
| 381 |
import math
def lowercase_ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def lowercase_ ( SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return 2 * x
def lowercase_ ( SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
snake_case__ : Dict =2.0
while start <= a:
snake_case__ : List[Any] =math.pow(SCREAMING_SNAKE_CASE , 2 )
return start
def lowercase_ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int = 99_99 , SCREAMING_SNAKE_CASE : float = 0.00_0000_0000_0001 ):
"""simple docstring"""
if a < 0:
raise ValueError('''math domain error''' )
snake_case__ : List[str] =get_initial_point(SCREAMING_SNAKE_CASE )
for _ in range(SCREAMING_SNAKE_CASE ):
snake_case__ : Union[str, Any] =value
snake_case__ : Any =value - fx(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) / fx_derivative(SCREAMING_SNAKE_CASE )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 381 | 1 |
import argparse
import json
from tqdm import tqdm
def UpperCAmelCase_( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=a__ , default='''biencoder-nq-dev.json''' , help='''Path to raw DPR training data''' , )
parser.add_argument(
'''--evaluation_set''' , type=a__ , help='''where to store parsed evaluation_set file''' , )
parser.add_argument(
'''--gold_data_path''' , type=a__ , help='''where to store parsed gold_data_path file''' , )
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
with open(args.src_path , '''r''' ) as src_file, open(args.evaluation_set , '''w''' ) as eval_file, open(
args.gold_data_path , '''w''' ) as gold_file:
SCREAMING_SNAKE_CASE : Dict = json.load(a__ )
for dpr_record in tqdm(a__ ):
SCREAMING_SNAKE_CASE : List[str] = dpr_record['''question''']
SCREAMING_SNAKE_CASE : List[str] = [context['''title'''] for context in dpr_record['''positive_ctxs''']]
eval_file.write(question + '''\n''' )
gold_file.write('''\t'''.join(a__ ) + '''\n''' )
if __name__ == "__main__":
main()
| 333 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a__ : Tuple = logging.get_logger(__name__)
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ) ->None:
warnings.warn(
'''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ImageGPTImageProcessor instead.''' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 333 | 1 |
def a ( a ) ->Any: # noqa: E741
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(a )
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = [0] * n
SCREAMING_SNAKE_CASE = [False] * n
SCREAMING_SNAKE_CASE = [False] * n
def dfs(a , a , a , a ):
if parent == root:
out_edge_count += 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
SCREAMING_SNAKE_CASE = dfs(a , a , a , a )
SCREAMING_SNAKE_CASE = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
SCREAMING_SNAKE_CASE = True
# AP found via cycle
if at == low[to]:
SCREAMING_SNAKE_CASE = True
else:
SCREAMING_SNAKE_CASE = min(low[at] , a )
return out_edge_count
for i in range(a ):
if not visited[i]:
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = dfs(a , a , -1 , a )
SCREAMING_SNAKE_CASE = out_edge_count > 1
for x in range(len(a ) ):
if is_art[x] is True:
print(a )
# Adjacency list of graph
__lowerCAmelCase = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data) | 201 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_=1_3 , lowerCamelCase_=7 , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=9_9 , lowerCamelCase_=1_6 , lowerCamelCase_=3_6 , lowerCamelCase_=6 , lowerCamelCase_=6 , lowerCamelCase_=6 , lowerCamelCase_=3_7 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=5_1_2 , lowerCamelCase_=1_6 , lowerCamelCase_=2 , lowerCamelCase_=0.02 , lowerCamelCase_=3 , lowerCamelCase_=4 , lowerCamelCase_=None , ) -> Dict:
_a : Dict = parent
_a : Tuple = batch_size
_a : Optional[int] = seq_length
_a : List[str] = is_training
_a : Tuple = use_input_mask
_a : Dict = use_token_type_ids
_a : List[str] = use_labels
_a : Optional[int] = vocab_size
_a : int = embedding_size
_a : Tuple = hidden_size
_a : str = num_hidden_layers
_a : List[str] = num_hidden_groups
_a : Union[str, Any] = num_attention_heads
_a : Dict = intermediate_size
_a : List[str] = hidden_act
_a : Tuple = hidden_dropout_prob
_a : Any = attention_probs_dropout_prob
_a : Tuple = max_position_embeddings
_a : Dict = type_vocab_size
_a : Tuple = type_sequence_label_size
_a : Dict = initializer_range
_a : str = num_labels
_a : Optional[Any] = num_choices
_a : Optional[int] = scope
def __UpperCamelCase ( self ) -> Optional[int]:
_a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : List[str] = None
if self.use_input_mask:
_a : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
_a : Dict = None
if self.use_token_type_ids:
_a : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a : List[str] = None
_a : Optional[int] = None
_a : List[Any] = None
if self.use_labels:
_a : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a : str = ids_tensor([self.batch_size] , self.num_choices )
_a : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self ) -> Any:
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
_a : str = AlbertModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : Tuple = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
_a : Dict = model(lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
_a : List[str] = model(lowerCamelCase_ )
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 __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_a : Optional[int] = AlbertForPreTraining(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : Optional[Any] = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , sentence_order_label=lowerCamelCase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
_a : Union[str, Any] = AlbertForMaskedLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : int = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
_a : Union[str, Any] = AlbertForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : Tuple = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , start_positions=lowerCamelCase_ , end_positions=lowerCamelCase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
_a : str = self.num_labels
_a : str = AlbertForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : str = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
_a : Optional[int] = self.num_labels
_a : Any = AlbertForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : List[Any] = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
_a : Optional[int] = self.num_choices
_a : Any = AlbertForMultipleChoice(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a : Optional[Any] = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self ) -> Optional[Any]:
_a : Optional[int] = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : Optional[int] = config_and_inputs
_a : Optional[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a ( snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase : str = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
__lowerCAmelCase : List[Any] = (
{
"""feature-extraction""": AlbertModel,
"""fill-mask""": AlbertForMaskedLM,
"""question-answering""": AlbertForQuestionAnswering,
"""text-classification""": AlbertForSequenceClassification,
"""token-classification""": AlbertForTokenClassification,
"""zero-shot""": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCAmelCase : Optional[int] = True
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False ) -> List[str]:
_a : Optional[Any] = super()._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ , return_labels=lowerCamelCase_ )
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
_a : str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCamelCase_ )
_a : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
return inputs_dict
def __UpperCamelCase ( self ) -> List[Any]:
_a : int = AlbertModelTester(self )
_a : Union[str, Any] = ConfigTester(self , config_class=lowerCamelCase_ , hidden_size=3_7 )
def __UpperCamelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ) -> Optional[int]:
_a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Tuple:
_a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Any:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Tuple:
_a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Dict:
_a : str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a : Optional[Any] = type
self.model_tester.create_and_check_model(*lowerCamelCase_ )
@slow
def __UpperCamelCase ( self ) -> Optional[Any]:
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : int = AlbertModel.from_pretrained(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
@require_torch
class a ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : Dict = AlbertModel.from_pretrained('albert-base-v2' )
_a : Optional[Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_a : List[str] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_a : int = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ )[0]
_a : str = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , lowerCamelCase_ )
_a : int = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowerCamelCase_ , atol=1e-4 ) )
| 120 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
"tokenization_electra": ["ElectraTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["ElectraTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"ElectraForCausalLM",
"ElectraForMaskedLM",
"ElectraForMultipleChoice",
"ElectraForPreTraining",
"ElectraForQuestionAnswering",
"ElectraForSequenceClassification",
"ElectraForTokenClassification",
"ElectraModel",
"ElectraPreTrainedModel",
"load_tf_weights_in_electra",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFElectraPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxElectraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a_ = {
'bert-base-uncased': 5_1_2,
'bert-large-uncased': 5_1_2,
'bert-base-cased': 5_1_2,
'bert-large-cased': 5_1_2,
'bert-base-multilingual-uncased': 5_1_2,
'bert-base-multilingual-cased': 5_1_2,
'bert-base-chinese': 5_1_2,
'bert-base-german-cased': 5_1_2,
'bert-large-uncased-whole-word-masking': 5_1_2,
'bert-large-cased-whole-word-masking': 5_1_2,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-base-cased-finetuned-mrpc': 5_1_2,
'bert-base-german-dbmdz-cased': 5_1_2,
'bert-base-german-dbmdz-uncased': 5_1_2,
'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
'wietsedv/bert-base-dutch-cased': 5_1_2,
}
a_ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BertTokenizer
def __init__( self : int , __lowercase : Union[str, Any]=None , __lowercase : Tuple=None , __lowercase : str=True , __lowercase : Optional[Any]="[UNK]" , __lowercase : Tuple="[SEP]" , __lowercase : Any="[PAD]" , __lowercase : List[Any]="[CLS]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Tuple=True , __lowercase : str=None , **__lowercase : Any , ) -> Optional[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : str =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Any =do_lower_case
SCREAMING_SNAKE_CASE__ : Any =strip_accents
SCREAMING_SNAKE_CASE__ : Dict =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=None ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase ) | 665 | 0 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
__lowerCAmelCase : Any = "3"
print("Python version:", sys.version)
print("OS platform:", platform.platform())
print("OS architecture:", platform.machine())
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
except ImportError:
print("Torch version:", None)
try:
import transformers
print("transformers version:", transformers.__version__)
except ImportError:
print("transformers version:", None)
| 509 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase : List[Any] = []
for i in range(6):
# 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}.cross_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.cross_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'))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
__lowercase : Union[str, Any] = state_dict.pop(__lowerCAmelCase )
__lowercase : Optional[Any] = val
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
__lowercase : List[str] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
__lowercase : List[Any] = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
__lowercase : Tuple = value
else:
__lowercase : Union[str, Any] = value
return new_state_dict
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=False ) -> List[Any]:
__lowercase : Tuple = ''''''
if is_panoptic:
__lowercase : Optional[int] = '''conditional_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)
__lowercase : List[str] = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
__lowercase : Union[str, Any] = 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
__lowercase : int = in_proj_weight[:256, :]
__lowercase : List[str] = in_proj_bias[:256]
__lowercase : Tuple = in_proj_weight[256:512, :]
__lowercase : Tuple = in_proj_bias[256:512]
__lowercase : str = in_proj_weight[-256:, :]
__lowercase : Optional[Any] = in_proj_bias[-256:]
def UpperCAmelCase_ ( ) -> List[str]:
__lowercase : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__lowercase : List[str] = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
__lowercase : Optional[int] = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
__lowercase : int = '''resnet101'''
if "dc5" in model_name:
__lowercase : Optional[Any] = True
__lowercase : int = '''panoptic''' in model_name
if is_panoptic:
__lowercase : Optional[int] = 250
else:
__lowercase : List[Any] = 91
__lowercase : int = '''huggingface/label-files'''
__lowercase : int = '''coco-detection-id2label.json'''
__lowercase : Optional[int] = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
__lowercase : Union[str, Any] = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
__lowercase : List[Any] = idalabel
__lowercase : Optional[Any] = {v: k for k, v in idalabel.items()}
# load image processor
__lowercase : Optional[Any] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
__lowercase : List[str] = ConditionalDetrImageProcessor(format=__lowerCAmelCase )
# prepare image
__lowercase : Union[str, Any] = prepare_img()
__lowercase : List[Any] = image_processor(images=__lowerCAmelCase , return_tensors='''pt''' )
__lowercase : str = encoding['''pixel_values''']
logger.info(F'Converting model {model_name}...' )
# load original model from torch hub
__lowercase : Tuple = torch.hub.load('''DeppMeng/ConditionalDETR''' , __lowerCAmelCase , pretrained=__lowerCAmelCase ).eval()
__lowercase : Any = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
__lowercase : Tuple = '''conditional_detr.''' + src
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__lowercase : Any = rename_backbone_keys(__lowerCAmelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(__lowerCAmelCase , is_panoptic=__lowerCAmelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
__lowercase : Any = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''conditional_detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
__lowercase : Union[str, Any] = state_dict.pop(__lowerCAmelCase )
__lowercase : Tuple = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
__lowercase : str = state_dict.pop(__lowerCAmelCase )
__lowercase : Tuple = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
__lowercase : Any = state_dict.pop(__lowerCAmelCase )
__lowercase : List[str] = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
__lowercase : Any = state_dict.pop(__lowerCAmelCase )
__lowercase : List[str] = val
# finally, create HuggingFace model and load state dict
__lowercase : Union[str, Any] = ConditionalDetrForSegmentation(__lowerCAmelCase ) if is_panoptic else ConditionalDetrForObjectDetection(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
model.push_to_hub(repo_id=__lowerCAmelCase , organization='''DepuMeng''' , commit_message='''Add model''' )
# verify our conversion
__lowercase : Optional[Any] = conditional_detr(__lowerCAmelCase )
__lowercase : Dict = model(__lowerCAmelCase )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
# Save model and image processor
logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
model.save_pretrained(__lowerCAmelCase )
image_processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_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."
)
__lowerCAmelCase : str = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 509 | 1 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ : Tuple = sorted(zip(__UpperCamelCase , __UpperCamelCase ) , key=lambda __UpperCamelCase : x[0] / x[1] , reverse=__UpperCamelCase )
A__ , A__ : Dict = [i[0] for i in r], [i[1] for i in r]
A__ : Any = list(accumulate(__UpperCamelCase ) )
A__ : Any = bisect(__UpperCamelCase , __UpperCamelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 55 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################
_SCREAMING_SNAKE_CASE : str = 1_6
_SCREAMING_SNAKE_CASE : Tuple = 3_2
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Accelerator , __UpperCamelCase : int = 16 ) -> Optional[int]:
"""simple docstring"""
A__ : List[str] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
A__ : Optional[int] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__UpperCamelCase : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
A__ : int = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__UpperCamelCase , max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ : Optional[int] = datasets.map(
__UpperCamelCase , batched=__UpperCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ : List[Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__UpperCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ : Optional[Any] = 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":
A__ : int = 16
elif accelerator.mixed_precision != "no":
A__ : Any = 8
else:
A__ : Union[str, Any] = None
return tokenizer.pad(
__UpperCamelCase , padding='''longest''' , max_length=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
A__ : Optional[int] = DataLoader(
tokenized_datasets['''train'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
A__ : Tuple = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
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
_SCREAMING_SNAKE_CASE : Dict = mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __UpperCamelCase ) == "1":
A__ : List[str] = 2
# Initialize accelerator
A__ : Optional[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ : Tuple = config['''lr''']
A__ : Dict = int(config['''num_epochs'''] )
A__ : int = int(config['''seed'''] )
A__ : Optional[Any] = int(config['''batch_size'''] )
A__ : int = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
A__ : Union[str, Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ : List[Any] = batch_size // MAX_GPU_BATCH_SIZE
A__ : Dict = MAX_GPU_BATCH_SIZE
set_seed(__UpperCamelCase )
A__ , A__ : int = get_dataloaders(__UpperCamelCase , __UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ : Tuple = model.to(accelerator.device )
# Instantiate optimizer
A__ : Optional[int] = AdamW(params=model.parameters() , lr=__UpperCamelCase )
# Instantiate scheduler
A__ : Any = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ : Dict = accelerator.prepare(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Now we train the model
for epoch in range(__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ : Dict = model(**__UpperCamelCase )
A__ : Dict = outputs.loss
A__ : List[str] = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
A__ : Optional[int] = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ : Union[str, Any] = model(**__UpperCamelCase )
A__ : int = outputs.logits.argmax(dim=-1 )
A__ , A__ : Optional[Any] = accelerator.gather((predictions, batch['''labels''']) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(__UpperCamelCase ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
A__ : Tuple = predictions[: len(eval_dataloader.dataset ) - samples_seen]
A__ : int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=__UpperCamelCase , references=__UpperCamelCase , )
A__ : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , __UpperCamelCase )
def SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
"""simple docstring"""
A__ : Tuple = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__UpperCamelCase , default=__UpperCamelCase , 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.''' )
A__ : Dict = parser.parse_args()
A__ : Any = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
main() | 55 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __lowercase ( a__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = FileLock(str(tmpdir / 'foo.lock' ) )
__SCREAMING_SNAKE_CASE = FileLock(str(tmpdir / 'foo.lock' ) )
__SCREAMING_SNAKE_CASE = 0.01
with locka.acquire():
with pytest.raises(a__ ):
__SCREAMING_SNAKE_CASE = time.time()
locka.acquire(a__ )
assert time.time() - _start > timeout
def __lowercase ( a__ ) -> Dict:
__SCREAMING_SNAKE_CASE = 'a' * 10_00 + '.lock'
__SCREAMING_SNAKE_CASE = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(a__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_55
__SCREAMING_SNAKE_CASE = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(a__ ):
locka.acquire(0 )
| 148 |
# 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowerCAmelCase__ : Any ='''Create a default config file for Accelerate with only a few flags set.'''
def __lowercase ( a__="no" , a__ = default_json_config_file , a__ = False ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = Path(a__ )
path.parent.mkdir(parents=a__ , exist_ok=a__ )
if path.exists():
print(
f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
__SCREAMING_SNAKE_CASE = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
__SCREAMING_SNAKE_CASE = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
__SCREAMING_SNAKE_CASE = num_gpus
__SCREAMING_SNAKE_CASE = False
if num_gpus > 1:
__SCREAMING_SNAKE_CASE = 'MULTI_GPU'
else:
__SCREAMING_SNAKE_CASE = 'NO'
elif is_xpu_available() and use_xpu:
__SCREAMING_SNAKE_CASE = torch.xpu.device_count()
__SCREAMING_SNAKE_CASE = num_xpus
__SCREAMING_SNAKE_CASE = False
if num_xpus > 1:
__SCREAMING_SNAKE_CASE = 'MULTI_XPU'
else:
__SCREAMING_SNAKE_CASE = 'NO'
elif is_npu_available():
__SCREAMING_SNAKE_CASE = torch.npu.device_count()
__SCREAMING_SNAKE_CASE = num_npus
__SCREAMING_SNAKE_CASE = False
if num_npus > 1:
__SCREAMING_SNAKE_CASE = 'MULTI_NPU'
else:
__SCREAMING_SNAKE_CASE = 'NO'
else:
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 'NO'
__SCREAMING_SNAKE_CASE = ClusterConfig(**a__ )
config.to_json_file(a__ )
return path
def __lowercase ( a__ , a__ ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = parser.add_parser('default' , parents=a__ , help=a__ , formatter_class=a__ )
parser.add_argument(
'--config_file' , default=a__ , 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\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=a__ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=a__ )
return parser
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"""accelerate configuration saved at {config_file}""" )
| 148 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_original_tf_checkpoint_to_pytorch": [],
"tokenization_funnel": ["FunnelTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ["FunnelTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
"FunnelBaseModel",
"FunnelForMaskedLM",
"FunnelForMultipleChoice",
"FunnelForPreTraining",
"FunnelForQuestionAnswering",
"FunnelForSequenceClassification",
"FunnelForTokenClassification",
"FunnelModel",
"FunnelPreTrainedModel",
"load_tf_weights_in_funnel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFFunnelBaseModel",
"TFFunnelForMaskedLM",
"TFFunnelForMultipleChoice",
"TFFunnelForPreTraining",
"TFFunnelForQuestionAnswering",
"TFFunnelForSequenceClassification",
"TFFunnelForTokenClassification",
"TFFunnelModel",
"TFFunnelPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 718 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class a__ ( unittest.TestCase ):
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = "ylacombe/bark-small"
__UpperCAmelCase : Any = tempfile.mkdtemp()
__UpperCAmelCase : List[str] = "en_speaker_1"
__UpperCAmelCase : List[Any] = "This is a test string"
__UpperCAmelCase : List[str] = "speaker_embeddings_path.json"
__UpperCAmelCase : Tuple = "speaker_embeddings"
def a_ ( self : Optional[int] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
shutil.rmtree(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_tokenizer()
__UpperCAmelCase : Optional[Any] = BarkProcessor(tokenizer=UpperCamelCase_)
processor.save_pretrained(self.tmpdirname)
__UpperCAmelCase : Optional[int] = BarkProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab())
@slow
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : str = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
__UpperCAmelCase : Optional[Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)")
__UpperCAmelCase : Optional[Any] = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
__UpperCAmelCase : Optional[int] = 35
__UpperCAmelCase : Union[str, Any] = 2
__UpperCAmelCase : List[Any] = 8
__UpperCAmelCase : str = {
"semantic_prompt": np.ones(UpperCamelCase_),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
"fine_prompt": np.ones((nb_codebooks_total, seq_len)),
}
# test providing already loaded voice_preset
__UpperCAmelCase : Union[str, Any] = processor(text=self.input_string , voice_preset=UpperCamelCase_)
__UpperCAmelCase : str = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCamelCase_ , np.array([])).tolist())
# test loading voice preset from npz file
__UpperCAmelCase : int = os.path.join(self.tmpdirname , "file.npz")
np.savez(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : List[Any] = processor(text=self.input_string , voice_preset=UpperCamelCase_)
__UpperCAmelCase : List[str] = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCamelCase_ , np.array([])).tolist())
# test loading voice preset from the hub
__UpperCAmelCase : Tuple = processor(text=self.input_string , voice_preset=self.voice_preset)
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.get_tokenizer()
__UpperCAmelCase : Any = BarkProcessor(tokenizer=UpperCamelCase_)
__UpperCAmelCase : List[Any] = processor(text=self.input_string)
__UpperCAmelCase : List[Any] = tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist())
| 487 | 0 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if len(__a ) < 2:
return collection
def circle_sort_util(__a, __a, __a ) -> bool:
SCREAMING_SNAKE_CASE_ = False
if low == high:
return swapped
SCREAMING_SNAKE_CASE_ = low
SCREAMING_SNAKE_CASE_ = high
while left < right:
if collection[left] > collection[right]:
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = (
collection[right],
collection[left],
)
SCREAMING_SNAKE_CASE_ = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = (
collection[right + 1],
collection[left],
)
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = low + int((high - low) / 2 )
SCREAMING_SNAKE_CASE_ = circle_sort_util(__a, __a, __a )
SCREAMING_SNAKE_CASE_ = circle_sort_util(__a, mid + 1, __a )
return swapped or left_swap or right_swap
SCREAMING_SNAKE_CASE_ = True
while is_not_sorted is True:
SCREAMING_SNAKE_CASE_ = circle_sort_util(__a, 0, len(__a ) - 1 )
return collection
if __name__ == "__main__":
lowerCAmelCase__ = input('Enter numbers separated by a comma:\n').strip()
lowerCAmelCase__ = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted)) | 626 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _lowerCamelCase ( __a=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser('''test''' )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser('''Accelerate test command''' )
parser.add_argument(
'''--config_file''', default=__a, 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=__a )
return parser
def _lowerCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
SCREAMING_SNAKE_CASE_ = script_name
else:
SCREAMING_SNAKE_CASE_ = F'--config_file={args.config_file} {script_name}'
SCREAMING_SNAKE_CASE_ = ['''accelerate-launch'''] + test_args.split()
SCREAMING_SNAKE_CASE_ = execute_subprocess_async(__a, env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def _lowerCamelCase ( ):
SCREAMING_SNAKE_CASE_ = test_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
test_command(__a )
if __name__ == "__main__":
main() | 626 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 711 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase_ :
def __init__( self , A , A=13 , A=32 , A=3 , A=4 , A=[10, 20, 30, 40] , A=[2, 2, 3, 2] , A=True , A=True , A=37 , A="gelu" , A=10 , A=0.0_2 , A=["stage2", "stage3", "stage4"] , A=[2, 3, 4] , A=None , ) -> int:
UpperCAmelCase : str = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Dict = image_size
UpperCAmelCase : Tuple = num_channels
UpperCAmelCase : Union[str, Any] = num_stages
UpperCAmelCase : Any = hidden_sizes
UpperCAmelCase : str = depths
UpperCAmelCase : Optional[int] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : str = hidden_act
UpperCAmelCase : List[str] = num_labels
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Optional[Any] = out_features
UpperCAmelCase : List[str] = out_indices
UpperCAmelCase : Any = scope
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase : List[str] = self.get_config()
return config, pixel_values, labels
def _lowercase( self ) -> Optional[Any]:
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=A , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _lowercase( self , A , A , A ) -> Optional[Any]:
UpperCAmelCase : int = ConvNextVaModel(config=A )
model.to(A )
model.eval()
UpperCAmelCase : List[Any] = model(A )
# 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 _lowercase( self , A , A , A ) -> Any:
UpperCAmelCase : List[str] = ConvNextVaForImageClassification(A )
model.to(A )
model.eval()
UpperCAmelCase : int = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase( self , A , A , A ) -> Any:
UpperCAmelCase : Optional[Any] = ConvNextVaBackbone(config=A )
model.to(A )
model.eval()
UpperCAmelCase : Any = model(A )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
UpperCAmelCase : Any = None
UpperCAmelCase : Optional[int] = ConvNextVaBackbone(config=A )
model.to(A )
model.eval()
UpperCAmelCase : int = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _lowercase( self ) -> List[str]:
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : str = {"""pixel_values""": pixel_values}
return config, inputs_dict
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] = config_and_inputs
UpperCAmelCase : List[str] = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowercase = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Dict = ConvNextVaModelTester(self )
UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=37 )
def _lowercase( self ) -> int:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowercase( self ) -> List[str]:
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def _lowercase( self ) -> Dict:
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def _lowercase( self ) -> Any:
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def _lowercase( self ) -> int:
pass
def _lowercase( self ) -> Dict:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase : Optional[int] = True
if model_class.__name__ in [
*get_values(A ),
*get_values(A ),
]:
continue
UpperCAmelCase : Any = model_class(A )
model.to(A )
model.train()
UpperCAmelCase : List[str] = self._prepare_for_class(A , A , return_labels=A )
UpperCAmelCase : List[str] = model(**A ).loss
loss.backward()
def _lowercase( self ) -> Tuple:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase : List[str] = False
UpperCAmelCase : int = True
if (
model_class.__name__
in [*get_values(A ), *get_values(A )]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase : Dict = model_class(A )
model.to(A )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase : Any = self._prepare_for_class(A , A , return_labels=A )
UpperCAmelCase : Any = model(**A ).loss
loss.backward()
def _lowercase( self ) -> Tuple:
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(A )
UpperCAmelCase : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Tuple = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _lowercase( self ) -> List[str]:
def check_hidden_states_output(A , A , A ):
UpperCAmelCase : Optional[Any] = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(A , A ) )
UpperCAmelCase : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase : Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ConvNextV2'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] , )
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : str = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : int = True
check_hidden_states_output(A , A , A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def _lowercase( self ) -> Any:
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Tuple = ConvNextVaModel.from_pretrained(A )
self.assertIsNotNone(A )
def __lowerCamelCase ( ) -> Optional[int]:
UpperCAmelCase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class UpperCamelCase_ ( unittest.TestCase ):
@cached_property
def _lowercase( self ) -> str:
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Any = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(A )
UpperCAmelCase : List[Any] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : Tuple = preprocessor(images=A , return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**A )
# verify the logits
UpperCAmelCase : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , A )
UpperCAmelCase : Dict = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1e-4 ) )
| 672 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__a = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'FlaxLongT5ForConditionalGeneration',
'FlaxLongT5Model',
'FlaxLongT5PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 30 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
_lowerCamelCase : Union[str, Any] = '''
Human: <<task>>
Assistant: '''
_lowerCamelCase : Optional[Any] = '''huggingface-tools/default-prompts'''
_lowerCamelCase : int = {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}
def __lowerCamelCase (UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple="run" ):
if prompt_or_repo_id is None:
SCREAMING_SNAKE_CASE = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , UpperCAmelCase__ ) is not None:
return prompt_or_repo_id
SCREAMING_SNAKE_CASE = cached_file(
UpperCAmelCase__ , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(UpperCAmelCase__ , "r" , encoding="utf-8" ) as f:
return f.read()
| 403 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class _lowerCamelCase ( _lowercase ):
UpperCAmelCase_ = "altclip_text_model"
def __init__(self , __a=25_00_02 , __a=10_24 , __a=24 , __a=16 , __a=40_96 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_14 , __a=1 , __a=0.02 , __a=0.02 , __a=1e-0_5 , __a=1 , __a=0 , __a=2 , __a="absolute" , __a=True , __a=7_68 , **__a , ) -> Optional[Any]:
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
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 = initializer_factor
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = use_cache
UpperCamelCase = project_dim
class _lowerCamelCase ( _lowercase ):
UpperCAmelCase_ = "altclip_vision_model"
def __init__(self , __a=7_68 , __a=30_72 , __a=5_12 , __a=12 , __a=12 , __a=3 , __a=2_24 , __a=32 , __a="quick_gelu" , __a=1e-5 , __a=0.0 , __a=0.02 , __a=1.0 , **__a , ) -> List[str]:
super().__init__(**__a )
UpperCamelCase = hidden_size
UpperCamelCase = intermediate_size
UpperCamelCase = projection_dim
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = num_channels
UpperCamelCase = patch_size
UpperCamelCase = image_size
UpperCamelCase = initializer_range
UpperCamelCase = initializer_factor
UpperCamelCase = attention_dropout
UpperCamelCase = layer_norm_eps
UpperCamelCase = hidden_act
@classmethod
def snake_case_ (cls , __a , **__a ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__a )
UpperCamelCase , UpperCamelCase = cls.get_config_dict(__a , **__a )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("model_type" ) == "altclip":
UpperCamelCase = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__a , **__a )
class _lowerCamelCase ( _lowercase ):
UpperCAmelCase_ = "altclip"
UpperCAmelCase_ = True
def __init__(self , __a=None , __a=None , __a=7_68 , __a=2.6592 , **__a ) -> Any:
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
UpperCamelCase = kwargs.pop("text_config_dict" , __a )
UpperCamelCase = kwargs.pop("vision_config_dict" , __a )
super().__init__(**__a )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
UpperCamelCase = {}
# This is the complete result when using `text_config_dict`.
UpperCamelCase = AltCLIPTextConfig(**__a ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
UpperCamelCase = (
F"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
F"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
UpperCamelCase = (
F"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
F"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(__a )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
UpperCamelCase = {}
# This is the complete result when using `vision_config_dict`.
UpperCamelCase = AltCLIPVisionConfig(**__a ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
UpperCamelCase = {
str(__a ): value for key, value in _vision_config_dict["id2label"].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
UpperCamelCase = (
F"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
F"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
UpperCamelCase = (
F"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
F"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(__a )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
UpperCamelCase = {}
logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values." )
if vision_config is None:
UpperCamelCase = {}
logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values." )
UpperCamelCase = AltCLIPTextConfig(**__a )
UpperCamelCase = AltCLIPVisionConfig(**__a )
UpperCamelCase = projection_dim
UpperCamelCase = logit_scale_init_value
UpperCamelCase = 1.0
@classmethod
def snake_case_ (cls , __a , __a , **__a ) -> List[Any]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__a )
def snake_case_ (self ) -> int:
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.text_config.to_dict()
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 706 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = 0
for ch in input_str:
UpperCamelCase = ord(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pow(2 , _SCREAMING_SNAKE_CASE )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 544 | 0 |
"""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_ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''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:
lowerCamelCase_ = [
'''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:
lowerCamelCase_ = [
'''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
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 95 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( __UpperCamelCase ):
__a = (PNDMScheduler,)
__a = (("""num_inference_steps""", 50),)
def lowercase_ ( self , **SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCAmelCase__ : Any = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**SCREAMING_SNAKE_CASE__ )
return config
def lowercase_ ( self , SCREAMING_SNAKE_CASE__=0 , **SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = dict(self.forward_default_kwargs )
lowerCAmelCase__ : List[str] = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Union[str, Any] = self.dummy_sample
lowerCAmelCase__ : List[str] = 0.1 * sample
lowerCAmelCase__ : int = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowerCAmelCase__ : Dict = 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__ : int = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Optional[int] = 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[Any] = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : Optional[Any] = new_scheduler.step_prk(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__ : Optional[int] = scheduler.step_plms(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : Any = new_scheduler.step_plms(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 lowercase_ ( self ):
"""simple docstring"""
pass
def lowercase_ ( self , SCREAMING_SNAKE_CASE__=0 , **SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCAmelCase__ : Any = dict(self.forward_default_kwargs )
lowerCAmelCase__ : str = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Optional[Any] = self.dummy_sample
lowerCAmelCase__ : int = 0.1 * sample
lowerCAmelCase__ : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
lowerCAmelCase__ : int = self.get_scheduler_config()
lowerCAmelCase__ : Union[str, Any] = 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[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Union[str, Any] = 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__ : Optional[Any] = dummy_past_residuals[:]
lowerCAmelCase__ : str = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : List[str] = new_scheduler.step_prk(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__ : Optional[Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : str = new_scheduler.step_plms(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 lowercase_ ( self , **SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCAmelCase__ : Dict = self.scheduler_classes[0]
lowerCAmelCase__ : Any = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Dict = scheduler_class(**SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Union[str, Any] = 10
lowerCAmelCase__ : List[str] = self.dummy_model()
lowerCAmelCase__ : str = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for i, t in enumerate(scheduler.prk_timesteps ):
lowerCAmelCase__ : List[Any] = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Optional[int] = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
lowerCAmelCase__ : List[Any] = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : List[Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
return sample
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = dict(self.forward_default_kwargs )
lowerCAmelCase__ : Tuple = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE__ )
for scheduler_class in self.scheduler_classes:
lowerCAmelCase__ : Optional[Any] = self.get_scheduler_config()
lowerCAmelCase__ : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : str = self.dummy_sample
lowerCAmelCase__ : Dict = 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__ : List[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowerCAmelCase__ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
lowerCAmelCase__ : int = dummy_past_residuals[:]
lowerCAmelCase__ : Union[str, Any] = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , 0 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : Any = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , 1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
lowerCAmelCase__ : List[Any] = scheduler.step_plms(SCREAMING_SNAKE_CASE__ , 0 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
lowerCAmelCase__ : Any = scheduler.step_plms(SCREAMING_SNAKE_CASE__ , 1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase_ ( self ):
"""simple docstring"""
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Dict = self.scheduler_classes[0]
lowerCAmelCase__ : Union[str, Any] = self.get_scheduler_config(steps_offset=1 )
lowerCAmelCase__ : Tuple = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def lowercase_ ( self ):
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ , beta_end=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
for t in [1, 5, 10]:
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__ )
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : Dict = 27
for scheduler_class in self.scheduler_classes:
lowerCAmelCase__ : Optional[Any] = self.dummy_sample
lowerCAmelCase__ : Optional[int] = 0.1 * sample
lowerCAmelCase__ : Any = self.get_scheduler_config()
lowerCAmelCase__ : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
lowerCAmelCase__ : Tuple = scheduler.step_prk(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
def lowercase_ ( self ):
"""simple docstring"""
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ : Dict = self.scheduler_classes[0]
lowerCAmelCase__ : Optional[Any] = self.get_scheduler_config()
lowerCAmelCase__ : str = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = self.full_loop()
lowerCAmelCase__ : Tuple = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowerCAmelCase__ : Dict = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 198.1_318 ) < 1E-2
assert abs(result_mean.item() - 0.2_580 ) < 1E-3
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : Any = self.full_loop(prediction_type='''v_prediction''' )
lowerCAmelCase__ : Optional[int] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowerCAmelCase__ : Optional[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 67.3_986 ) < 1E-2
assert abs(result_mean.item() - 0.0_878 ) < 1E-3
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__ , beta_start=0.01 )
lowerCAmelCase__ : Union[str, Any] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowerCAmelCase__ : Optional[int] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 230.0_399 ) < 1E-2
assert abs(result_mean.item() - 0.2_995 ) < 1E-3
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__ , beta_start=0.01 )
lowerCAmelCase__ : str = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowerCAmelCase__ : Tuple = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 186.9_482 ) < 1E-2
assert abs(result_mean.item() - 0.2_434 ) < 1E-3
| 233 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : str = os.path.join(args.tf_model_dir, "parameters.json" )
_UpperCAmelCase : Dict = json.loads(open(_UpperCamelCase ).read() )
if not params:
raise ValueError(
f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(".pt" ):
_UpperCAmelCase : Tuple = args.output + ".pt"
_UpperCAmelCase : Union[str, Any] = OrderedDict()
with tf.device("/CPU:0" ):
_UpperCAmelCase : Any = tf.train.load_checkpoint(args.tf_model_dir )
_UpperCAmelCase : Union[str, Any] = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_UpperCAmelCase : Optional[int] = reader.get_tensor(_UpperCamelCase ).astype(np.floataa )
if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
continue
if key_name.startswith("pasts/" ):
if key_name.startswith("pasts/mlp" ):
_UpperCAmelCase : int = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
_UpperCAmelCase : Any = 8
_UpperCAmelCase : int = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_UpperCAmelCase : Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : List[Any] = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/moe" ):
_UpperCAmelCase : Union[str, Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
_UpperCAmelCase : str = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
_UpperCAmelCase : str = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Tuple = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/softmlp/kernel" ):
_UpperCAmelCase : Any = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
_UpperCAmelCase : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : int = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
_UpperCAmelCase : str = key_name[-9:-7]
for i in range(16 ):
_UpperCAmelCase : Any = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
_UpperCAmelCase : Tuple = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_UpperCAmelCase : Optional[Any] = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/mlp" ):
_UpperCAmelCase : Optional[int] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
_UpperCAmelCase : Tuple = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
_UpperCAmelCase : Any = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : int = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p1/bias" ):
_UpperCAmelCase : str = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : List[Any] = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p2/kernel" ):
_UpperCAmelCase : Dict = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
_UpperCAmelCase : List[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Tuple = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p2/bias" ):
_UpperCAmelCase : Dict = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
_UpperCAmelCase : Tuple = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Any = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/ln" ):
_UpperCAmelCase : int = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : Tuple = "model.blocks.%d.feed_forward.norm.bias" % player
_UpperCAmelCase : Tuple = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Dict = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : Optional[int] = "model.blocks.%d.feed_forward.norm.weight" % player
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : List[str] = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/att" ):
_UpperCAmelCase : List[str] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
_UpperCAmelCase : Union[str, Any] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_UpperCAmelCase : List[str] = state[:, 0, :, :]
_UpperCAmelCase : List[Any] = state[:, 1, :, :]
_UpperCAmelCase : Optional[Any] = state[:, 2, :, :]
_UpperCAmelCase : Any = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Any = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : List[Any] = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[int] = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
_UpperCAmelCase : int = torch.tensor(_UpperCamelCase )
_UpperCAmelCase : Tuple = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
_UpperCAmelCase : Dict = torch.tensor(_UpperCamelCase )
_UpperCAmelCase : Any = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/o/kernel" ):
_UpperCAmelCase : Union[str, Any] = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
_UpperCAmelCase : Dict = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[int] = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/an" ):
_UpperCAmelCase : Optional[Any] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : Tuple = "model.blocks.%d.self_attn.norm.bias" % player
_UpperCAmelCase : Dict = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : List[Any] = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : Any = "model.blocks.%d.self_attn.norm.weight" % player
_UpperCAmelCase : str = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Any = torch.tensor(_UpperCamelCase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
_UpperCAmelCase : Optional[Any] = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
_UpperCAmelCase : Any = "model.%s.weight" % nlayer
_UpperCAmelCase : Any = vnp.copy() # same in embedded
_UpperCAmelCase : Any = torch.tensor(_UpperCamelCase )
if key_name.startswith("model/wte" ):
_UpperCAmelCase : Optional[Any] = "lm_head.weight"
_UpperCAmelCase : Optional[int] = vnp.copy() # same in embedded
_UpperCAmelCase : Dict = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/wob" ):
_UpperCAmelCase : List[str] = "final_logits_bias"
_UpperCAmelCase : Tuple = vnp.copy() # same in embedded
_UpperCAmelCase : Optional[int] = state.reshape((1, -1) )
_UpperCAmelCase : List[Any] = torch.tensor(_UpperCamelCase )
elif key_name == "model/dense/kernel":
_UpperCAmelCase : Tuple = "model.last_project.weight"
_UpperCAmelCase : str = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCamelCase )
elif key_name == "model/dense_1/bias":
_UpperCAmelCase : Union[str, Any] = "model.last_project.bias"
_UpperCAmelCase : Dict = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Dict = torch.tensor(_UpperCamelCase )
torch.save(_UpperCamelCase, args.output )
if __name__ == "__main__":
__a = argparse.ArgumentParser(
description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
__a = parser.parse_args()
convert_tf_gptsan_to_pt(args) | 721 | '''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : int = '''MCTCTFeatureExtractor'''
UpperCamelCase_ : List[str] = '''AutoTokenizer'''
def __init__( self : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Dict ) -> str:
"""simple docstring"""
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = self.feature_extractor
_UpperCAmelCase : List[Any] = False
def __call__( self : List[str] , *lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*lowerCAmelCase__ , **lowerCAmelCase__ )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
_UpperCAmelCase : List[Any] = kwargs.pop("raw_speech" )
else:
_UpperCAmelCase : Union[str, Any] = kwargs.pop("audio" , lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = kwargs.pop("sampling_rate" , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("text" , lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > 0:
_UpperCAmelCase : List[str] = args[0]
_UpperCAmelCase : List[Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
_UpperCAmelCase : Optional[int] = self.feature_extractor(lowerCAmelCase__ , *lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None:
_UpperCAmelCase : Optional[Any] = self.tokenizer(lowerCAmelCase__ , **lowerCAmelCase__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase : Union[str, Any] = encodings["input_ids"]
return inputs
def _lowerCAmelCase ( self : List[Any] , *lowerCAmelCase__ : Union[str, Any] , **lowerCAmelCase__ : Optional[int] ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Dict , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("input_features" , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("labels" , lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > 0:
_UpperCAmelCase : List[str] = args[0]
_UpperCAmelCase : List[str] = args[1:]
if input_features is not None:
_UpperCAmelCase : Dict = self.feature_extractor.pad(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
if labels is not None:
_UpperCAmelCase : List[Any] = self.tokenizer.pad(lowerCAmelCase__ , **lowerCAmelCase__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_UpperCAmelCase : Tuple = labels["input_ids"]
return input_features
def _lowerCAmelCase ( self : Tuple , *lowerCAmelCase__ : Any , **lowerCAmelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@contextmanager
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Any = self.tokenizer
yield
_UpperCAmelCase : Any = self.feature_extractor
_UpperCAmelCase : List[str] = False | 257 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def __magic_name__ ( __snake_case : Tuple ) -> Tuple:
return 1.0 / (1.0 + np.exp(-_outputs ))
def __magic_name__ ( __snake_case : Optional[Any] ) -> int:
lowercase : str = np.max(_outputs , axis=-1 , keepdims=__snake_case )
lowercase : Any = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=__snake_case )
class a__ ( a_ ):
__lowerCAmelCase = """sigmoid"""
__lowerCAmelCase = """softmax"""
__lowerCAmelCase = """none"""
@add_end_docstrings(
a_, r"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""", )
class a__ ( a_ ):
__lowerCAmelCase = False
__lowerCAmelCase = ClassificationFunction.NONE
def __init__( self , **_a ):
super().__init__(**_a )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def __magic_name__ ( self , _a=None , _a=None , _a="" , **_a ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
lowercase : Any = tokenizer_kwargs
lowercase : Dict = {}
if hasattr(self.model.config , "return_all_scores" ) and return_all_scores is None:
lowercase : Dict = self.model.config.return_all_scores
if isinstance(_a , _a ) or top_k is None:
lowercase : List[str] = top_k
lowercase : Tuple = False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`." , _a , )
if return_all_scores:
lowercase : Tuple = None
else:
lowercase : Tuple = 1
if isinstance(_a , _a ):
lowercase : Optional[int] = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
lowercase : Union[str, Any] = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *_a , **_a ):
lowercase : Optional[Any] = super().__call__(*_a , **_a )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
lowercase : Any = "top_k" not in kwargs
if isinstance(args[0] , _a ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def __magic_name__ ( self , _a , **_a ):
lowercase : Union[str, Any] = self.framework
if isinstance(_a , _a ):
return self.tokenizer(**_a , return_tensors=_a , **_a )
elif isinstance(_a , _a ) and len(_a ) == 1 and isinstance(inputs[0] , _a ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=_a , **_a )
elif isinstance(_a , _a ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair." )
return self.tokenizer(_a , return_tensors=_a , **_a )
def __magic_name__ ( self , _a ):
return self.model(**_a )
def __magic_name__ ( self , _a , _a=None , _a=1 , _a=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
lowercase : int = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
lowercase : Tuple = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , "function_to_apply" ) and function_to_apply is None:
lowercase : Optional[Any] = self.model.config.function_to_apply
else:
lowercase : List[Any] = ClassificationFunction.NONE
lowercase : Tuple = model_outputs["logits"][0]
lowercase : Dict = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
lowercase : Optional[Any] = sigmoid(_a )
elif function_to_apply == ClassificationFunction.SOFTMAX:
lowercase : Tuple = softmax(_a )
elif function_to_apply == ClassificationFunction.NONE:
lowercase : str = outputs
else:
raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
lowercase : Union[str, Any] = [
{"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(_a )
]
if not _legacy:
dict_scores.sort(key=lambda _a : x["score"] , reverse=_a )
if top_k is not None:
lowercase : Any = dict_scores[:top_k]
return dict_scores
| 361 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Optional[int] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Any = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
_A : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 361 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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 BertGenerationDecoder, BertGenerationEncoder
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=13 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=99 , UpperCamelCase__=32 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=50 , UpperCamelCase__=0.02 , UpperCamelCase__=True , UpperCamelCase__=None , ) -> Optional[Any]:
'''simple docstring'''
snake_case : str = parent
snake_case : int = batch_size
snake_case : str = seq_length
snake_case : Any = is_training
snake_case : List[str] = use_input_mask
snake_case : str = vocab_size
snake_case : int = hidden_size
snake_case : Optional[int] = num_hidden_layers
snake_case : List[str] = num_attention_heads
snake_case : List[Any] = intermediate_size
snake_case : Dict = hidden_act
snake_case : Any = hidden_dropout_prob
snake_case : Optional[Any] = attention_probs_dropout_prob
snake_case : Optional[int] = max_position_embeddings
snake_case : int = initializer_range
snake_case : Tuple = use_labels
snake_case : int = scope
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case : Tuple = None
if self.use_input_mask:
snake_case : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case : Dict = self.get_config()
return config, input_ids, input_mask, token_labels
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
return BertGenerationConfig(
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 , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
(
(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,
) : Any = self.prepare_config_and_inputs()
snake_case : Dict = True
snake_case : List[str] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ , ) -> List[str]:
'''simple docstring'''
snake_case : str = BertGenerationEncoder(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
snake_case : List[str] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
snake_case : Optional[Any] = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
snake_case : Dict = True
snake_case : Tuple = BertGenerationEncoder(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
snake_case : Optional[int] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , )
snake_case : Tuple = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
snake_case : Optional[int] = True
snake_case : str = True
snake_case : Optional[int] = BertGenerationDecoder(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
# first forward pass
snake_case : Union[str, Any] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ , )
snake_case : Dict = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case : int = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case : int = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case : int = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case : Union[str, Any] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , )["hidden_states"][0]
snake_case : Any = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , )["hidden_states"][0]
# select random slice
snake_case : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case : Optional[int] = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case : Tuple = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , *UpperCamelCase__ , ) -> Tuple:
'''simple docstring'''
snake_case : int = BertGenerationDecoder(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
snake_case : List[str] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case ,snake_case ,snake_case ,snake_case : Union[str, Any] = self.prepare_config_and_inputs()
snake_case : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
__UpperCAmelCase : Tuple = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
__UpperCAmelCase : Any = (BertGenerationDecoder,) if is_torch_available() else ()
__UpperCAmelCase : Union[str, Any] = (
{'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
if is_torch_available()
else {}
)
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : int = BertGenerationEncoderTester(self )
snake_case : str = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case ,snake_case ,snake_case ,snake_case : Dict = self.model_tester.prepare_config_and_inputs()
snake_case : List[Any] = "bert"
self.model_tester.create_and_check_model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase__ )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase__ )
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
(
(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,
) : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case : str = None
self.model_tester.create_and_check_model_as_decoder(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase__ )
@slow
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : str = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : List[str] = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
snake_case : List[str] = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
snake_case : Optional[int] = model(UpperCamelCase__ )[0]
snake_case : Dict = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , UpperCamelCase__ )
snake_case : Any = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Union[str, Any] = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
snake_case : str = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
snake_case : Any = model(UpperCamelCase__ )[0]
snake_case : str = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , UpperCamelCase__ )
snake_case : Dict = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 117 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _lowerCAmelCase ( nn.Module ):
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : int = 1
__UpperCAmelCase : int = 1
__UpperCAmelCase : bool = True
__UpperCAmelCase : bool = False
__UpperCAmelCase : bool = False
__UpperCAmelCase : bool = False
__UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = []
snake_case : Tuple = []
for i in range(self.num_layers ):
snake_case : Any = self.in_channels if i == 0 else self.out_channels
snake_case : Optional[int] = FlaxResnetBlockaD(
in_channels=UpperCamelCase__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(UpperCamelCase__ )
snake_case : Dict = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(UpperCamelCase__ )
snake_case : List[Any] = resnets
snake_case : List[Any] = attentions
if self.add_downsample:
snake_case : Optional[Any] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = ()
for resnet, attn in zip(self.resnets , self.attentions ):
snake_case : str = resnet(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
snake_case : int = attn(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
output_states += (hidden_states,)
if self.add_downsample:
snake_case : Any = self.downsamplers_a(UpperCamelCase__ )
output_states += (hidden_states,)
return hidden_states, output_states
class _lowerCAmelCase ( nn.Module ):
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : int = 1
__UpperCAmelCase : bool = True
__UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : Dict = []
for i in range(self.num_layers ):
snake_case : List[str] = self.in_channels if i == 0 else self.out_channels
snake_case : str = FlaxResnetBlockaD(
in_channels=UpperCamelCase__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(UpperCamelCase__ )
snake_case : str = resnets
if self.add_downsample:
snake_case : str = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True ) -> List[Any]:
'''simple docstring'''
snake_case : Tuple = ()
for resnet in self.resnets:
snake_case : Optional[Any] = resnet(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
output_states += (hidden_states,)
if self.add_downsample:
snake_case : Tuple = self.downsamplers_a(UpperCamelCase__ )
output_states += (hidden_states,)
return hidden_states, output_states
class _lowerCAmelCase ( nn.Module ):
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : int = 1
__UpperCAmelCase : int = 1
__UpperCAmelCase : bool = True
__UpperCAmelCase : bool = False
__UpperCAmelCase : bool = False
__UpperCAmelCase : bool = False
__UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
snake_case : Dict = []
snake_case : Union[str, Any] = []
for i in range(self.num_layers ):
snake_case : Optional[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
snake_case : Union[str, Any] = self.prev_output_channel if i == 0 else self.out_channels
snake_case : Optional[Any] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(UpperCamelCase__ )
snake_case : int = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(UpperCamelCase__ )
snake_case : str = resnets
snake_case : List[Any] = attentions
if self.add_upsample:
snake_case : str = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True ) -> Optional[int]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
snake_case : Dict = res_hidden_states_tuple[-1]
snake_case : Optional[int] = res_hidden_states_tuple[:-1]
snake_case : List[Any] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
snake_case : List[Any] = resnet(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
snake_case : Any = attn(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
if self.add_upsample:
snake_case : int = self.upsamplers_a(UpperCamelCase__ )
return hidden_states
class _lowerCAmelCase ( nn.Module ):
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : int
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : int = 1
__UpperCAmelCase : bool = True
__UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : str = []
for i in range(self.num_layers ):
snake_case : Union[str, Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
snake_case : Optional[Any] = self.prev_output_channel if i == 0 else self.out_channels
snake_case : Dict = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(UpperCamelCase__ )
snake_case : Optional[int] = resnets
if self.add_upsample:
snake_case : Dict = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True ) -> int:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
snake_case : int = res_hidden_states_tuple[-1]
snake_case : List[Any] = res_hidden_states_tuple[:-1]
snake_case : Optional[int] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
snake_case : Dict = resnet(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
if self.add_upsample:
snake_case : Optional[int] = self.upsamplers_a(UpperCamelCase__ )
return hidden_states
class _lowerCAmelCase ( nn.Module ):
__UpperCAmelCase : int
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : int = 1
__UpperCAmelCase : int = 1
__UpperCAmelCase : bool = False
__UpperCAmelCase : bool = False
__UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Dict = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
snake_case : Any = []
for _ in range(self.num_layers ):
snake_case : Union[str, Any] = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(UpperCamelCase__ )
snake_case : List[Any] = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(UpperCamelCase__ )
snake_case : Union[str, Any] = resnets
snake_case : str = attentions
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True ) -> Any:
'''simple docstring'''
snake_case : Optional[Any] = self.resnets[0](UpperCamelCase__ , UpperCamelCase__ )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
snake_case : Union[str, Any] = attn(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
snake_case : Optional[Any] = resnet(UpperCamelCase__ , UpperCamelCase__ , deterministic=UpperCamelCase__ )
return hidden_states
| 117 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = """vision-encoder-decoder"""
_a = True
def __init__( self, **_lowercase ) -> List[str]:
super().__init__(**snake_case_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"""A configuraton of type {self.model_type} cannot be instantiated because """
f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
SCREAMING_SNAKE_CASE_ = kwargs.pop('encoder' )
SCREAMING_SNAKE_CASE_ = encoder_config.pop('model_type' )
SCREAMING_SNAKE_CASE_ = kwargs.pop('decoder' )
SCREAMING_SNAKE_CASE_ = decoder_config.pop('model_type' )
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(snake_case_, **snake_case_ )
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(snake_case_, **snake_case_ )
SCREAMING_SNAKE_CASE_ = True
@classmethod
def a__ ( cls, _lowercase, _lowercase, **_lowercase ) -> PretrainedConfig:
logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **snake_case_ )
def a__ ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.encoder.to_dict()
SCREAMING_SNAKE_CASE_ = self.decoder.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = version.parse("""1.11""" )
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def a__ ( self ) -> float:
return 1E-4
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}} )
class snake_case ( lowercase_ ):
"""simple docstring"""
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
SCREAMING_SNAKE_CASE_ = OrderedDict()
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'encoder_sequence'}
return common_inputs
def a__ ( self, _lowercase, _lowercase = -1, _lowercase = -1, _lowercase = False, _lowercase = None, ) -> Mapping[str, Any]:
import torch
SCREAMING_SNAKE_CASE_ = OrderedDict()
SCREAMING_SNAKE_CASE_ = super().generate_dummy_inputs(
snake_case_, batch_size=snake_case_, seq_length=snake_case_, is_pair=snake_case_, framework=snake_case_ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = dummy_input['input_ids'].shape
SCREAMING_SNAKE_CASE_ = (batch, encoder_sequence, self._config.encoder_hidden_size)
SCREAMING_SNAKE_CASE_ = dummy_input.pop('input_ids' )
SCREAMING_SNAKE_CASE_ = dummy_input.pop('attention_mask' )
SCREAMING_SNAKE_CASE_ = torch.zeros(snake_case_ )
return common_inputs
class snake_case ( lowercase_ ):
"""simple docstring"""
@property
def a__ ( self ) -> None:
pass
def a__ ( self, _lowercase ) -> OnnxConfig:
return VisionEncoderDecoderEncoderOnnxConfig(snake_case_ )
def a__ ( self, _lowercase, _lowercase, _lowercase = "default" ) -> OnnxConfig:
SCREAMING_SNAKE_CASE_ = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(snake_case_, snake_case_ )
| 294 |
'''simple docstring'''
from torch import nn
class A ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ ) -> List[Any]:
super().__init__()
_a = class_size
_a = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
_a = nn.Linear(snake_case_ , snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> Tuple:
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
_a = self.mlp(snake_case_ )
return logits
| 131 | 0 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
_lowerCamelCase = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def snake_case_ ( self , __A , __A , __A ):
__a = AudioClassificationPipeline(model=__A , feature_extractor=__A )
# test with a raw waveform
__a = np.zeros((34000,) )
__a = np.zeros((14000,) )
return audio_classifier, [audioa, audio]
def snake_case_ ( self , __A , __A ):
__a , __a = examples
__a = audio_classifier(__A )
# by default a model is initialized with num_labels=2
self.assertEqual(
__A , [
{"""score""": ANY(__A ), """label""": ANY(__A )},
{"""score""": ANY(__A ), """label""": ANY(__A )},
] , )
__a = audio_classifier(__A , top_k=1 )
self.assertEqual(
__A , [
{"""score""": ANY(__A ), """label""": ANY(__A )},
] , )
self.run_torchaudio(__A )
@require_torchaudio
def snake_case_ ( self , __A ):
import datasets
# test with a local file
__a = datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
__a = dataset[0]["""audio"""]["""array"""]
__a = audio_classifier(__A )
self.assertEqual(
__A , [
{"""score""": ANY(__A ), """label""": ANY(__A )},
{"""score""": ANY(__A ), """label""": ANY(__A )},
] , )
@require_torch
def snake_case_ ( self ):
__a = """anton-l/wav2vec2-random-tiny-classifier"""
__a = pipeline("""audio-classification""" , model=__A )
__a = np.ones((8000,) )
__a = audio_classifier(__A , top_k=4 )
__a = [
{"""score""": 0.0842, """label""": """no"""},
{"""score""": 0.0838, """label""": """up"""},
{"""score""": 0.0837, """label""": """go"""},
{"""score""": 0.0834, """label""": """right"""},
]
__a = [
{"""score""": 0.0845, """label""": """stop"""},
{"""score""": 0.0844, """label""": """on"""},
{"""score""": 0.0841, """label""": """right"""},
{"""score""": 0.0834, """label""": """left"""},
]
self.assertIn(nested_simplify(__A , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
__a = {"""array""": np.ones((8000,) ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
__a = audio_classifier(__A , top_k=4 )
self.assertIn(nested_simplify(__A , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def snake_case_ ( self ):
import datasets
__a = """superb/wav2vec2-base-superb-ks"""
__a = pipeline("""audio-classification""" , model=__A )
__a = datasets.load_dataset("""anton-l/superb_dummy""" , """ks""" , split="""test""" )
__a = np.array(dataset[3]["""speech"""] , dtype=np.floataa )
__a = audio_classifier(__A , top_k=4 )
self.assertEqual(
nested_simplify(__A , decimals=3 ) , [
{"""score""": 0.981, """label""": """go"""},
{"""score""": 0.007, """label""": """up"""},
{"""score""": 0.006, """label""": """_unknown_"""},
{"""score""": 0.001, """label""": """down"""},
] , )
@require_tf
@unittest.skip("""Audio classification is not implemented for TF""" )
def snake_case_ ( self ):
pass
| 209 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
SCREAMING_SNAKE_CASE = ''
if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
class __UpperCAmelCase ( tr.AbstractTransform ):
"""simple docstring"""
def __init__( self , __A = " " ):
__a = sentence_delimiter
def snake_case_ ( self , __A ):
return list(__A )
def snake_case_ ( self , __A ):
__a = []
for sent_idx, sentence in enumerate(__A ):
chars.extend(self.process_string(__A ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A ) - 1:
chars.append(self.sentence_delimiter )
return chars
SCREAMING_SNAKE_CASE = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
SCREAMING_SNAKE_CASE = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
SCREAMING_SNAKE_CASE = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
SCREAMING_SNAKE_CASE = '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'
SCREAMING_SNAKE_CASE = '\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> cer = datasets.load_metric("cer")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCAmelCase ( datasets.Metric ):
"""simple docstring"""
def snake_case_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
"""https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
] , )
def snake_case_ ( self , __A , __A , __A=False ):
if concatenate_texts:
return jiwer.compute_measures(
__A , __A , truth_transform=__A , hypothesis_transform=__A , )["wer"]
__a = 0
__a = 0
for prediction, reference in zip(__A , __A ):
__a = jiwer.compute_measures(
__A , __A , truth_transform=__A , hypothesis_transform=__A , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 209 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def __a ( _UpperCamelCase: List[Any]="ro" , _UpperCamelCase: Any="en" , _UpperCamelCase: Optional[Any]="wmt16" , _UpperCamelCase: Optional[int]=None ) -> List[Any]:
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
_snake_case = F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
_snake_case = datasets.load_dataset(_UpperCamelCase , _UpperCamelCase )
if save_dir is None:
_snake_case = F"""{dataset}-{pair}"""
_snake_case = Path(_UpperCamelCase )
save_dir.mkdir(exist_ok=_UpperCamelCase )
for split in ds.keys():
print(F"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
_snake_case = "val" if split == "validation" else split
_snake_case = save_dir.joinpath(F"""{fn}.source""" )
_snake_case = save_dir.joinpath(F"""{fn}.target""" )
_snake_case = src_path.open("w+" )
_snake_case = tgt_path.open("w+" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
_snake_case = x["translation"]
src_fp.write(ex[src_lang] + "\n" )
tgt_fp.write(ex[tgt_lang] + "\n" )
print(F"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 185 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str=None ) -> int:
"""simple docstring"""
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = None
def _a ( self : int , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=_lowerCAmelCase , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=torch.floataa ) -> Tuple:
"""simple docstring"""
__lowercase = torch.empty(_lowerCAmelCase , dtype=_lowerCAmelCase )
dist.scatter(_lowerCAmelCase , src=0 , scatter_list=_lowerCAmelCase , group=self.process_group )
return target_tensor
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("""e""" )) , _lowerCAmelCase )
return ifname
def _a ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(_lowerCAmelCase )]
dist.gather(torch.tensor(_lowerCAmelCase ) , dst=0 , gather_list=_lowerCAmelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(_lowerCAmelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(_lowerCAmelCase ).numpy() , _lowerCAmelCase )
__lowercase , __lowercase = torch.tensor(_lowerCAmelCase ), torch.tensor(_lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowerCAmelCase )
| 80 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 )]),
({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(_UpperCamelCase , i + 1 ) for i in range(10 )]),
({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1 )]),
({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Optional[Any] = _distribute_shards(**_UpperCamelCase )
assert out == expected
@pytest.mark.parametrize(
'''gen_kwargs, max_num_jobs, expected''' , [
({'''foo''': 0}, 10, [{'''foo''': 0}]),
({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
] , )
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Optional[Any] = _split_gen_kwargs(_UpperCamelCase , _UpperCamelCase )
assert out == expected
@pytest.mark.parametrize(
'''gen_kwargs, expected''' , [
({'''foo''': 0}, 1),
({'''shards''': [0]}, 1),
({'''shards''': [0, 1, 2, 3]}, 4),
({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
] , )
def _A ( _UpperCamelCase , _UpperCamelCase ):
if expected is RuntimeError:
with pytest.raises(_UpperCamelCase ):
_number_of_shards_in_gen_kwargs(_UpperCamelCase )
else:
_UpperCAmelCase : Any = _number_of_shards_in_gen_kwargs(_UpperCamelCase )
assert out == expected
| 416 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase_ :
def __init__( self : Any , UpperCAmelCase_ : Collection[float] | None = None ) -> None:
'''simple docstring'''
if components is None:
_UpperCAmelCase : Optional[Any] = []
_UpperCAmelCase : Any = list(UpperCAmelCase_ )
def __len__( self : Tuple ) -> int:
'''simple docstring'''
return len(self.__components )
def __str__( self : List[Any] ) -> str:
'''simple docstring'''
return "(" + ",".join(map(UpperCAmelCase_ , self.__components ) ) + ")"
def __add__( self : List[str] , UpperCAmelCase_ : Vector ) -> Vector:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = len(self )
if size == len(UpperCAmelCase_ ):
_UpperCAmelCase : List[str] = [self.__components[i] + other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )]
return Vector(UpperCAmelCase_ )
else:
raise Exception('''must have the same size''' )
def __sub__( self : Optional[int] , UpperCAmelCase_ : Vector ) -> Vector:
'''simple docstring'''
_UpperCAmelCase : str = len(self )
if size == len(UpperCAmelCase_ ):
_UpperCAmelCase : Any = [self.__components[i] - other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )]
return Vector(UpperCAmelCase_ )
else: # error case
raise Exception('''must have the same size''' )
@overload
def __mul__( self : List[Any] , UpperCAmelCase_ : float ) -> Vector:
'''simple docstring'''
...
@overload
def __mul__( self : Dict , UpperCAmelCase_ : Vector ) -> float:
'''simple docstring'''
...
def __mul__( self : Dict , UpperCAmelCase_ : float | Vector ) -> float | Vector:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , (float, int) ):
_UpperCAmelCase : Dict = [c * other for c in self.__components]
return Vector(UpperCAmelCase_ )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and len(self ) == len(UpperCAmelCase_ ):
_UpperCAmelCase : Optional[int] = len(self )
_UpperCAmelCase : Optional[Any] = [self.__components[i] * other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )]
return sum(UpperCAmelCase_ )
else: # error case
raise Exception('''invalid operand!''' )
def a_ ( self : Optional[int] ) -> Vector:
'''simple docstring'''
return Vector(self.__components )
def a_ ( self : Optional[int] , UpperCAmelCase_ : int ) -> float:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('''index out of range''' )
def a_ ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : float ) -> None:
'''simple docstring'''
assert -len(self.__components ) <= pos < len(self.__components )
_UpperCAmelCase : Optional[Any] = value
def a_ ( self : List[str] ) -> float:
'''simple docstring'''
if len(self.__components ) == 0:
raise Exception('''Vector is empty''' )
_UpperCAmelCase : str = [c**2 for c in self.__components]
return math.sqrt(sum(UpperCAmelCase_ ) )
def a_ ( self : Optional[int] , UpperCAmelCase_ : Vector , UpperCAmelCase_ : bool = False ) -> float:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = self * other
_UpperCAmelCase : Union[str, Any] = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def _A ( _UpperCamelCase ):
assert isinstance(_UpperCamelCase , _UpperCamelCase )
return Vector([0] * dimension )
def _A ( _UpperCamelCase , _UpperCamelCase ):
assert isinstance(_UpperCamelCase , _UpperCamelCase ) and (isinstance(_UpperCamelCase , _UpperCamelCase ))
_UpperCAmelCase : Optional[Any] = [0] * dimension
_UpperCAmelCase : Optional[Any] = 1
return Vector(_UpperCamelCase )
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
assert (
isinstance(_UpperCamelCase , _UpperCamelCase )
and isinstance(_UpperCamelCase , _UpperCamelCase )
and (isinstance(_UpperCamelCase , (int, float) ))
)
return x * scalar + y
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
random.seed(_UpperCamelCase )
_UpperCAmelCase : Tuple = [random.randint(_UpperCamelCase , _UpperCamelCase ) for _ in range(_UpperCamelCase )]
return Vector(_UpperCamelCase )
class lowerCAmelCase_ :
def __init__( self : int , UpperCAmelCase_ : list[list[float]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> None:
'''simple docstring'''
_UpperCAmelCase : Tuple = matrix
_UpperCAmelCase : Tuple = w
_UpperCAmelCase : Tuple = h
def __str__( self : str ) -> str:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = ''''''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self : str , UpperCAmelCase_ : Matrix ) -> Matrix:
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
_UpperCAmelCase : Optional[int] = []
for i in range(self.__height ):
_UpperCAmelCase : Union[str, Any] = [
self.__matrix[i][j] + other.component(UpperCAmelCase_ , UpperCAmelCase_ )
for j in range(self.__width )
]
matrix.append(UpperCAmelCase_ )
return Matrix(UpperCAmelCase_ , self.__width , self.__height )
else:
raise Exception('''matrix must have the same dimension!''' )
def __sub__( self : Dict , UpperCAmelCase_ : Matrix ) -> Matrix:
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
_UpperCAmelCase : Optional[Any] = []
for i in range(self.__height ):
_UpperCAmelCase : Union[str, Any] = [
self.__matrix[i][j] - other.component(UpperCAmelCase_ , UpperCAmelCase_ )
for j in range(self.__width )
]
matrix.append(UpperCAmelCase_ )
return Matrix(UpperCAmelCase_ , self.__width , self.__height )
else:
raise Exception('''matrices must have the same dimension!''' )
@overload
def __mul__( self : int , UpperCAmelCase_ : float ) -> Matrix:
'''simple docstring'''
...
@overload
def __mul__( self : Optional[Any] , UpperCAmelCase_ : Vector ) -> Vector:
'''simple docstring'''
...
def __mul__( self : List[str] , UpperCAmelCase_ : float | Vector ) -> Vector | Matrix:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ): # matrix-vector
if len(UpperCAmelCase_ ) == self.__width:
_UpperCAmelCase : int = zero_vector(self.__height )
for i in range(self.__height ):
_UpperCAmelCase : Optional[int] = [
self.__matrix[i][j] * other.component(UpperCAmelCase_ )
for j in range(self.__width )
]
ans.change_component(UpperCAmelCase_ , sum(UpperCAmelCase_ ) )
return ans
else:
raise Exception(
'''vector must have the same size as the '''
'''number of columns of the matrix!''' )
elif isinstance(UpperCAmelCase_ , (int, float) ): # matrix-scalar
_UpperCAmelCase : str = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(UpperCAmelCase_ , self.__width , self.__height )
return None
def a_ ( self : Optional[int] ) -> int:
'''simple docstring'''
return self.__height
def a_ ( self : Tuple ) -> int:
'''simple docstring'''
return self.__width
def a_ ( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float:
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('''change_component: indices out of bounds''' )
def a_ ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float ) -> None:
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
_UpperCAmelCase : Dict = value
else:
raise Exception('''change_component: indices out of bounds''' )
def a_ ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
_UpperCAmelCase : Any = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(UpperCAmelCase_ ) ):
_UpperCAmelCase : Dict = minor[i][:y] + minor[i][y + 1 :]
return Matrix(UpperCAmelCase_ , self.__width - 1 , self.__height - 1 ).determinant()
def a_ ( self : Any , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(UpperCAmelCase_ , UpperCAmelCase_ )
else:
raise Exception('''Indices out of bounds''' )
def a_ ( self : Optional[int] ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if self.__height < 1:
raise Exception('''Matrix has no element''' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
_UpperCAmelCase : Optional[int] = [
self.__matrix[0][y] * self.cofactor(0 , UpperCAmelCase_ ) for y in range(self.__width )
]
return sum(UpperCAmelCase_ )
def _A ( _UpperCamelCase ):
_UpperCAmelCase : list[list[float]] = [[0] * n for _ in range(_UpperCamelCase )]
return Matrix(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
random.seed(_UpperCamelCase )
_UpperCAmelCase : list[list[float]] = [
[random.randint(_UpperCamelCase , _UpperCamelCase ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )
]
return Matrix(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
| 416 | 1 |
"""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
a : int = logging.getLogger(__name__)
a : str = '''pytorch_model.bin'''
@dataclasses.dataclass
class a_ :
a : str = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
a : Optional[str] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class a_ :
a : str = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
a : str = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
a : Optional[str] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'A csv or a json file containing the validation data.'} )
a : Optional[str] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'The name of the task to train on.'} , )
a : Optional[List[str]] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class a_ :
a : str = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
a : Optional[str] = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
a : Optional[str] = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
a : Optional[int] = dataclasses.field(
default=10 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
a : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
a : Optional[bool] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
a : Optional[bool] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
a : Optional[bool] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
a : Optional[float] = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
a : Optional[int] = dataclasses.field(
default=100 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
a : Optional[int] = dataclasses.field(
default=_UpperCAmelCase , metadata={'help': 'Random seed for initialization.'} , )
def _UpperCamelCase ( _A , _A , _A , _A , _A , _A ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_UpperCAmelCase = dataset.filter(lambda _A : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_UpperCAmelCase = int(eval_result * len(_A ) )
print(_A )
_UpperCAmelCase = dataset.sort("""probability""" , reverse=_A )
_UpperCAmelCase = dataset.select(range(_A ) )
_UpperCAmelCase = dataset.remove_columns(["""label""", """probability"""] )
_UpperCAmelCase = dataset.rename_column("""prediction""" , """label""" )
_UpperCAmelCase = dataset.map(lambda _A : {"label": idalabel[example["label"]]} )
_UpperCAmelCase = dataset.shuffle(seed=args.seed )
_UpperCAmelCase = os.path.join(_A , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(_A , index=_A )
else:
dataset.to_json(_A )
def _UpperCamelCase ( _A , _A , _A , _A , **_A ) -> int:
"""simple docstring"""
_UpperCAmelCase = 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()
_UpperCAmelCase = STModelArguments(model_name_or_path=_A )
_UpperCAmelCase = STDataArguments(train_file=_A , infer_file=_A )
_UpperCAmelCase = STTrainingArguments(output_dir=_A )
_UpperCAmelCase = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_A ).items():
setattr(_A , _A , _A )
for key, value in kwargs.items():
if hasattr(_A , _A ):
setattr(_A , _A , _A )
# Sanity checks
_UpperCAmelCase = {}
_UpperCAmelCase = 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
_UpperCAmelCase = args.train_file
_UpperCAmelCase = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_UpperCAmelCase = args.eval_file
for key in data_files:
_UpperCAmelCase = 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:
_UpperCAmelCase = 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...""" )
_UpperCAmelCase = F"""{args.output_dir}/self-train_iter-{{}}""".format
_UpperCAmelCase = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_A )
os.makedirs(_A , exist_ok=_A )
accelerator.wait_for_everyone()
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = False
# Show the progress bar
_UpperCAmelCase = 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 ) ):
_UpperCAmelCase = data_dir_format(_A )
assert os.path.exists(_A )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_UpperCAmelCase = os.path.join(_A , """stage-1""" )
_UpperCAmelCase = {
"""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(_A , _A ):
arguments_dict.update({key: value} )
_UpperCAmelCase = os.path.join(_A , """best-checkpoint""" , _A )
if os.path.exists(_A ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , _A , _A , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , _A )
finetune(**_A )
accelerator.wait_for_everyone()
assert os.path.exists(_A )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , _A )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_UpperCAmelCase = os.path.join(_A , """best-checkpoint""" )
_UpperCAmelCase = os.path.join(_A , """stage-2""" )
# Update arguments_dict
_UpperCAmelCase = model_path
_UpperCAmelCase = data_files["""train"""]
_UpperCAmelCase = current_output_dir
_UpperCAmelCase = os.path.join(_A , """best-checkpoint""" , _A )
if os.path.exists(_A ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , _A , _A , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , _A )
finetune(**_A )
accelerator.wait_for_everyone()
assert os.path.exists(_A )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , _A )
_UpperCAmelCase = iteration
_UpperCAmelCase = data_dir_format(iteration + 1 )
_UpperCAmelCase = AutoConfig.from_pretrained(os.path.join(_A , """best-checkpoint""" ) )
_UpperCAmelCase = config.idalabel
_UpperCAmelCase = os.path.join(_A , """eval_results_best-checkpoint.json""" )
_UpperCAmelCase = os.path.join(_A , """test_results_best-checkpoint.json""" )
assert os.path.exists(_A )
with open(_A , """r""" ) as f:
_UpperCAmelCase = float(json.load(_A )[args.eval_metric] )
_UpperCAmelCase = os.path.join(_A , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(_A )
# Loading the dataset from local csv or json files.
_UpperCAmelCase = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_UpperCAmelCase = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(_A , exist_ok=_A )
shutil.copy(_A , os.path.join(_A , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(_A ):
shutil.copy(_A , os.path.join(_A , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(_A , _A , _A , _A , _A , _A )
accelerator.wait_for_everyone()
_UpperCAmelCase = os.path.join(_A , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_UpperCAmelCase = eval_result
if best_iteration is None:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
_UpperCAmelCase = 0
else:
if new_eval_result == best_eval_result:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_UpperCAmelCase = 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""" , _A )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , _A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_A , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(_A , """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 , _A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_A , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(_A , """eval_results_best-iteration.json""" ) , ) | 555 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a : Dict = logging.get_logger(__name__)
# TODO: upload to AWS
a : Tuple = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
),
}
class a_ ( _UpperCAmelCase ):
a : int = 'retribert'
def __init__( self : Optional[Any] , __UpperCamelCase : Optional[Any]=3_05_22 , __UpperCamelCase : int=7_68 , __UpperCamelCase : Any=8 , __UpperCamelCase : Dict=12 , __UpperCamelCase : List[Any]=30_72 , __UpperCamelCase : List[Any]="gelu" , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : Union[str, Any]=5_12 , __UpperCamelCase : Dict=2 , __UpperCamelCase : Tuple=0.0_2 , __UpperCamelCase : List[str]=1e-12 , __UpperCamelCase : int=True , __UpperCamelCase : str=1_28 , __UpperCamelCase : Union[str, Any]=0 , **__UpperCamelCase : Any , ) ->List[Any]:
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , **__UpperCamelCase )
_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 = share_encoders
_UpperCAmelCase = projection_dim | 555 | 1 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ = 2_000_000 ):
A_ : List[str] = [0 for i in range(n + 1 )]
A_ : Dict = 1
A_ : Any = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , snake_case__ ):
A_ : Any = 1
A_ : Tuple = 0
for i in range(snake_case__ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'{solution() = }')
| 480 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase(self ):
A_ : Optional[int] = get_activation("""swish""" )
self.assertIsInstance(lowerCAmelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase(self ):
A_ : Dict = get_activation("""silu""" )
self.assertIsInstance(lowerCAmelCase_ , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase(self ):
A_ : Optional[Any] = get_activation("""mish""" )
self.assertIsInstance(lowerCAmelCase_ , nn.Mish )
self.assertEqual(act(torch.tensor(-200 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def lowerCamelCase(self ):
A_ : int = get_activation("""gelu""" )
self.assertIsInstance(lowerCAmelCase_ , nn.GELU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 480 | 1 |
'''simple docstring'''
import os
import sys
import unittest
UpperCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase_ = os.path.join(git_repo_path, 'src', 'transformers')
UpperCAmelCase_ = '\n{0} = None\n'
UpperCAmelCase_ = '\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n'
UpperCAmelCase_ = '\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n'
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = find_backend(""" _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")""" )
self.assertIsNone(snake_case_ )
UpperCAmelCase__ = find_backend(""" if not is_tokenizers_available():""" )
self.assertEqual(snake_case_ , """tokenizers""" )
UpperCAmelCase__ = find_backend(""" if not is_tensorflow_text_available():""" )
self.assertEqual(snake_case_ , """tensorflow_text""" )
UpperCAmelCase__ = find_backend(""" if not (is_sentencepiece_available() and is_tokenizers_available()):""" )
self.assertEqual(snake_case_ , """sentencepiece_and_tokenizers""" )
UpperCAmelCase__ = find_backend(
""" if not (is_sentencepiece_available() and is_tensorflow_text_available()):""" )
self.assertEqual(snake_case_ , """sentencepiece_and_tensorflow_text""" )
UpperCAmelCase__ = find_backend(
""" if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):""" )
self.assertEqual(snake_case_ , """sentencepiece_and_tokenizers_and_vision""" )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""" , snake_case_ )
self.assertIn("""tensorflow_text""" , snake_case_ )
self.assertIn("""sentencepiece_and_tokenizers""" , snake_case_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""BertModel""" , objects["""torch"""] )
self.assertIn("""TFBertModel""" , objects["""tf"""] )
self.assertIn("""FlaxBertModel""" , objects["""flax"""] )
self.assertIn("""BertModel""" , objects["""torch"""] )
self.assertIn("""TFBertTokenizer""" , objects["""tensorflow_text"""] )
self.assertIn("""convert_slow_tokenizer""" , objects["""sentencepiece_and_tokenizers"""] )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = create_dummy_object("""CONSTANT""" , """'torch'""" )
self.assertEqual(snake_case_ , """\nCONSTANT = None\n""" )
UpperCAmelCase__ = create_dummy_object("""function""" , """'torch'""" )
self.assertEqual(
snake_case_ , """\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
UpperCAmelCase__ = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
"""
UpperCAmelCase__ = create_dummy_object("""FakeClass""" , """'torch'""" )
self.assertEqual(snake_case_ , snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
"""
UpperCAmelCase__ = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""] , snake_case_ )
| 603 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {"vocab_file": "sentencepiece.model"}
_UpperCamelCase = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
_UpperCamelCase = {
"google/rembert": 2_56,
}
class __UpperCAmelCase (__A ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , snake_case_ , snake_case_=False , snake_case_=True , snake_case_=True , snake_case_="[CLS]" , snake_case_="[SEP]" , snake_case_="[UNK]" , snake_case_="[SEP]" , snake_case_="[PAD]" , snake_case_="[CLS]" , snake_case_="[MASK]" , **snake_case_ , ):
'''simple docstring'''
super().__init__(
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_ , )
A__ : List[Any] = do_lower_case
A__ : Dict = remove_space
A__ : Optional[int] = keep_accents
A__ : Tuple = vocab_file
A__ : List[str] = spm.SentencePieceProcessor()
self.sp_model.Load(snake_case_ )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def lowerCamelCase ( self ):
'''simple docstring'''
A__ : Optional[Any] = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
'''simple docstring'''
A__ : str = self.__dict__.copy()
A__ : Tuple = None
return state
def __setstate__( self , snake_case_ ):
'''simple docstring'''
A__ : Dict = d
A__ : int = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def lowerCamelCase ( self , snake_case_ , snake_case_=False ):
'''simple docstring'''
A__ : Tuple = self.sp_model.EncodeAsPieces(snake_case_ )
return pieces
def lowerCamelCase ( self , snake_case_ ):
'''simple docstring'''
return self.sp_model.PieceToId(snake_case_ )
def lowerCamelCase ( self , snake_case_ ):
'''simple docstring'''
return self.sp_model.IdToPiece(snake_case_ )
def lowerCamelCase ( self , snake_case_ ):
'''simple docstring'''
A__ : str = self.sp_model.decode_pieces(snake_case_ )
return out_string
def lowerCamelCase ( self , snake_case_ , snake_case_ = None ):
'''simple docstring'''
A__ : List[Any] = [self.sep_token_id]
A__ : str = [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 , snake_case_ , snake_case_ = None , snake_case_ = 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(snake_case_ )) + [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1]
def lowerCamelCase ( self , snake_case_ , snake_case_ = None ):
'''simple docstring'''
A__ : int = [self.sep_token_id]
A__ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase ( self , snake_case_ , snake_case_ = None ):
'''simple docstring'''
if not os.path.isdir(snake_case_ ):
logger.error("""Vocabulary path ({}) should be a directory""".format(snake_case_ ) )
return
A__ : Any = os.path.join(
snake_case_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 363 | 0 |
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 lowerCAmelCase_ :
@property
def UpperCamelCase_ ( self : Optional[int] ):
return self.get_dummy_input()
@property
def UpperCamelCase_ ( self : Dict ):
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 UpperCamelCase_ ( self : Union[str, Any] , _A : List[str]=True , _A : Any=False , _A : Union[str, Any]=False , _A : int=False , ):
_UpperCamelCase = 4
_UpperCamelCase = 32
_UpperCamelCase = (32, 32)
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = torch.device(_A )
_UpperCamelCase = (batch_size, num_channels) + sizes
_UpperCamelCase = randn_tensor(_A , generator=_A , device=_A )
_UpperCamelCase = {'''hidden_states''': hidden_states}
if include_temb:
_UpperCamelCase = 128
_UpperCamelCase = randn_tensor((batch_size, temb_channels) , generator=_A , device=_A )
if include_res_hidden_states_tuple:
_UpperCamelCase = torch.manual_seed(1 )
_UpperCamelCase = (randn_tensor(_A , generator=_A , device=_A ),)
if include_encoder_hidden_states:
_UpperCamelCase = floats_tensor((batch_size, 32, 32) ).to(_A )
if include_skip_sample:
_UpperCamelCase = randn_tensor(((batch_size, 3) + sizes) , generator=_A , device=_A )
return dummy_input
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = {
'''in_channels''': 32,
'''out_channels''': 32,
'''temb_channels''': 128,
}
if self.block_type == "up":
_UpperCamelCase = 32
if self.block_type == "mid":
init_dict.pop('''out_channels''' )
_UpperCamelCase = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase_ ( self : Tuple , _A : Union[str, Any] ):
_UpperCamelCase , _UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**_A )
unet_block.to(_A )
unet_block.eval()
with torch.no_grad():
_UpperCamelCase = unet_block(**_A )
if isinstance(_A , _A ):
_UpperCamelCase = output[0]
self.assertEqual(output.shape , self.output_shape )
_UpperCamelCase = output[0, -1, -3:, -3:]
_UpperCamelCase = torch.tensor(_A ).to(_A )
assert torch_all_close(output_slice.flatten() , _A , atol=5e-3 )
@unittest.skipIf(torch_device == '''mps''' , '''Training is not supported in mps''' )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase , _UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**_A )
model.to(_A )
model.train()
_UpperCamelCase = model(**_A )
if isinstance(_A , _A ):
_UpperCamelCase = output[0]
_UpperCamelCase = torch.device(_A )
_UpperCamelCase = randn_tensor(output.shape , device=_A )
_UpperCamelCase = torch.nn.functional.mse_loss(_A , _A )
loss.backward()
| 713 | from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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 (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowerCAmelCase_ :
def __init__( self : Optional[Any] , _A : Optional[Any] , _A : List[str]=13 , _A : Union[str, Any]=7 , _A : int=True , _A : Optional[int]=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : Optional[int]=99 , _A : Union[str, Any]=32 , _A : Dict=2 , _A : List[Any]=4 , _A : Optional[Any]=37 , _A : int="gelu" , _A : Optional[int]=0.1 , _A : str=0.1 , _A : List[str]=512 , _A : Optional[Any]=16 , _A : Optional[Any]=2 , _A : Optional[int]=0.02 , _A : str=False , _A : int=True , _A : Any="None" , _A : Dict=3 , _A : List[Any]=4 , _A : Optional[Any]=None , ):
_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 = relative_attention
_UpperCamelCase = position_biased_input
_UpperCamelCase = pos_att_type
_UpperCamelCase = scope
def UpperCamelCase_ ( self : Dict ):
_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 = DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=_A , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase_ ( self : Dict , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : Optional[int] , _A : int , _A : Optional[Any] ):
_UpperCamelCase = TFDebertaVaModel(config=_A )
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCamelCase = [input_ids, input_mask]
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Dict , _A : Optional[int] , _A : Any , _A : Dict , _A : Union[str, Any] , _A : Union[str, Any] , _A : List[Any] , _A : List[str] ):
_UpperCamelCase = TFDebertaVaForMaskedLM(config=_A )
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Dict , _A : Dict , _A : List[str] , _A : List[Any] , _A : List[Any] , _A : Optional[Any] , _A : Tuple , _A : int ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFDebertaVaForSequenceClassification(config=_A )
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : Optional[int] , _A : Any , _A : List[Any] , _A : Dict , _A : Union[str, Any] , _A : List[str] ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFDebertaVaForTokenClassification(config=_A )
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Dict , _A : Optional[Any] , _A : Optional[int] , _A : Any , _A : List[str] , _A : str , _A : Optional[int] , _A : str ):
_UpperCamelCase = TFDebertaVaForQuestionAnswering(config=_A )
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Any ):
_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_tf
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": TFDebertaVaModel,
"fill-mask": TFDebertaVaForMaskedLM,
"question-answering": TFDebertaVaForQuestionAnswering,
"text-classification": TFDebertaVaForSequenceClassification,
"token-classification": TFDebertaVaForTokenClassification,
"zero-shot": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = TFDebertaVaModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Any ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_A )
@slow
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
self.assertIsNotNone(_A )
@require_tf
class lowerCAmelCase_ ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def UpperCamelCase_ ( self : List[Any] ):
pass
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
_UpperCamelCase = tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
_UpperCamelCase = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_UpperCamelCase = model(_A , attention_mask=_A )[0]
_UpperCamelCase = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , _A , atol=1e-4 )
| 71 | 0 |
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 351 |
"""simple docstring"""
from typing import Any
def __lowerCamelCase ( __UpperCamelCase ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
lowerCAmelCase_ : Any = [input_list.count(__UpperCamelCase ) for value in input_list]
lowerCAmelCase_ : List[Any] = max(__UpperCamelCase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(__UpperCamelCase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 610 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase ( __snake_case , __snake_case , __snake_case ):
__lowerCamelCase = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = None , __lowerCamelCase = 5_02_57 , __lowerCamelCase = 10_24 , __lowerCamelCase = 7_68 , __lowerCamelCase = 12 , __lowerCamelCase = 12 , __lowerCamelCase = None , __lowerCamelCase = "gelu_new" , __lowerCamelCase = 0.1 , __lowerCamelCase = 0.1 , __lowerCamelCase = 0.1 , __lowerCamelCase = 1e-5 , __lowerCamelCase = 0.02 , __lowerCamelCase = True , __lowerCamelCase = True , __lowerCamelCase = False , __lowerCamelCase = False , ) -> Tuple:
'''simple docstring'''
super().__init__()
snake_case: int = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
F" `n_embd`: {n_embd} are not equal." )
snake_case: Any = prefix_inner_dim
snake_case: Union[str, Any] = prefix_hidden_dim
snake_case: List[str] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
snake_case: int = (
nn.Linear(self.prefix_hidden_dim , __lowerCamelCase ) if self.prefix_hidden_dim is not None else nn.Identity()
)
snake_case: Tuple = GPTaConfig(
vocab_size=__lowerCamelCase , n_positions=__lowerCamelCase , n_embd=__lowerCamelCase , n_layer=__lowerCamelCase , n_head=__lowerCamelCase , n_inner=__lowerCamelCase , activation_function=__lowerCamelCase , resid_pdrop=__lowerCamelCase , embd_pdrop=__lowerCamelCase , attn_pdrop=__lowerCamelCase , layer_norm_epsilon=__lowerCamelCase , initializer_range=__lowerCamelCase , scale_attn_weights=__lowerCamelCase , use_cache=__lowerCamelCase , scale_attn_by_inverse_layer_idx=__lowerCamelCase , reorder_and_upcast_attn=__lowerCamelCase , )
snake_case: Any = GPTaLMHeadModel(__lowerCamelCase )
def lowerCAmelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = None , __lowerCamelCase = None , ) -> str:
'''simple docstring'''
snake_case: Optional[Any] = self.transformer.transformer.wte(__lowerCamelCase )
snake_case: Dict = self.encode_prefix(__lowerCamelCase )
snake_case: str = self.decode_prefix(__lowerCamelCase )
snake_case: Tuple = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
snake_case: Union[str, Any] = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
snake_case: Optional[Any] = torch.cat((dummy_token, input_ids) , dim=1 )
snake_case: Any = self.transformer(inputs_embeds=__lowerCamelCase , labels=__lowerCamelCase , attention_mask=__lowerCamelCase )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def lowerCAmelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> torch.Tensor:
'''simple docstring'''
return torch.zeros(__lowerCamelCase , self.prefix_length , dtype=torch.intaa , device=__lowerCamelCase )
def lowerCAmelCase_ ( self , __lowerCamelCase ) -> Dict:
'''simple docstring'''
return self.encode_prefix(__lowerCamelCase )
@torch.no_grad()
def lowerCAmelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
'''simple docstring'''
snake_case: int = torch.split(__lowerCamelCase , 1 , dim=0 )
snake_case: Any = []
snake_case: Union[str, Any] = []
for feature in features:
snake_case: List[Any] = self.decode_prefix(feature.to(__lowerCamelCase ) ) # back to the clip feature
# Only support beam search for now
snake_case , snake_case: Optional[int] = self.generate_beam(
input_embeds=__lowerCamelCase , device=__lowerCamelCase , eos_token_id=__lowerCamelCase )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
snake_case: Optional[int] = torch.stack(__lowerCamelCase )
snake_case: Union[str, Any] = torch.stack(__lowerCamelCase )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def lowerCAmelCase_ ( self , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase = 5 , __lowerCamelCase = 67 , __lowerCamelCase = 1.0 , __lowerCamelCase = None , ) -> int:
'''simple docstring'''
snake_case: List[Any] = eos_token_id
snake_case: Optional[int] = None
snake_case: Optional[int] = None
snake_case: int = torch.ones(__lowerCamelCase , device=__lowerCamelCase , dtype=torch.int )
snake_case: Tuple = torch.zeros(__lowerCamelCase , device=__lowerCamelCase , dtype=torch.bool )
if input_embeds is not None:
snake_case: str = input_embeds
else:
snake_case: Any = self.transformer.transformer.wte(__lowerCamelCase )
for i in range(__lowerCamelCase ):
snake_case: Tuple = self.transformer(inputs_embeds=__lowerCamelCase )
snake_case: Dict = outputs.logits
snake_case: List[str] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
snake_case: Optional[int] = logits.softmax(-1 ).log()
if scores is None:
snake_case , snake_case: str = logits.topk(__lowerCamelCase , -1 )
snake_case: Dict = generated.expand(__lowerCamelCase , *generated.shape[1:] )
snake_case , snake_case: Any = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
snake_case: Dict = next_tokens
else:
snake_case: List[Any] = tokens.expand(__lowerCamelCase , *tokens.shape[1:] )
snake_case: Optional[int] = torch.cat((tokens, next_tokens) , dim=1 )
else:
snake_case: Tuple = -float(np.inf )
snake_case: str = 0
snake_case: Any = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
snake_case: int = scores_sum / seq_lengths[:, None]
snake_case , snake_case: str = scores_sum_average.view(-1 ).topk(__lowerCamelCase , -1 )
snake_case: Tuple = next_tokens // scores_sum.shape[1]
snake_case: List[Any] = seq_lengths[next_tokens_source]
snake_case: str = next_tokens % scores_sum.shape[1]
snake_case: Optional[int] = next_tokens.unsqueeze(1 )
snake_case: Union[str, Any] = tokens[next_tokens_source]
snake_case: List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
snake_case: List[str] = generated[next_tokens_source]
snake_case: Dict = scores_sum_average * seq_lengths
snake_case: str = is_stopped[next_tokens_source]
snake_case: Dict = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
snake_case: Union[str, Any] = torch.cat((generated, next_token_embed) , dim=1 )
snake_case: Tuple = is_stopped + next_tokens.eq(__lowerCamelCase ).squeeze()
if is_stopped.all():
break
snake_case: str = scores / seq_lengths
snake_case: int = scores.argsort(descending=__lowerCamelCase )
# tokens tensors are already padded to max_seq_length
snake_case: Any = [tokens[i] for i in order]
snake_case: Tuple = torch.stack(__lowerCamelCase , dim=0 )
snake_case: Any = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 164 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}
class lowerCamelCase ( __snake_case ):
__lowerCamelCase = 'funnel'
__lowerCamelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
}
def __init__( self , __lowerCamelCase=3_05_22 , __lowerCamelCase=[4, 4, 4] , __lowerCamelCase=None , __lowerCamelCase=2 , __lowerCamelCase=7_68 , __lowerCamelCase=12 , __lowerCamelCase=64 , __lowerCamelCase=30_72 , __lowerCamelCase="gelu_new" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=0.0 , __lowerCamelCase=0.1 , __lowerCamelCase=None , __lowerCamelCase=1e-9 , __lowerCamelCase="mean" , __lowerCamelCase="relative_shift" , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=True , **__lowerCamelCase , ) -> Optional[int]:
'''simple docstring'''
snake_case: int = vocab_size
snake_case: List[str] = block_sizes
snake_case: str = [1] * len(__lowerCamelCase ) if block_repeats is None else block_repeats
assert len(__lowerCamelCase ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
snake_case: Any = num_decoder_layers
snake_case: List[str] = d_model
snake_case: Any = n_head
snake_case: str = d_head
snake_case: Optional[Any] = d_inner
snake_case: Dict = hidden_act
snake_case: Tuple = hidden_dropout
snake_case: Optional[Any] = attention_dropout
snake_case: Optional[int] = activation_dropout
snake_case: Union[str, Any] = initializer_range
snake_case: Tuple = initializer_std
snake_case: Optional[int] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
snake_case: str = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
snake_case: List[str] = attention_type
snake_case: str = separate_cls
snake_case: Dict = truncate_seq
snake_case: List[Any] = pool_q_only
super().__init__(**__lowerCamelCase )
@property
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
return sum(self.block_sizes )
@num_hidden_layers.setter
def lowerCAmelCase_ ( self , __lowerCamelCase ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.""" )
@property
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
return len(self.block_sizes )
@num_blocks.setter
def lowerCAmelCase_ ( self , __lowerCamelCase ) -> Tuple:
'''simple docstring'''
raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`.""" )
| 164 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "sew-d"
def __init__(self : Optional[int] , UpperCAmelCase_ : str=32 , UpperCAmelCase_ : str=768 , UpperCAmelCase_ : Any=12 , UpperCAmelCase_ : Optional[Any]=12 , UpperCAmelCase_ : Optional[Any]=3_072 , UpperCAmelCase_ : Tuple=2 , UpperCAmelCase_ : List[Any]=512 , UpperCAmelCase_ : Union[str, Any]=256 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : int=("p2c", "c2p") , UpperCAmelCase_ : Tuple="layer_norm" , UpperCAmelCase_ : Any="gelu_python" , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : Optional[Any]=0.1 , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : int=0.02 , UpperCAmelCase_ : List[str]=1E-7 , UpperCAmelCase_ : List[Any]=1E-5 , UpperCAmelCase_ : Union[str, Any]="group" , UpperCAmelCase_ : List[Any]="gelu" , UpperCAmelCase_ : Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , UpperCAmelCase_ : str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , UpperCAmelCase_ : Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : Tuple=128 , UpperCAmelCase_ : Optional[int]=16 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Tuple=0.05 , UpperCAmelCase_ : Union[str, Any]=10 , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : Any=10 , UpperCAmelCase_ : Union[str, Any]=0 , UpperCAmelCase_ : List[str]="mean" , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : Dict=256 , UpperCAmelCase_ : Union[str, Any]=0 , UpperCAmelCase_ : Tuple=1 , UpperCAmelCase_ : int=2 , **UpperCAmelCase_ : Tuple , ) ->str:
'''simple docstring'''
super().__init__(**UpperCAmelCase_ , pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_)
lowerCamelCase__: str =hidden_size
lowerCamelCase__: int =feat_extract_norm
lowerCamelCase__: str =feat_extract_activation
lowerCamelCase__: List[Any] =list(UpperCAmelCase_)
lowerCamelCase__: Dict =list(UpperCAmelCase_)
lowerCamelCase__: Tuple =list(UpperCAmelCase_)
lowerCamelCase__: str =conv_bias
lowerCamelCase__: Any =num_conv_pos_embeddings
lowerCamelCase__: List[Any] =num_conv_pos_embedding_groups
lowerCamelCase__: Optional[int] =len(self.conv_dim)
lowerCamelCase__: Tuple =num_hidden_layers
lowerCamelCase__: Tuple =intermediate_size
lowerCamelCase__: Optional[Any] =squeeze_factor
lowerCamelCase__: int =max_position_embeddings
lowerCamelCase__: Dict =position_buckets
lowerCamelCase__: List[Any] =share_att_key
lowerCamelCase__: Optional[Any] =relative_attention
lowerCamelCase__: List[str] =norm_rel_ebd
lowerCamelCase__: List[Any] =list(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =hidden_act
lowerCamelCase__: str =num_attention_heads
lowerCamelCase__: str =hidden_dropout
lowerCamelCase__: List[str] =attention_dropout
lowerCamelCase__: str =activation_dropout
lowerCamelCase__: int =feat_proj_dropout
lowerCamelCase__: Tuple =final_dropout
lowerCamelCase__: Any =layer_norm_eps
lowerCamelCase__: Optional[Any] =feature_layer_norm_eps
lowerCamelCase__: Dict =initializer_range
lowerCamelCase__: int =vocab_size
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)`,"
F"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase__: Any =apply_spec_augment
lowerCamelCase__: List[Any] =mask_time_prob
lowerCamelCase__: Optional[int] =mask_time_length
lowerCamelCase__: Optional[Any] =mask_time_min_masks
lowerCamelCase__: Tuple =mask_feature_prob
lowerCamelCase__: int =mask_feature_length
lowerCamelCase__: Optional[int] =mask_feature_min_masks
# ctc loss
lowerCamelCase__: Dict =ctc_loss_reduction
lowerCamelCase__: Any =ctc_zero_infinity
# sequence classification
lowerCamelCase__: Any =use_weighted_layer_sum
lowerCamelCase__: Optional[Any] =classifier_proj_size
@property
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 59 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Optional[Any] ):
"""simple docstring"""
snake_case : str = tmp_path / "file.csv"
snake_case : Tuple = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(lowerCamelCase_ , "w" ) as f:
f.write(lowerCamelCase_ )
return str(lowerCamelCase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: List[str] ):
"""simple docstring"""
snake_case : List[Any] = tmp_path / "malformed_file.csv"
snake_case : Union[str, Any] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(lowerCamelCase_ , "w" ) as f:
f.write(lowerCamelCase_ )
return str(lowerCamelCase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: int , lowerCamelCase_: Optional[Any] ):
"""simple docstring"""
snake_case : Dict = tmp_path / "csv_with_image.csv"
snake_case : List[Any] = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(lowerCamelCase_ , "w" ) as f:
f.write(lowerCamelCase_ )
return str(lowerCamelCase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: List[str] ):
"""simple docstring"""
snake_case : Optional[Any] = tmp_path / "csv_with_label.csv"
snake_case : Tuple = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(lowerCamelCase_ , "w" ) as f:
f.write(lowerCamelCase_ )
return str(lowerCamelCase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: List[Any] ):
"""simple docstring"""
snake_case : Union[str, Any] = tmp_path / "csv_with_int_list.csv"
snake_case : Optional[int] = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(lowerCamelCase_ , "w" ) as f:
f.write(lowerCamelCase_ )
return str(lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: List[str] , lowerCamelCase_: Any , lowerCamelCase_: List[str] ):
"""simple docstring"""
snake_case : Tuple = Csv()
snake_case : str = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(lowerCamelCase_ , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(lowerCamelCase_ ) in record.message
for record in caplog.records )
@require_pil
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str ):
"""simple docstring"""
with open(lowerCamelCase_ , encoding="utf-8" ) as f:
snake_case : List[Any] = f.read().splitlines()[1]
snake_case : List[str] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
snake_case : Optional[int] = csv._generate_tables([[csv_file_with_image]] )
snake_case : List[Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
snake_case : str = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: int ):
"""simple docstring"""
with open(lowerCamelCase_ , encoding="utf-8" ) as f:
snake_case : Optional[Any] = f.read().splitlines()[1:]
snake_case : List[str] = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
snake_case : int = csv._generate_tables([[csv_file_with_label]] )
snake_case : Dict = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
snake_case : Dict = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowerCamelCase_ ) for label in labels]
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Union[str, Any] ):
"""simple docstring"""
snake_case : List[Any] = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda lowerCamelCase_ : [int(lowerCamelCase_ ) for i in x.split()]} )
snake_case : Tuple = csv._generate_tables([[csv_file_with_int_list]] )
snake_case : str = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
snake_case : Any = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 449 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'SqueezeBertForMaskedLM',
'SqueezeBertForMultipleChoice',
'SqueezeBertForQuestionAnswering',
'SqueezeBertForSequenceClassification',
'SqueezeBertForTokenClassification',
'SqueezeBertModel',
'SqueezeBertModule',
'SqueezeBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 523 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
assert column_title.isupper()
__lowercase : Optional[Any] = 0
__lowercase : Union[str, Any] = len(__UpperCamelCase ) - 1
__lowercase : Union[str, Any] = 0
while index >= 0:
__lowercase : List[Any] = (ord(column_title[index] ) - 64) * pow(26 , __UpperCamelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 523 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''CLIPImageProcessor'''
lowerCamelCase__ = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')
def __init__( self :Dict , _lowerCamelCase :Optional[int]=None , _lowerCamelCase :Union[str, Any]=None , **_lowerCamelCase :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Optional[int] = kwargs.pop('''feature_extractor''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __call__( self :Tuple , _lowerCamelCase :int=None , _lowerCamelCase :Any=None , _lowerCamelCase :Any=None , **_lowerCamelCase :Dict ):
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if images is not None:
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processor(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if text is not None and images is not None:
__SCREAMING_SNAKE_CASE : int = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) , tensor_type=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , *_lowerCamelCase :Optional[Any] , **_lowerCamelCase :str ):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , *_lowerCamelCase :int , **_lowerCamelCase :Union[str, Any] ):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer.model_input_names
__SCREAMING_SNAKE_CASE : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 674 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 1 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a__ ( ):
'''simple docstring'''
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
"""-m""", """--pretrained_model_name_or_path""", type=A_, default=A_, required=A_, help="""Path to pretrained model or model identifier from huggingface.co/models.""", )
parser.add_argument(
"""-c""", """--caption""", type=A_, default="""robotic cat with wings""", help="""Text used to generate images.""", )
parser.add_argument(
"""-n""", """--images_num""", type=A_, default=4, help="""How much images to generate.""", )
parser.add_argument(
"""-s""", """--seed""", type=A_, default=42, help="""Seed for random process.""", )
parser.add_argument(
"""-ci""", """--cuda_id""", type=A_, default=0, help="""cuda_id.""", )
__magic_name__ = parser.parse_args()
return args
def a__ ( A_, A_, A_ ):
'''simple docstring'''
if not len(A_ ) == rows * cols:
raise ValueError("""The specified number of rows and columns are not correct.""" )
__magic_name__ , __magic_name__ = imgs[0].size
__magic_name__ = Image.new("""RGB""", size=(cols * w, rows * h) )
__magic_name__ , __magic_name__ = grid.size
for i, img in enumerate(A_ ):
grid.paste(A_, box=(i % cols * w, i // cols * h) )
return grid
def a__ ( A_, A_="robotic cat with wings", A_=7.5, A_=50, A_=1, A_=42, ):
'''simple docstring'''
__magic_name__ = torch.Generator(pipeline.device ).manual_seed(A_ )
__magic_name__ = pipeline(
A_, guidance_scale=A_, num_inference_steps=A_, generator=A_, num_images_per_prompt=A_, ).images
__magic_name__ = int(math.sqrt(A_ ) )
__magic_name__ = image_grid(A_, rows=_rows, cols=num_images_per_prompt // _rows )
return grid, images
__lowerCAmelCase : List[str] = parse_args()
# Load models and create wrapper for stable diffusion
__lowerCAmelCase : Tuple = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__lowerCAmelCase : Optional[Any] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__lowerCAmelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__lowerCAmelCase : Any = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__lowerCAmelCase : str = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCAmelCase : Any = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__lowerCAmelCase : int = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__lowerCAmelCase : str = unet.to(torch.device('cuda', args.cuda_id))
__lowerCAmelCase : Optional[int] = pipeline.to(unet.device)
__lowerCAmelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__lowerCAmelCase : List[Any] = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 716 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_A ):
'''simple docstring'''
a__ = ["""note_seq"""]
def __init__( self : Any , *UpperCamelCase__ : str , **UpperCamelCase__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""note_seq"""] )
@classmethod
def _lowercase ( cls : str , *UpperCamelCase__ : Dict , **UpperCamelCase__ : Tuple ) -> Dict:
"""simple docstring"""
requires_backends(cls , ["""note_seq"""] )
@classmethod
def _lowercase ( cls : List[str] , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ["""note_seq"""] )
| 76 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
lowercase__ :Dict = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def lowerCamelCase_ ( UpperCAmelCase_=True ) ->Dict:
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase ) )
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Optional[Any] = None
_A : List[str] = None
def A_ ( self : Any , __lowercase : List[Any] , __lowercase : Dict ):
'''simple docstring'''
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Optional[Any] = dataset_module_factory(__lowercase , cache_dir=__lowercase )
__UpperCAmelCase : Tuple = import_main_class(dataset_module.module_path , dataset=__lowercase )
__UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=__lowercase , config_name=__lowercase , hash=dataset_module.hash , )
__UpperCAmelCase : Optional[Any] = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=__lowercase ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
__UpperCAmelCase : Optional[Any] = cached_path(__lowercase , cache_dir=__lowercase )
self.assertTrue(os.path.exists(__lowercase ) )
@pytest.mark.integration
def lowerCamelCase_ ( UpperCAmelCase_ ) ->List[str]:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
__UpperCAmelCase : str = dataset_module_factory('''wikipedia''' , cache_dir=UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path )
__UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=UpperCAmelCase_ , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
__UpperCAmelCase : Union[str, Any] = None
builder_instance.download_and_prepare()
__UpperCAmelCase : Tuple = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowerCamelCase_ ( UpperCAmelCase_ ) ->Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = dataset_module_factory('''wikipedia''' , cache_dir=UpperCAmelCase_ )
__UpperCAmelCase : str = import_main_class(dataset_module.module_path , dataset=UpperCAmelCase_ )
__UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=UpperCAmelCase_ , config_name='''20220301.frr''' , hash=dataset_module.hash , )
__UpperCAmelCase : int = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
assert "train" in ds
assert isinstance(ds['''train'''] , UpperCAmelCase_ )
assert next(iter(ds['''train'''] ) ) | 522 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class snake_case :
'''simple docstring'''
def __init__( self : int , __lowercase : Dict , __lowercase : int=13 , __lowercase : str=7 , __lowercase : List[str]=True , __lowercase : Union[str, Any]=True , __lowercase : List[Any]=True , __lowercase : Optional[int]=True , __lowercase : Dict=99 , __lowercase : int=64 , __lowercase : Dict=32 , __lowercase : Optional[Any]=5 , __lowercase : Tuple=4 , __lowercase : Optional[Any]=37 , __lowercase : Dict="gelu" , __lowercase : int=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Tuple=512 , __lowercase : List[str]=16 , __lowercase : Dict=2 , __lowercase : int=0.0_2 , __lowercase : Dict=3 , __lowercase : List[str]=4 , __lowercase : Optional[int]=None , ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Any = seq_length
__UpperCAmelCase : Dict = is_training
__UpperCAmelCase : List[str] = use_input_mask
__UpperCAmelCase : Optional[Any] = use_token_type_ids
__UpperCAmelCase : int = use_labels
__UpperCAmelCase : Optional[int] = vocab_size
__UpperCAmelCase : Any = hidden_size
__UpperCAmelCase : str = embedding_size
__UpperCAmelCase : Optional[Any] = num_hidden_layers
__UpperCAmelCase : int = num_attention_heads
__UpperCAmelCase : List[str] = intermediate_size
__UpperCAmelCase : Dict = hidden_act
__UpperCAmelCase : Any = hidden_dropout_prob
__UpperCAmelCase : List[str] = attention_probs_dropout_prob
__UpperCAmelCase : Dict = max_position_embeddings
__UpperCAmelCase : Tuple = type_vocab_size
__UpperCAmelCase : Dict = type_sequence_label_size
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Tuple = num_labels
__UpperCAmelCase : List[str] = num_choices
__UpperCAmelCase : Optional[int] = scope
def A_ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : int = None
if self.use_input_mask:
__UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase : List[Any] = None
if self.use_token_type_ids:
__UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase : Dict = None
__UpperCAmelCase : str = None
__UpperCAmelCase : Dict = None
if self.use_labels:
__UpperCAmelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : List[Any] ):
'''simple docstring'''
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )
def A_ ( self : List[str] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : List[Any] , __lowercase : Dict , __lowercase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = MobileBertModel(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : List[str] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
__UpperCAmelCase : int = model(__lowercase , token_type_ids=__lowercase )
__UpperCAmelCase : Tuple = model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Tuple , __lowercase : int , __lowercase : Optional[int] , __lowercase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Any = MobileBertForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Tuple = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : Dict , __lowercase : str , __lowercase : Dict , __lowercase : Tuple , __lowercase : Dict , __lowercase : List[Any] , __lowercase : Any , __lowercase : str ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = MobileBertForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Tuple = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A_ ( self : List[Any] , __lowercase : List[Any] , __lowercase : Any , __lowercase : List[str] , __lowercase : Optional[Any] , __lowercase : Optional[int] , __lowercase : int , __lowercase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = MobileBertForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : List[str] = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A_ ( self : str , __lowercase : List[Any] , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = MobileBertForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : str = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A_ ( self : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : List[str] , __lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : int = self.num_labels
__UpperCAmelCase : Any = MobileBertForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Tuple = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Dict , __lowercase : int , __lowercase : Tuple , __lowercase : List[Any] , __lowercase : Tuple , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Any ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.num_labels
__UpperCAmelCase : int = MobileBertForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : int = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Tuple , __lowercase : str , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : List[str] , __lowercase : Any , __lowercase : Union[str, Any] , __lowercase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.num_choices
__UpperCAmelCase : List[Any] = MobileBertForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : List[str] = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Dict = config_and_inputs
__UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_A : Tuple = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_A : Dict = True
def A_ ( self : Any , __lowercase : List[Any] , __lowercase : Dict , __lowercase : Union[str, Any]=False ):
'''simple docstring'''
__UpperCAmelCase : Any = super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
__UpperCAmelCase : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
__UpperCAmelCase : Optional[int] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def A_ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : str = MobileBertModelTester(self )
__UpperCAmelCase : str = ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def A_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A_ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowercase )
def A_ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowercase )
def A_ ( self : Any ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowercase )
def A_ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowercase )
def A_ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowercase )
def A_ ( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowercase )
def A_ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowercase )
def A_ ( self : Any ):
'''simple docstring'''
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowercase )
def lowerCamelCase_ ( UpperCAmelCase_ ) ->List[Any]:
"""simple docstring"""
return torch.tensor(
UpperCAmelCase_ , dtype=torch.long , device=UpperCAmelCase_ , )
lowercase__ :Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = MobileBertModel.from_pretrained('''google/mobilebert-uncased''' ).to(__lowercase )
__UpperCAmelCase : Optional[int] = _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]] )
with torch.no_grad():
__UpperCAmelCase : str = model(__lowercase )[0]
__UpperCAmelCase : Any = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowercase )
__UpperCAmelCase : int = torch.tensor(
[
[
[-2.473_6526e07, 8.269_1656e04, 1.652_1838e05],
[-5.754_1704e-01, 3.905_6022e00, 4.401_1507e00],
[2.604_7359e00, 1.567_7652e00, -1.732_4188e-01],
]
] , device=__lowercase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
__UpperCAmelCase : str = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
__UpperCAmelCase : List[Any] = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound ) | 522 | 1 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( enum.Enum ):
"""simple docstring"""
__lowercase : List[Any] = 0
__lowercase : List[Any] = 1
@add_end_docstrings(__a )
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : Union[str, Any] = '''generated'''
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__)
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
def snake_case_ ( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ , ):
__SCREAMING_SNAKE_CASE = {}
if truncation is not None:
__SCREAMING_SNAKE_CASE = truncation
__SCREAMING_SNAKE_CASE = generate_kwargs
__SCREAMING_SNAKE_CASE = {}
if return_tensors is not None and return_type is None:
__SCREAMING_SNAKE_CASE = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
__SCREAMING_SNAKE_CASE = return_type
if clean_up_tokenization_spaces is not None:
__SCREAMING_SNAKE_CASE = clean_up_tokenization_spaces
if stop_sequence is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__)
if len(lowerCAmelCase__) > 1:
warnings.warn(
"""Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
""" the stop sequence will be used as the stop sequence string in the interim.""")
__SCREAMING_SNAKE_CASE = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
return True
def snake_case_ ( self , *lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = self.model.config.prefix if self.model.config.prefix is not None else """"""
if isinstance(args[0] , lowerCAmelCase__):
if self.tokenizer.pad_token_id is None:
raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input""")
__SCREAMING_SNAKE_CASE = ([prefix + arg for arg in args[0]],)
__SCREAMING_SNAKE_CASE = True
elif isinstance(args[0] , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = (prefix + args[0],)
__SCREAMING_SNAKE_CASE = False
else:
raise ValueError(
f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`")
__SCREAMING_SNAKE_CASE = self.tokenizer(*lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors=self.framework)
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = super().__call__(*lowerCAmelCase__ , **lowerCAmelCase__)
if (
isinstance(args[0] , lowerCAmelCase__)
and all(isinstance(lowerCAmelCase__ , lowerCAmelCase__) for el in args[0])
and all(len(lowerCAmelCase__) == 1 for res in result)
):
return [res[0] for res in result]
return result
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__=TruncationStrategy.DO_NOT_TRUNCATE , **lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = self._parse_and_tokenize(lowerCAmelCase__ , truncation=lowerCAmelCase__ , **lowerCAmelCase__)
return inputs
def snake_case_ ( self , lowerCAmelCase__ , **lowerCAmelCase__):
if self.framework == "pt":
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = model_inputs["""input_ids"""].shape
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = tf.shape(model_inputs["""input_ids"""]).numpy()
__SCREAMING_SNAKE_CASE = generate_kwargs.get("""min_length""" , self.model.config.min_length)
__SCREAMING_SNAKE_CASE = generate_kwargs.get("""max_length""" , self.model.config.max_length)
self.check_inputs(lowerCAmelCase__ , generate_kwargs["""min_length"""] , generate_kwargs["""max_length"""])
__SCREAMING_SNAKE_CASE = self.model.generate(**lowerCAmelCase__ , **lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = output_ids.shape[0]
if self.framework == "pt":
__SCREAMING_SNAKE_CASE = output_ids.reshape(lowerCAmelCase__ , out_b // in_b , *output_ids.shape[1:])
elif self.framework == "tf":
__SCREAMING_SNAKE_CASE = tf.reshape(lowerCAmelCase__ , (in_b, out_b // in_b, *output_ids.shape[1:]))
return {"output_ids": output_ids}
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__=ReturnType.TEXT , lowerCAmelCase__=False):
__SCREAMING_SNAKE_CASE = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
__SCREAMING_SNAKE_CASE = {f"{self.return_name}_token_ids": output_ids}
elif return_type == ReturnType.TEXT:
__SCREAMING_SNAKE_CASE = {
f"{self.return_name}_text": self.tokenizer.decode(
lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ , clean_up_tokenization_spaces=lowerCAmelCase__ , )
}
records.append(lowerCAmelCase__)
return records
@add_end_docstrings(__a )
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : List[str] = '''summary'''
def __call__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
return super().__call__(*lowerCAmelCase__ , **lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
if max_length < min_length:
logger.warning(f"Your min_length={min_length} must be inferior than your max_length={max_length}.")
if input_length < max_length:
logger.warning(
f"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
"""a summarization task, where outputs shorter than the input are typically wanted, you might """
f"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})")
@add_end_docstrings(__a )
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : List[str] = '''translation'''
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
if input_length > 0.9 * max_length:
logger.warning(
f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
"""increasing your max_length manually, e.g. translator('...', max_length=400)""")
return True
def snake_case_ ( self , *lowerCAmelCase__ , lowerCAmelCase__=TruncationStrategy.DO_NOT_TRUNCATE , lowerCAmelCase__=None , lowerCAmelCase__=None):
if getattr(self.tokenizer , """_build_translation_inputs""" , lowerCAmelCase__):
return self.tokenizer._build_translation_inputs(
*lowerCAmelCase__ , return_tensors=self.framework , truncation=lowerCAmelCase__ , src_lang=lowerCAmelCase__ , tgt_lang=lowerCAmelCase__)
else:
return super()._parse_and_tokenize(*lowerCAmelCase__ , truncation=lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = super()._sanitize_parameters(**lowerCAmelCase__)
if src_lang is not None:
__SCREAMING_SNAKE_CASE = src_lang
if tgt_lang is not None:
__SCREAMING_SNAKE_CASE = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
__SCREAMING_SNAKE_CASE = kwargs.get("""task""" , self.task)
__SCREAMING_SNAKE_CASE = task.split("""_""")
if task and len(lowerCAmelCase__) == 4:
# translation, XX, to YY
__SCREAMING_SNAKE_CASE = items[1]
__SCREAMING_SNAKE_CASE = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
return super().__call__(*lowerCAmelCase__ , **lowerCAmelCase__)
| 701 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowerCAmelCase ( UpperCamelCase_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(UpperCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(UpperCamelCase_ ):
yield num
num += 1
def _lowerCAmelCase ( UpperCamelCase_ = 200_0000 ):
return sum(takewhile(lambda UpperCamelCase_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 248 | 0 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 17 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Dict = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 17 | 1 |
from math import ceil, sqrt
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 1_000_000 ) -> int:
_snake_case : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_snake_case : int = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_snake_case : Any = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 198 |
from math import ceil, sqrt
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 1_000_000 ) -> int:
_snake_case : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_snake_case : int = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_snake_case : Any = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 198 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 144 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2m_100': ['M2M100Tokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
'M2M100ForConditionalGeneration',
'M2M100Model',
'M2M100PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 144 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 166 |
snake_case_ : Any = 9.8_06_65
def __UpperCAmelCase ( snake_case_ : float , snake_case_ : float , snake_case_ : float = g ):
'''simple docstring'''
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise ValueError("Impossible Gravity" )
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 166 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = IFPipeline
lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
def A ( self : Dict ) -> Optional[Any]:
return self._get_dummy_components()
def A ( self : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any]=0 ) -> Optional[Any]:
if str(_UpperCAmelCase ).startswith('''mps''' ):
UpperCAmelCase : Optional[int] = torch.manual_seed(_UpperCAmelCase )
else:
UpperCAmelCase : List[str] = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
UpperCAmelCase : Any = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def A ( self : Dict ) -> List[str]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def A ( self : str ) -> Dict:
super().test_save_load_floataa(expected_max_diff=1E-1 )
def A ( self : int ) -> Optional[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def A ( self : Union[str, Any] ) -> List[Any]:
self._test_save_load_local()
def A ( self : Optional[int] ) -> Any:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def A ( self : Tuple ) -> int:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : Tuple ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Optional[int] ) -> List[str]:
UpperCAmelCase : List[str] = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa )
UpperCAmelCase : Optional[Any] = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa , text_encoder=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''' )
UpperCAmelCase : List[str] = pipe_a.encode_prompt('''anime turtle''' , device='''cuda''' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
UpperCAmelCase : Tuple = None
UpperCAmelCase : Dict = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
UpperCAmelCase : Any = IFImgaImgPipeline(**pipe_a.components )
UpperCAmelCase : int = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
UpperCAmelCase : Optional[int] = IFInpaintingPipeline(**pipe_a.components )
UpperCAmelCase : Optional[Any] = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : str , __snake_case : Optional[int] ) -> Union[str, Any]:
_start_torch_memory_measurement()
UpperCAmelCase : Dict = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : List[str] = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , num_inference_steps=2 , generator=_UpperCAmelCase , output_type='''np''' , )
UpperCAmelCase : int = output.images[0]
assert image.shape == (64, 64, 3)
UpperCAmelCase : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
UpperCAmelCase : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
UpperCAmelCase : Any = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : List[Any] = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , image=_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
UpperCAmelCase : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCAmelCase : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
def A ( self : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[int] ) -> Optional[int]:
_start_torch_memory_measurement()
UpperCAmelCase : Tuple = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , image=_UpperCAmelCase , num_inference_steps=2 , generator=_UpperCAmelCase , output_type='''np''' , )
UpperCAmelCase : Dict = output.images[0]
assert image.shape == (64, 64, 3)
UpperCAmelCase : Tuple = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
UpperCAmelCase : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
UpperCAmelCase : List[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : Dict = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : int = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , image=_UpperCAmelCase , original_image=_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase : Any = output.images[0]
assert image.shape == (256, 256, 3)
UpperCAmelCase : int = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCAmelCase : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
def A ( self : List[str] , __snake_case : int , __snake_case : Any , __snake_case : List[str] , __snake_case : List[Any] ) -> List[str]:
_start_torch_memory_measurement()
UpperCAmelCase : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(_UpperCAmelCase )
UpperCAmelCase : List[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , image=_UpperCAmelCase , mask_image=_UpperCAmelCase , num_inference_steps=2 , generator=_UpperCAmelCase , output_type='''np''' , )
UpperCAmelCase : List[Any] = output.images[0]
assert image.shape == (64, 64, 3)
UpperCAmelCase : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
UpperCAmelCase : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
UpperCAmelCase : Optional[int] = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : str = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_UpperCAmelCase )
UpperCAmelCase : Any = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(_UpperCAmelCase )
UpperCAmelCase : Dict = pipe_a(
prompt_embeds=_UpperCAmelCase , negative_prompt_embeds=_UpperCAmelCase , image=_UpperCAmelCase , mask_image=_UpperCAmelCase , original_image=_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase : Dict = output.images[0]
assert image.shape == (256, 256, 3)
UpperCAmelCase : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
UpperCAmelCase : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
def snake_case_ ( ) -> Any:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 127 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ['''MobileViTFeatureExtractor''']
_lowerCamelCase : List[str] = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileViTForImageClassification''',
'''TFMobileViTForSemanticSegmentation''',
'''TFMobileViTModel''',
'''TFMobileViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 663 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__lowercase = None
__lowercase = """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowercase = [
np.dtype("""|b1"""),
np.dtype("""|u1"""),
np.dtype("""<u2"""),
np.dtype(""">u2"""),
np.dtype("""<i2"""),
np.dtype(""">i2"""),
np.dtype("""<u4"""),
np.dtype(""">u4"""),
np.dtype("""<i4"""),
np.dtype(""">i4"""),
np.dtype("""<f4"""),
np.dtype(""">f4"""),
np.dtype("""<f8"""),
np.dtype(""">f8"""),
]
@dataclass
class _lowercase :
_lowercase : bool = True
_lowercase : Optional[str] = None
# Automatically constructed
_lowercase : ClassVar[str] = "PIL.Image.Image"
_lowercase : ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
_lowercase : str = field(default='Image',init=__lowerCamelCase,repr=__lowerCamelCase )
def __call__( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return self.pa_type
def UpperCamelCase ( self : Optional[int] , lowerCamelCase__ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ = np.array(lowerCamelCase__ )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
return {"path": value, "bytes": None}
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
return {"path": None, "bytes": value}
elif isinstance(lowerCamelCase__ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowerCamelCase__ )
elif isinstance(lowerCamelCase__ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowerCamelCase__ )
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}." )
def UpperCamelCase ( self : str , lowerCamelCase__ : dict , lowerCamelCase__ : int=None ) -> "PIL.Image.Image":
"""simple docstring"""
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support decoding images, please install \'Pillow\'.''' )
if token_per_repo_id is None:
A_ = {}
A_ ,A_ = value['''path'''], value['''bytes''']
if bytes_ is None:
if path is None:
raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}." )
else:
if is_local_path(lowerCamelCase__ ):
A_ = PIL.Image.open(lowerCamelCase__ )
else:
A_ = path.split('''::''' )[-1]
try:
A_ = string_to_dict(lowerCamelCase__ , config.HUB_DATASETS_URL )['''repo_id''']
A_ = token_per_repo_id.get(lowerCamelCase__ )
except ValueError:
A_ = None
with xopen(lowerCamelCase__ , '''rb''' , use_auth_token=lowerCamelCase__ ) as f:
A_ = BytesIO(f.read() )
A_ = PIL.Image.open(bytes_ )
else:
A_ = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def UpperCamelCase ( self : List[str] ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
)
def UpperCamelCase ( self : List[Any] , lowerCamelCase__ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ) -> pa.StructArray:
"""simple docstring"""
if pa.types.is_string(storage.type ):
A_ = pa.array([None] * len(lowerCamelCase__ ) , type=pa.binary() )
A_ = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
A_ = pa.array([None] * len(lowerCamelCase__ ) , type=pa.string() )
A_ = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
A_ = storage.field('''bytes''' )
else:
A_ = pa.array([None] * len(lowerCamelCase__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
A_ = storage.field('''path''' )
else:
A_ = pa.array([None] * len(lowerCamelCase__ ) , type=pa.string() )
A_ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
A_ = pa.array(
[encode_np_array(np.array(lowerCamelCase__ ) )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
A_ = pa.array([None] * len(lowerCamelCase__ ) , type=pa.string() )
A_ = pa.StructArray.from_arrays(
[bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(lowerCamelCase__ , self.pa_type )
def UpperCamelCase ( self : Any , lowerCamelCase__ : pa.StructArray ) -> pa.StructArray:
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(lowerCamelCase__ : Union[str, Any] ):
with xopen(lowerCamelCase__ , '''rb''' ) as f:
A_ = f.read()
return bytes_
A_ = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
A_ = pa.array(
[os.path.basename(lowerCamelCase__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
A_ = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(lowerCamelCase__ , self.pa_type )
def _lowerCamelCase ( ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
A_ = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = BytesIO()
if image.format in list_image_compression_formats():
A_ = image.format
else:
A_ = '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
image.save(SCREAMING_SNAKE_CASE , format=SCREAMING_SNAKE_CASE )
return buffer.getvalue()
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if hasattr(SCREAMING_SNAKE_CASE , '''filename''' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE )}
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
A_ = array.dtype
A_ = dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
A_ = dtype.kind
A_ = dtype.itemsize
A_ = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
A_ = np.dtype('''|u1''' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays." )
if dtype is not dest_dtype:
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
A_ = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
A_ = dtype_byteorder + dtype_kind + str(SCREAMING_SNAKE_CASE )
A_ = np.dtype(SCREAMING_SNAKE_CASE )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}" )
A_ = PIL.Image.fromarray(array.astype(SCREAMING_SNAKE_CASE ) )
return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE )}
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
if objs:
A_ ,A_ = first_non_null_value(SCREAMING_SNAKE_CASE )
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(SCREAMING_SNAKE_CASE , np.ndarray ):
A_ = no_op_if_value_is_null(SCREAMING_SNAKE_CASE )
return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE ) for obj in objs]
elif isinstance(SCREAMING_SNAKE_CASE , PIL.Image.Image ):
A_ = no_op_if_value_is_null(SCREAMING_SNAKE_CASE )
return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE ) for obj in objs]
else:
return objs
else:
return objs
| 563 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
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 (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class _lowercase :
def __init__( self : Dict , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : str=1_3 , lowerCamelCase__ : Any=7 , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : List[Any]=True , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : List[str]=9_9 , lowerCamelCase__ : Optional[Any]=[1, 1, 2] , lowerCamelCase__ : Optional[Any]=1 , lowerCamelCase__ : Union[str, Any]=3_2 , lowerCamelCase__ : int=4 , lowerCamelCase__ : Optional[int]=8 , lowerCamelCase__ : Union[str, Any]=3_7 , lowerCamelCase__ : List[Any]="gelu_new" , lowerCamelCase__ : List[Any]=0.1 , lowerCamelCase__ : Union[str, Any]=0.1 , lowerCamelCase__ : Optional[Any]=0.0 , lowerCamelCase__ : List[Any]=5_1_2 , lowerCamelCase__ : Optional[int]=3 , lowerCamelCase__ : List[Any]=0.02 , lowerCamelCase__ : str=3 , lowerCamelCase__ : Union[str, Any]=4 , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : List[Any]=False , ) -> Union[str, Any]:
"""simple docstring"""
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_token_type_ids
A_ = use_labels
A_ = vocab_size
A_ = block_sizes
A_ = num_decoder_layers
A_ = d_model
A_ = n_head
A_ = d_head
A_ = d_inner
A_ = hidden_act
A_ = hidden_dropout
A_ = attention_dropout
A_ = activation_dropout
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = 2
A_ = num_labels
A_ = num_choices
A_ = scope
A_ = initializer_std
# Used in the tests to check the size of the first attention layer
A_ = n_head
# Used in the tests to check the size of the first hidden state
A_ = self.d_model
# Used in the tests to check the number of output hidden states/attentions
A_ = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
A_ = self.num_hidden_layers + 2
def UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def UpperCamelCase ( self : Optional[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : int , ) -> List[Any]:
"""simple docstring"""
A_ = TFFunnelModel(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
A_ = [input_ids, input_mask]
A_ = model(lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
A_ = False
A_ = TFFunnelModel(config=lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
A_ = False
A_ = TFFunnelModel(config=lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def UpperCamelCase ( self : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Tuple , lowerCamelCase__ : Any , lowerCamelCase__ : Tuple , lowerCamelCase__ : Tuple , lowerCamelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
A_ = TFFunnelBaseModel(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
A_ = [input_ids, input_mask]
A_ = model(lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
A_ = False
A_ = TFFunnelBaseModel(config=lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
A_ = False
A_ = TFFunnelBaseModel(config=lowerCamelCase__ )
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def UpperCamelCase ( self : Optional[Any] , lowerCamelCase__ : int , lowerCamelCase__ : Dict , lowerCamelCase__ : int , lowerCamelCase__ : Dict , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] , ) -> List[str]:
"""simple docstring"""
A_ = TFFunnelForPreTraining(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self : List[Any] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict , ) -> List[str]:
"""simple docstring"""
A_ = TFFunnelForMaskedLM(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict , lowerCamelCase__ : int , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
A_ = self.num_labels
A_ = TFFunnelForSequenceClassification(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self : List[str] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple , lowerCamelCase__ : int , lowerCamelCase__ : Dict , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
A_ = self.num_choices
A_ = TFFunnelForMultipleChoice(config=lowerCamelCase__ )
A_ = tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
A_ = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self : Optional[Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict , lowerCamelCase__ : str , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Dict , ) -> Union[str, Any]:
"""simple docstring"""
A_ = self.num_labels
A_ = TFFunnelForTokenClassification(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self : Any , lowerCamelCase__ : Any , lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : str , ) -> str:
"""simple docstring"""
A_ = TFFunnelForQuestionAnswering(config=lowerCamelCase__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = 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 UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
A_ = self.prepare_config_and_inputs()
(
(
A_
) ,(
A_
) ,(
A_
) ,(
A_
) ,(
A_
) ,(
A_
) ,(
A_
) ,
) = config_and_inputs
A_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class _lowercase ( __lowerCamelCase,__lowerCamelCase,unittest.TestCase ):
_lowercase : Optional[Any] = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : List[Any] = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowercase : Dict = False
_lowercase : Optional[int] = False
def UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
A_ = TFFunnelModelTester(self )
A_ = ConfigTester(self , config_class=lowerCamelCase__ )
def UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def UpperCamelCase ( self : Any ) -> List[str]:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__ )
def UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__ )
def UpperCamelCase ( self : Any ) -> Any:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__ )
def UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__ )
@require_tf
class _lowercase ( __lowerCamelCase,unittest.TestCase ):
_lowercase : Any = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_lowercase : str = False
_lowercase : Any = False
def UpperCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
A_ = TFFunnelModelTester(self , base=lowerCamelCase__ )
A_ = ConfigTester(self , config_class=lowerCamelCase__ )
def UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowerCamelCase__ )
def UpperCamelCase ( self : str ) -> Dict:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__ )
def UpperCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__ )
| 563 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( __snake_case , __snake_case ):
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!" )
__lowerCAmelCase = number_of_bytes // partitions
__lowerCAmelCase = []
for i in range(__snake_case ):
__lowerCAmelCase = i * bytes_per_partition + 1
__lowerCAmelCase = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(F"""{start_bytes}-{end_bytes}""" )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = (EulerDiscreteScheduler,)
SCREAMING_SNAKE_CASE__ : List[Any] = 10
def A_ ( self , **snake_case ):
'''simple docstring'''
UpperCAmelCase : List[Any] = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**snake_case )
return config
def A_ ( self ):
'''simple docstring'''
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=snake_case )
def A_ ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case , beta_end=snake_case )
def A_ ( self ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case )
def A_ ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
UpperCAmelCase : Union[str, Any] = self.get_scheduler_config()
UpperCAmelCase : Optional[Any] = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Union[str, Any] = torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = self.dummy_model()
UpperCAmelCase : int = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Any = sample.to(snake_case )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Tuple = scheduler.scale_model_input(snake_case , snake_case )
UpperCAmelCase : List[Any] = model(snake_case , snake_case )
UpperCAmelCase : str = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
UpperCAmelCase : Dict = output.prev_sample
UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(snake_case ) )
UpperCAmelCase : List[Any] = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase : int = self.get_scheduler_config(prediction_type="v_prediction" )
UpperCAmelCase : List[Any] = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : List[Any] = torch.manual_seed(0 )
UpperCAmelCase : Dict = self.dummy_model()
UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : int = sample.to(snake_case )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : str = scheduler.scale_model_input(snake_case , snake_case )
UpperCAmelCase : Dict = model(snake_case , snake_case )
UpperCAmelCase : List[Any] = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
UpperCAmelCase : Any = output.prev_sample
UpperCAmelCase : Optional[int] = torch.sum(torch.abs(snake_case ) )
UpperCAmelCase : Any = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase : Optional[int] = self.get_scheduler_config()
UpperCAmelCase : Any = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case )
UpperCAmelCase : List[Any] = torch.manual_seed(0 )
UpperCAmelCase : int = self.dummy_model()
UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
UpperCAmelCase : str = sample.to(snake_case )
for t in scheduler.timesteps:
UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(snake_case , snake_case )
UpperCAmelCase : List[Any] = model(snake_case , snake_case )
UpperCAmelCase : List[str] = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
UpperCAmelCase : Dict = output.prev_sample
UpperCAmelCase : Optional[int] = torch.sum(torch.abs(snake_case ) )
UpperCAmelCase : Any = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = self.scheduler_classes[0]
UpperCAmelCase : Tuple = self.get_scheduler_config()
UpperCAmelCase : Dict = scheduler_class(**snake_case , use_karras_sigmas=snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case )
UpperCAmelCase : List[str] = torch.manual_seed(0 )
UpperCAmelCase : Any = self.dummy_model()
UpperCAmelCase : Optional[int] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
UpperCAmelCase : List[str] = sample.to(snake_case )
for t in scheduler.timesteps:
UpperCAmelCase : str = scheduler.scale_model_input(snake_case , snake_case )
UpperCAmelCase : Dict = model(snake_case , snake_case )
UpperCAmelCase : Dict = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
UpperCAmelCase : List[str] = output.prev_sample
UpperCAmelCase : int = torch.sum(torch.abs(snake_case ) )
UpperCAmelCase : Any = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3
| 679 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : Any ):
lowerCAmelCase : Tuple = [1]
for i in range(2 , _snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
lowerCAmelCase : str = []
lowerCAmelCase : Any = list(range(_snake_case ) )
# Find permutation
while factorials:
lowerCAmelCase : List[Any] = factorials.pop()
lowerCAmelCase : str = divmod(_snake_case , _snake_case )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705 |
"""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__ : Tuple = logging.get_logger(__name__)
snake_case__ : int = {
'''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class snake_case_( a__ ):
__UpperCamelCase = '''levit'''
def __init__( self : str , UpperCamelCase_ : Union[str, Any]=2_2_4 , UpperCamelCase_ : Union[str, Any]=3 , UpperCamelCase_ : Union[str, Any]=3 , UpperCamelCase_ : int=2 , UpperCamelCase_ : Union[str, Any]=1 , UpperCamelCase_ : Tuple=1_6 , UpperCamelCase_ : Dict=[1_2_8, 2_5_6, 3_8_4] , UpperCamelCase_ : Optional[Any]=[4, 8, 1_2] , UpperCamelCase_ : Dict=[4, 4, 4] , UpperCamelCase_ : Any=[1_6, 1_6, 1_6] , UpperCamelCase_ : str=0 , UpperCamelCase_ : int=[2, 2, 2] , UpperCamelCase_ : Optional[Any]=[2, 2, 2] , UpperCamelCase_ : str=0.02 , **UpperCamelCase_ : List[str] , ):
super().__init__(**UpperCamelCase_ )
lowerCAmelCase : Tuple = image_size
lowerCAmelCase : int = num_channels
lowerCAmelCase : Optional[int] = kernel_size
lowerCAmelCase : Dict = stride
lowerCAmelCase : List[Any] = padding
lowerCAmelCase : Dict = hidden_sizes
lowerCAmelCase : List[str] = num_attention_heads
lowerCAmelCase : Tuple = depths
lowerCAmelCase : Dict = key_dim
lowerCAmelCase : Union[str, Any] = drop_path_rate
lowerCAmelCase : List[Any] = patch_size
lowerCAmelCase : Tuple = attention_ratio
lowerCAmelCase : Optional[int] = mlp_ratio
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : List[str] = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class snake_case_( a__ ):
__UpperCamelCase = version.parse('''1.11''' )
@property
def lowerCamelCase__ ( self : Tuple ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase__ ( self : Optional[Any] ):
return 1E-4
| 637 | 0 |
'''simple docstring'''
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 A ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
a_ = StableDiffusionInstructPixaPixPipeline
a_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
a_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
a_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
a_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case__ ( self : List[str] ) -> Optional[Any]:
torch.manual_seed(0 )
__UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
__UpperCAmelCase = PNDMScheduler(skip_prk_steps=__a )
torch.manual_seed(0 )
__UpperCAmelCase = 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 )
__UpperCAmelCase = 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 , )
__UpperCAmelCase = CLIPTextModel(__a )
__UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def snake_case__ ( self : Optional[Any] , __a : Optional[Any] , __a : str=0 ) -> Any:
__UpperCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__a ) ).to(__a )
__UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCAmelCase = Image.fromarray(np.uinta(__a ) ).convert('''RGB''' )
if str(__a ).startswith('''mps''' ):
__UpperCAmelCase = torch.manual_seed(__a )
else:
__UpperCAmelCase = torch.Generator(device=__a ).manual_seed(__a )
__UpperCAmelCase = {
'''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 snake_case__ ( self : Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline(**__a )
__UpperCAmelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__UpperCAmelCase = self.get_dummy_inputs(__a )
__UpperCAmelCase = sd_pipe(**__a ).images
__UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCAmelCase = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : List[str] ) -> Any:
__UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline(**__a )
__UpperCAmelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__UpperCAmelCase = self.get_dummy_inputs(__a )
__UpperCAmelCase = '''french fries'''
__UpperCAmelCase = sd_pipe(**__a , negative_prompt=__a )
__UpperCAmelCase = output.images
__UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCAmelCase = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : Tuple ) -> List[Any]:
__UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline(**__a )
__UpperCAmelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__UpperCAmelCase = self.get_dummy_inputs(__a )
__UpperCAmelCase = [inputs['''prompt''']] * 2
__UpperCAmelCase = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
__UpperCAmelCase = torch.from_numpy(__a ).unsqueeze(0 ).to(__a )
__UpperCAmelCase = image / 2 + 0.5
__UpperCAmelCase = image.permute(0 , 3 , 1 , 2 )
__UpperCAmelCase = image.repeat(2 , 1 , 1 , 1 )
__UpperCAmelCase = sd_pipe(**__a ).images
__UpperCAmelCase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
__UpperCAmelCase = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : Union[str, Any] ) -> List[Any]:
__UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline(**__a )
__UpperCAmelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__UpperCAmelCase = self.get_dummy_inputs(__a )
__UpperCAmelCase = sd_pipe(**__a ).images
__UpperCAmelCase = image[0, -3:, -3:, -1]
__UpperCAmelCase = [round(__a , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(__a ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCAmelCase = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : int ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def snake_case__ ( self : Tuple ) -> Union[str, Any]:
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline(**__a )
__UpperCAmelCase = VaeImageProcessor(do_resize=__a , do_normalize=__a )
__UpperCAmelCase = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__UpperCAmelCase = pipe(**self.get_dummy_inputs_by_type(__a , input_image_type='''pt''' ) )[0]
__UpperCAmelCase = components['''vae''']
__UpperCAmelCase = self.get_dummy_inputs_by_type(__a , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__UpperCAmelCase = vae.encode(inputs[image_param] ).latent_dist.mode()
__UpperCAmelCase = pipe(**__a )[0]
__UpperCAmelCase = np.abs(out - out_latents_inputs ).max()
self.assertLess(__a , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
def snake_case__ ( self : str ) -> Any:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self : Union[str, Any] , __a : int=0 ) -> str:
__UpperCAmelCase = torch.manual_seed(__a )
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
__UpperCAmelCase = {
'''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 snake_case__ ( self : Optional[Any] ) -> List[Any]:
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__UpperCAmelCase = self.get_inputs()
__UpperCAmelCase = pipe(**__a ).images
__UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCAmelCase = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self : Any ) -> Tuple:
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__a )
__UpperCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__UpperCAmelCase = self.get_inputs()
__UpperCAmelCase = pipe(**__a ).images
__UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCAmelCase = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self : Optional[Any] ) -> Any:
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__a )
__UpperCAmelCase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__UpperCAmelCase = self.get_inputs()
__UpperCAmelCase = pipe(**__a ).images
__UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCAmelCase = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self : Any ) -> Dict:
__UpperCAmelCase = 0
def callback_fn(__a : int , __a : int , __a : torch.FloatTensor ) -> None:
__UpperCAmelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__UpperCAmelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__UpperCAmelCase = latents[0, -3:, -3:, -1]
__UpperCAmelCase = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__UpperCAmelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__UpperCAmelCase = latents[0, -3:, -3:, -1]
__UpperCAmelCase = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__UpperCAmelCase = False
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__a , torch_dtype=torch.floataa )
__UpperCAmelCase = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__UpperCAmelCase = self.get_inputs()
pipe(**__a , callback=__a , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case__ ( self : Any ) -> Dict:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=__a , torch_dtype=torch.floataa )
__UpperCAmelCase = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__UpperCAmelCase = self.get_inputs()
__UpperCAmelCase = pipe(**__a )
__UpperCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def snake_case__ ( self : Optional[Any] ) -> Optional[Any]:
__UpperCAmelCase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__UpperCAmelCase = inputs['''image'''].resize((5_0_4, 5_0_4) )
__UpperCAmelCase = '''timbrooks/instruct-pix2pix'''
__UpperCAmelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
__a , safety_checker=__a , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__UpperCAmelCase = pipe(**__a )
__UpperCAmelCase = output.images[0]
__UpperCAmelCase = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
__UpperCAmelCase = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 262 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase : int = logging.getLogger(__name__)
@dataclass
class A :
a_ = 42
a_ = 42
a_ = 42
@dataclass
class A :
a_ = 42
a_ = 42
a_ = None
a_ = None
class A ( UpperCAmelCase ):
a_ = '''train'''
a_ = '''dev'''
a_ = '''test'''
class A :
@staticmethod
def snake_case__ ( __a : List[Any] , __a : Union[Split, str] ) -> List[InputExample]:
raise NotImplementedError
@staticmethod
def snake_case__ ( __a : str ) -> List[str]:
raise NotImplementedError
@staticmethod
def snake_case__ ( __a : List[InputExample] , __a : List[str] , __a : int , __a : PreTrainedTokenizer , __a : Dict=False , __a : int="[CLS]" , __a : Dict=1 , __a : Tuple="[SEP]" , __a : Any=False , __a : Union[str, Any]=False , __a : Any=0 , __a : Optional[int]=0 , __a : Tuple=-1_0_0 , __a : Optional[Any]=0 , __a : int=True , ) -> List[InputFeatures]:
__UpperCAmelCase = {label: i for i, label in enumerate(__a )}
__UpperCAmelCase = []
for ex_index, example in enumerate(__a ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d of %d''' , __a , len(__a ) )
__UpperCAmelCase = []
__UpperCAmelCase = []
for word, label in zip(example.words , example.labels ):
__UpperCAmelCase = tokenizer.tokenize(__a )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(__a ) > 0:
tokens.extend(__a )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__a ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
__UpperCAmelCase = tokenizer.num_special_tokens_to_add()
if len(__a ) > max_seq_length - special_tokens_count:
__UpperCAmelCase = tokens[: (max_seq_length - special_tokens_count)]
__UpperCAmelCase = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
__UpperCAmelCase = [sequence_a_segment_id] * len(__a )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
__UpperCAmelCase = [cls_token] + tokens
__UpperCAmelCase = [pad_token_label_id] + label_ids
__UpperCAmelCase = [cls_token_segment_id] + segment_ids
__UpperCAmelCase = tokenizer.convert_tokens_to_ids(__a )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
__UpperCAmelCase = [1 if mask_padding_with_zero else 0] * len(__a )
# Zero-pad up to the sequence length.
__UpperCAmelCase = max_seq_length - len(__a )
if pad_on_left:
__UpperCAmelCase = ([pad_token] * padding_length) + input_ids
__UpperCAmelCase = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
__UpperCAmelCase = ([pad_token_segment_id] * padding_length) + segment_ids
__UpperCAmelCase = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(__a ) == max_seq_length
assert len(__a ) == max_seq_length
assert len(__a ) == max_seq_length
assert len(__a ) == max_seq_length
if ex_index < 5:
logger.info('''*** Example ***''' )
logger.info('''guid: %s''' , example.guid )
logger.info('''tokens: %s''' , ''' '''.join([str(__a ) for x in tokens] ) )
logger.info('''input_ids: %s''' , ''' '''.join([str(__a ) for x in input_ids] ) )
logger.info('''input_mask: %s''' , ''' '''.join([str(__a ) for x in input_mask] ) )
logger.info('''segment_ids: %s''' , ''' '''.join([str(__a ) for x in segment_ids] ) )
logger.info('''label_ids: %s''' , ''' '''.join([str(__a ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
__UpperCAmelCase = None
features.append(
InputFeatures(
input_ids=__a , attention_mask=__a , token_type_ids=__a , label_ids=__a ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class A ( UpperCAmelCase ):
a_ = 42
a_ = nn.CrossEntropyLoss().ignore_index
def __init__( self : List[Any] , __a : TokenClassificationTask , __a : str , __a : PreTrainedTokenizer , __a : List[str] , __a : str , __a : Optional[int] = None , __a : Dict=False , __a : Split = Split.train , ) -> Optional[int]:
# Load data features from cache or dataset file
__UpperCAmelCase = os.path.join(
__a , '''cached_{}_{}_{}'''.format(mode.value , tokenizer.__class__.__name__ , str(__a ) ) , )
# 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(__a ):
if os.path.exists(__a ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
__UpperCAmelCase = torch.load(__a )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
__UpperCAmelCase = token_classification_task.read_examples_from_file(__a , __a )
# TODO clean up all this to leverage built-in features of tokenizers
__UpperCAmelCase = token_classification_task.convert_examples_to_features(
__a , __a , __a , __a , cls_token_at_end=bool(model_type in ['''xlnet'''] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=__a , pad_on_left=bool(tokenizer.padding_side == '''left''' ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f"""Saving features into cached file {cached_features_file}""" )
torch.save(self.features , __a )
def __len__( self : List[Any] ) -> Union[str, Any]:
return len(self.features )
def __getitem__( self : int , __a : int ) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class A :
a_ = 42
a_ = -1_0_0
def __init__( self : Union[str, Any] , __a : TokenClassificationTask , __a : str , __a : PreTrainedTokenizer , __a : List[str] , __a : str , __a : Optional[int] = None , __a : Any=False , __a : Split = Split.train , ) -> Union[str, Any]:
__UpperCAmelCase = token_classification_task.read_examples_from_file(__a , __a )
# TODO clean up all this to leverage built-in features of tokenizers
__UpperCAmelCase = token_classification_task.convert_examples_to_features(
__a , __a , __a , __a , cls_token_at_end=bool(model_type in ['''xlnet'''] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=__a , pad_on_left=bool(tokenizer.padding_side == '''left''' ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
__UpperCAmelCase = tf.data.Dataset.from_generator(
__a , ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa) , (
{'''input_ids''': tf.TensorShape([None] ), '''attention_mask''': tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
__UpperCAmelCase = tf.data.Dataset.from_generator(
__a , ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa) , (
{
'''input_ids''': tf.TensorShape([None] ),
'''attention_mask''': tf.TensorShape([None] ),
'''token_type_ids''': tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def snake_case__ ( self : Union[str, Any] ) -> Optional[Any]:
__UpperCAmelCase = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : int ) -> str:
return len(self.features )
def __getitem__( self : int , __a : List[Any] ) -> InputFeatures:
return self.features[i]
| 262 | 1 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCamelCase__ = '''__DUMMY_TRANSFORMERS_USER__'''
lowerCamelCase__ = '''Dummy User'''
lowerCamelCase__ = '''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'''
lowerCamelCase__ = '''https://hub-ci.huggingface.co'''
lowerCamelCase__ = CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowerCamelCase__ = CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
lowerCamelCase__ = Path('''~/.huggingface/hub_ci_token''').expanduser()
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
monkeypatch.setattr(
'''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' , a__ )
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
monkeypatch.setattr('''datasets.config.HF_ENDPOINT''' , a__ )
monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''' , a__ )
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' , a__ )
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
HfFolder.save_token(a__ )
yield
HfFolder.delete_token()
@pytest.fixture(scope='''session''' )
def lowercase_ ( ):
"""simple docstring"""
return HfApi(endpoint=a__ )
@pytest.fixture(scope='''session''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
snake_case__ : str =HfFolder.get_token()
HfFolder.save_token(a__ )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(a__ )
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
def _cleanup_repo(SCREAMING_SNAKE_CASE : Optional[Any] ):
hf_api.delete_repo(a__ , token=a__ , repo_type='''dataset''' )
return _cleanup_repo
@pytest.fixture
def lowercase_ ( SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
@contextmanager
def _temporary_repo(SCREAMING_SNAKE_CASE : int ):
try:
yield repo_id
finally:
cleanup_repo(a__ )
return _temporary_repo
@pytest.fixture(scope='''session''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
snake_case__ : List[Any] =F'''repo_txt_data-{int(time.time() * 10E3 )}'''
snake_case__ : Tuple =F'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(a__ , token=a__ , repo_type='''dataset''' , private=a__ )
hf_api.upload_file(
token=a__ , path_or_fileobj=str(a__ ) , path_in_repo='''data/text_data.txt''' , repo_id=a__ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(a__ , token=a__ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowercase_ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='''session''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
snake_case__ : List[Any] =F'''repo_zipped_txt_data-{int(time.time() * 10E3 )}'''
snake_case__ : List[Any] =F'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(a__ , token=a__ , repo_type='''dataset''' , private=a__ )
hf_api.upload_file(
token=a__ , path_or_fileobj=str(a__ ) , path_in_repo='''data.zip''' , repo_id=a__ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(a__ , token=a__ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowercase_ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='''session''' )
def lowercase_ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
snake_case__ : Any =F'''repo_zipped_img_data-{int(time.time() * 10E3 )}'''
snake_case__ : Tuple =F'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(a__ , token=a__ , repo_type='''dataset''' , private=a__ )
hf_api.upload_file(
token=a__ , path_or_fileobj=str(a__ ) , path_in_repo='''data.zip''' , repo_id=a__ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(a__ , token=a__ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
return hf_private_dataset_repo_zipped_img_data_
| 714 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
snake_case__ : Any =int(SCREAMING_SNAKE_CASE )
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.''' )
snake_case__ : Any =1
snake_case__ : List[str] =2
while i * i <= n:
while n % i == 0:
snake_case__ : Union[str, Any] =i
n //= i
i += 1
if n > 1:
snake_case__ : Any =n
return int(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 408 | 0 |
"""simple docstring"""
import string
def __snake_case ( _lowercase ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCamelCase = string.ascii_uppercase.find(_lowercase )
UpperCamelCase = num - key
if num < 0:
UpperCamelCase = num + len(string.ascii_uppercase )
UpperCamelCase = translated + string.ascii_uppercase[num]
else:
UpperCamelCase = translated + symbol
print(f'Decryption using Key #{key}: {translated}' )
def __snake_case ( ):
"""simple docstring"""
UpperCamelCase = input('''Encrypted message: ''' )
UpperCamelCase = message.upper()
decrypt(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 34 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Tuple = [
"""VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VanForImageClassification""",
"""VanModel""",
"""VanPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
__lowercase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure) | 142 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def SCREAMING_SNAKE_CASE ( snake_case__ ) -> str:
__UpperCAmelCase ={}
__UpperCAmelCase =job['''started_at''']
__UpperCAmelCase =job['''completed_at''']
__UpperCAmelCase =date_parser.parse(snake_case__ )
__UpperCAmelCase =date_parser.parse(snake_case__ )
__UpperCAmelCase =round((end_datetime - start_datetime).total_seconds() / 60.0 )
__UpperCAmelCase =start
__UpperCAmelCase =end
__UpperCAmelCase =duration_in_min
return job_info
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__=None ) -> Union[str, Any]:
__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(snake_case__ , headers=snake_case__ ).json()
__UpperCAmelCase ={}
try:
job_time.update({job['''name''']: extract_time_from_single_job(snake_case__ ) for job in result['''jobs''']} )
__UpperCAmelCase =math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(snake_case__ ):
__UpperCAmelCase =requests.get(url + f"""&page={i + 2}""" , headers=snake_case__ ).json()
job_time.update({job['''name''']: extract_time_from_single_job(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__":
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
UpperCamelCase_ = parser.parse_args()
UpperCamelCase_ = get_job_time(args.workflow_run_id)
UpperCamelCase_ = 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"]}') | 713 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _SCREAMING_SNAKE_CASE :
def __init__(self , UpperCAmelCase , UpperCAmelCase=3 , UpperCAmelCase=3_2 , UpperCAmelCase=3 , UpperCAmelCase=1_0 , UpperCAmelCase=[1_0, 2_0, 3_0, 4_0] , UpperCAmelCase=[1, 1, 2, 1] , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase="relu" , UpperCAmelCase=3 , UpperCAmelCase=None , ):
'''simple docstring'''
__UpperCAmelCase =parent
__UpperCAmelCase =batch_size
__UpperCAmelCase =image_size
__UpperCAmelCase =num_channels
__UpperCAmelCase =embeddings_size
__UpperCAmelCase =hidden_sizes
__UpperCAmelCase =depths
__UpperCAmelCase =is_training
__UpperCAmelCase =use_labels
__UpperCAmelCase =hidden_act
__UpperCAmelCase =num_labels
__UpperCAmelCase =scope
__UpperCAmelCase =len(UpperCAmelCase)
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase =None
if self.use_labels:
__UpperCAmelCase =ids_tensor([self.batch_size] , self.num_labels)
__UpperCAmelCase =self.get_config()
return config, pixel_values, labels
def A__ (self):
'''simple docstring'''
return RegNetConfig(
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 , )
def A__ (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =TFRegNetModel(config=UpperCAmelCase)
__UpperCAmelCase =model(UpperCAmelCase , training=UpperCAmelCase)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def A__ (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =self.num_labels
__UpperCAmelCase =TFRegNetForImageClassification(UpperCAmelCase)
__UpperCAmelCase =model(UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =config_and_inputs
__UpperCAmelCase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
a_ : Any = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
a_ : Union[str, Any] = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
a_ : str = False
a_ : List[str] = False
a_ : int = False
a_ : List[str] = False
a_ : List[Any] = False
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =TFRegNetModelTester(self)
__UpperCAmelCase =ConfigTester(self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase)
def A__ (self):
'''simple docstring'''
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''')
def A__ (self):
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''')) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def A__ (self):
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''')
def A__ (self):
'''simple docstring'''
pass
def A__ (self):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase =model_class(UpperCAmelCase)
__UpperCAmelCase =inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase =[*signature.parameters.keys()]
__UpperCAmelCase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase)
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase)
def A__ (self):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase):
__UpperCAmelCase =model_class(UpperCAmelCase)
__UpperCAmelCase =model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase) , training=UpperCAmelCase)
__UpperCAmelCase =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase) , expected_num_stages + 1)
# RegNet'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 // 2, self.model_tester.image_size // 2] , )
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase =['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__UpperCAmelCase =layer_type
__UpperCAmelCase =True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase =True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
def A__ (self):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase={}):
__UpperCAmelCase =model(UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase)
__UpperCAmelCase =model(UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase).to_tuple()
def recursive_check(UpperCAmelCase , UpperCAmelCase):
if isinstance(UpperCAmelCase , (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase , UpperCAmelCase):
recursive_check(UpperCAmelCase , UpperCAmelCase)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCAmelCase , UpperCAmelCase)) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
) , )
recursive_check(UpperCAmelCase , UpperCAmelCase)
for model_class in self.all_model_classes:
__UpperCAmelCase =model_class(UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase)
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase)
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase)
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'''output_hidden_states''': True})
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase)
__UpperCAmelCase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase)
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'''output_hidden_states''': True})
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)
@slow
def A__ (self):
'''simple docstring'''
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase =TFRegNetModel.from_pretrained(UpperCAmelCase)
self.assertIsNotNone(UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
__UpperCAmelCase =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def A__ (self):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
__UpperCAmelCase =self.default_image_processor
__UpperCAmelCase =prepare_img()
__UpperCAmelCase =image_processor(images=UpperCAmelCase , return_tensors='''tf''')
# forward pass
__UpperCAmelCase =model(**UpperCAmelCase , training=UpperCAmelCase)
# verify the logits
__UpperCAmelCase =tf.TensorShape((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , UpperCAmelCase)
__UpperCAmelCase =tf.constant([-0.4180, -1.5051, -3.4836])
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCAmelCase , atol=1e-4)
| 142 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__=() ,lowerCAmelCase__=None ,lowerCAmelCase__="no" ,lowerCAmelCase__="29500" ):
lowerCamelCase_ = False
lowerCamelCase_ = False
if any(key.startswith('''KAGGLE''' ) for key in os.environ.keys() ):
lowerCamelCase_ = True
elif "IPython" in sys.modules:
lowerCamelCase_ = '''google.colab''' in str(sys.modules['''IPython'''].get_ipython() )
try:
lowerCamelCase_ = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." )
if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME''' ,lowerCAmelCase__ ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
'''your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if num_processes is None:
lowerCamelCase_ = 8
lowerCamelCase_ = PrepareForLaunch(lowerCAmelCase__ ,distributed_type='''TPU''' )
print(f"Launching a training on {num_processes} TPU cores." )
xmp.spawn(lowerCAmelCase__ ,args=lowerCAmelCase__ ,nprocs=lowerCAmelCase__ ,start_method='''fork''' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on one CPU.''' )
function(*lowerCAmelCase__ )
else:
if num_processes is None:
raise ValueError(
'''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
'''inside your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if torch.cuda.is_initialized():
raise ValueError(
'''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
'''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
'''function.''' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=lowerCAmelCase__ ,master_addr='''127.0.01''' ,master_port=lowerCAmelCase__ ,mixed_precision=lowerCAmelCase__ ):
lowerCamelCase_ = PrepareForLaunch(lowerCAmelCase__ ,distributed_type='''MULTI_GPU''' )
print(f"Launching training on {num_processes} GPUs." )
try:
start_processes(lowerCAmelCase__ ,args=lowerCAmelCase__ ,nprocs=lowerCAmelCase__ ,start_method='''fork''' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
'''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
'''Please review your imports and test them when running the `notebook_launcher()` to identify '''
'''which one is problematic.''' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
lowerCamelCase_ = '''1'''
print('''Launching training on MPS.''' )
elif torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on CPU.''' )
function(*lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__=() ,lowerCAmelCase__=2 ):
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=lowerCAmelCase__ ,master_addr='''127.0.01''' ,master_port='''29500''' ,accelerate_mixed_precision='''no''' ,accelerate_debug_rdv_file=tmp_file.name ,accelerate_use_cpu='''yes''' ,):
lowerCamelCase_ = PrepareForLaunch(lowerCAmelCase__ ,debug=lowerCAmelCase__ )
start_processes(lowerCAmelCase__ ,args=lowerCAmelCase__ ,nprocs=lowerCAmelCase__ ,start_method='''fork''' )
| 29 | """simple docstring"""
from math import pow, sqrt
def UpperCAmelCase__ ( *lowerCAmelCase__ :float ) -> bool:
'''simple docstring'''
lowercase = len(lowerCAmelCase__ ) > 0 and all(value > 0.0 for value in values )
return result
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float | ValueError:
'''simple docstring'''
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowerCAmelCase__ , lowerCAmelCase__ )
else ValueError("""Input Error: Molar mass values must greater than 0.""" )
)
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float | ValueError:
'''simple docstring'''
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float | ValueError:
'''simple docstring'''
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float | ValueError:
'''simple docstring'''
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float | ValueError:
'''simple docstring'''
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else ValueError(
"""Input Error: Molar mass and effusion rate values must greater than 0.""" )
)
| 359 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=32 , snake_case_=3 , snake_case_=4 , snake_case_=[10, 20, 30, 40] , snake_case_=[2, 2, 3, 2] , snake_case_=True , snake_case_=True , snake_case_=37 , snake_case_="gelu" , snake_case_=10 , snake_case_=0.02 , snake_case_=["stage2", "stage3", "stage4"] , snake_case_=[2, 3, 4] , snake_case_=None , ) -> List[str]:
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = num_stages
__lowerCAmelCase = hidden_sizes
__lowerCAmelCase = depths
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = num_labels
__lowerCAmelCase = initializer_range
__lowerCAmelCase = out_features
__lowerCAmelCase = out_indices
__lowerCAmelCase = scope
def A__ ( self ) -> List[Any]:
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def A__ ( self ) -> List[Any]:
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=snake_case_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A__ ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[int]:
__lowerCAmelCase = ConvNextModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__lowerCAmelCase = model(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 A__ ( self , snake_case_ , snake_case_ , snake_case_ ) -> Dict:
__lowerCAmelCase = ConvNextForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
__lowerCAmelCase = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self , snake_case_ , snake_case_ , snake_case_ ) -> List[str]:
__lowerCAmelCase = ConvNextBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__lowerCAmelCase = model(snake_case_ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowerCAmelCase = None
__lowerCAmelCase = ConvNextBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__lowerCAmelCase = model(snake_case_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A__ ( self ) -> List[str]:
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( A__ , A__ , unittest.TestCase ):
'''simple docstring'''
_snake_case = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
_snake_case = (
{'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
if is_torch_available()
else {}
)
_snake_case = True
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
def A__ ( self ) -> int:
__lowerCAmelCase = ConvNextModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=37 )
def A__ ( self ) -> int:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self ) -> str:
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def A__ ( self ) -> str:
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def A__ ( self ) -> List[Any]:
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def A__ ( self ) -> Optional[int]:
pass
def A__ ( self ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(snake_case_ )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case_ )
def A__ ( self ) -> Union[str, Any]:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def A__ ( self ) -> Dict:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case_ )
def A__ ( self ) -> Union[str, Any]:
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
__lowerCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
__lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowerCAmelCase = self.model_tester.num_stages
self.assertEqual(len(snake_case_ ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def A__ ( self ) -> str:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def A__ ( self ) -> str:
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = ConvNextModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ():
__lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def A__ ( self ) -> Any:
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def A__ ( self ) -> Any:
__lowerCAmelCase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(snake_case_ )
__lowerCAmelCase = self.default_image_processor
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=snake_case_ , return_tensors="""pt""" ).to(snake_case_ )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**snake_case_ )
# verify the logits
__lowerCAmelCase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , snake_case_ )
__lowerCAmelCase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1e-4 ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase , A__ ):
'''simple docstring'''
_snake_case = (ConvNextBackbone,) if is_torch_available() else ()
_snake_case = ConvNextConfig
_snake_case = False
def A__ ( self ) -> Dict:
__lowerCAmelCase = ConvNextModelTester(self )
| 573 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 573 | 1 |
import qiskit
def UpperCAmelCase ( a_ , a_ ) -> qiskit.result.counts.Counts:
"""simple docstring"""
__A = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
__A = qiskit.QuantumCircuit(a_ , a_ )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
__A = qiskit.execute(a_ , a_ , shots=1_0_0_0 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = single_qubit_measure(2, 2)
print(f'''Total count for various states are: {counts}''')
| 55 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Any ):
__A = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A ,"tf_padding" ) )
self.parent.assertTrue(hasattr(A ,"depth_multiplier" ) )
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,A : int ,A : List[Any]=13 ,A : int=3 ,A : Optional[Any]=32 ,A : Union[str, Any]=0.25 ,A : Tuple=8 ,A : Optional[int]=True ,A : Union[str, Any]=10_24 ,A : Any=32 ,A : Optional[int]="relu6" ,A : int=0.1 ,A : Optional[Any]=0.02 ,A : Optional[Any]=True ,A : List[str]=True ,A : str=10 ,A : str=None ,):
__A = parent
__A = batch_size
__A = num_channels
__A = image_size
__A = depth_multiplier
__A = min_depth
__A = tf_padding
__A = int(last_hidden_size * depth_multiplier )
__A = output_stride
__A = hidden_act
__A = classifier_dropout_prob
__A = use_labels
__A = is_training
__A = num_labels
__A = initializer_range
__A = scope
def UpperCamelCase_ ( self : Optional[int] ):
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] ,self.num_labels )
__A = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
__A = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCamelCase_ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels ,image_size=self.image_size ,depth_multiplier=self.depth_multiplier ,min_depth=self.min_depth ,tf_padding=self.tf_padding ,hidden_act=self.hidden_act ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,)
def UpperCamelCase_ ( self : Optional[int] ,A : str ,A : Tuple ,A : Optional[int] ,A : List[str] ):
__A = MobileNetVaModel(config=A )
model.to(A )
model.eval()
__A = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape ,(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
def UpperCamelCase_ ( self : List[Any] ,A : Union[str, Any] ,A : List[Any] ,A : int ,A : Union[str, Any] ):
__A = self.num_labels
__A = MobileNetVaForImageClassification(A )
model.to(A )
model.eval()
__A = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : Tuple ):
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Any ):
__A = MobileNetVaModelTester(self )
__A = MobileNetVaConfigTester(self ,config_class=A ,has_text_modality=A )
def UpperCamelCase_ ( self : str ):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def UpperCamelCase_ ( self : Union[str, Any] ):
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def UpperCamelCase_ ( self : Any ):
pass
def UpperCamelCase_ ( self : Optional[int] ):
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(A )
__A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,A )
def UpperCamelCase_ ( self : List[Any] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : Optional[int] ):
def check_hidden_states_output(A : List[Any] ,A : List[Any] ,A : Optional[int] ):
__A = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
__A = model(**self._prepare_for_class(A ,A ) )
__A = outputs.hidden_states
__A = 26
self.assertEqual(len(A ) ,A )
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = True
check_hidden_states_output(A ,A ,A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A = True
check_hidden_states_output(A ,A ,A )
def UpperCamelCase_ ( self : Tuple ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = MobileNetVaModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCAmelCase ( ) -> str:
"""simple docstring"""
__A = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self : List[str] ):
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
__A = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(A )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
# verify the logits
__A = torch.Size((1, 10_01) )
self.assertEqual(outputs.logits.shape ,A )
__A = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A ,atol=1E-4 ) )
| 55 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A =logging.get_logger(__name__)
class _snake_case ( a__ ):
lowerCAmelCase :Optional[int] = ['''pixel_values''']
def __init__( self , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = PILImageResampling.BICUBIC , _lowerCamelCase = True , _lowerCamelCase = 1 / 255 , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = True , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase)
UpperCAmelCase__ : List[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCAmelCase__ : int = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
UpperCAmelCase__ : Union[str, Any] = do_resize
UpperCAmelCase__ : int = size
UpperCAmelCase__ : Union[str, Any] = resample
UpperCAmelCase__ : Union[str, Any] = do_rescale
UpperCAmelCase__ : Any = rescale_factor
UpperCAmelCase__ : Optional[int] = do_normalize
UpperCAmelCase__ : str = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase__ : Any = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase__ : int = do_convert_rgb
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = PILImageResampling.BICUBIC , _lowerCamelCase = None , **_lowerCamelCase , ):
UpperCAmelCase__ : List[str] = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
UpperCAmelCase__ : List[Any] = (size["""height"""], size["""width"""])
return resize(_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return rescale(_lowerCamelCase , scale=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = ChannelDimension.FIRST , **_lowerCamelCase , ):
UpperCAmelCase__ : List[Any] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : List[str] = resample if resample is not None else self.resample
UpperCAmelCase__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : int = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Optional[int] = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Union[str, Any] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : Dict = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase__ : Optional[int] = size if size is not None else self.size
UpperCAmelCase__ : Optional[Any] = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase)
UpperCAmelCase__ : Dict = make_list_of_images(_lowerCamelCase)
if not valid_images(_lowerCamelCase):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""")
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize 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.""")
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase__ : List[Any] = [convert_to_rgb(_lowerCamelCase) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase__ : int = [to_numpy_array(_lowerCamelCase) for image in images]
if do_resize:
UpperCAmelCase__ : Tuple = [self.resize(image=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase) for image in images]
if do_rescale:
UpperCAmelCase__ : Tuple = [self.rescale(image=_lowerCamelCase , scale=_lowerCamelCase) for image in images]
if do_normalize:
UpperCAmelCase__ : List[str] = [self.normalize(image=_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase) for image in images]
UpperCAmelCase__ : Tuple = [to_channel_dimension_format(_lowerCamelCase , _lowerCamelCase) for image in images]
UpperCAmelCase__ : Dict = BatchFeature(data={"""pixel_values""": images} , tensor_type=_lowerCamelCase)
return encoded_outputs | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A ={
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
'EncodecModel',
'EncodecPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
__A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 113 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = CLIPTokenizer
_SCREAMING_SNAKE_CASE :List[str] = CLIPTokenizerFast
_SCREAMING_SNAKE_CASE :Tuple = True
_SCREAMING_SNAKE_CASE :str = {}
_SCREAMING_SNAKE_CASE :Tuple = False
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE__ : Any = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
SCREAMING_SNAKE_CASE__ : List[Any] = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE__ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCamelCase_ ) )
def _a ( self , **_a ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _a ( self , **_a ) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _a ( self , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = 'lower newer'
SCREAMING_SNAKE_CASE__ : Dict = 'lower newer'
return input_text, output_text
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ : Optional[int] = 'lower newer'
SCREAMING_SNAKE_CASE__ : int = ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : int = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
@require_ftfy
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : List[str] = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : int = 'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer_s.tokenize(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer_r.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
SCREAMING_SNAKE_CASE__ : Tuple = 'xa\u0303y' + ' ' + 'x\xe3y'
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_s.tokenize(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer_r.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
# Test that the tokenization is identical on unicode of space type
SCREAMING_SNAKE_CASE__ : Tuple = [
'\u0009', # (horizontal tab, '\t')
'\u000B', # (vertical tab)
'\u000C', # (form feed)
'\u0020', # (space, ' ')
'\u200E', # (left-to-right mark):w
'\u200F', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_s.tokenize(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_r.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
# Test that the tokenization is identical on unicode of line break type
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
'\u000A', # (line feed, '\n')
'\r\n', # (carriage return and line feed, '\r\n')
'\u000D', # (carriage return, '\r')
'\r', # (carriage return, '\r')
'\u000D', # (carriage return, '\r')
'\u2028', # (line separator)
'\u2029', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_s.tokenize(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ : str = tokenizer_r.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : str = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE__ : int = f'''{text_of_1_token} {text_of_1_token}'''
SCREAMING_SNAKE_CASE__ : Dict = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase_ ) + 1, len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ : Dict = f''' {text}'''
SCREAMING_SNAKE_CASE__ : Dict = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase_ ) + 1, 1 + len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(UpperCamelCase_ ) as context:
self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer""" )
self.assertTrue(
context.exception.args[0].startswith(
"""The `backend_tokenizer` provided does not match the expected format.""" ) )
@require_ftfy
def _a ( self ) -> Any:
"""simple docstring"""
super().test_tokenization_python_rust_equals()
def _a ( self ) -> int:
"""simple docstring"""
pass
| 680 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def lowerCamelCase ( ):
UpperCAmelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument('--model_ckpt' ,type=_snake_case ,default='microsoft/unixcoder-base-nine' )
parser.add_argument('--num_epochs' ,type=_snake_case ,default=5 )
parser.add_argument('--batch_size' ,type=_snake_case ,default=6 )
parser.add_argument('--gradient_accumulation_steps' ,type=_snake_case ,default=1 )
parser.add_argument('--freeze' ,type=_snake_case ,default=_snake_case )
parser.add_argument('--learning_rate' ,type=_snake_case ,default=5e-4 )
parser.add_argument('--seed' ,type=_snake_case ,default=0 )
parser.add_argument('--lr_scheduler_type' ,type=_snake_case ,default='cosine' )
parser.add_argument('--num_warmup_steps' ,type=_snake_case ,default=10 )
parser.add_argument('--weight_decay' ,type=_snake_case ,default=0.01 )
parser.add_argument('--output_dir' ,type=_snake_case ,default='./results' )
return parser.parse_args()
UpperCamelCase__ = load('accuracy')
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ , UpperCAmelCase__ : int = eval_pred
UpperCAmelCase__ : Optional[int] = np.argmax(_snake_case ,axis=1 )
return metric.compute(predictions=_snake_case ,references=_snake_case )
class a ( lowercase ):
def __init__( self , UpperCamelCase_ ):
super().__init__()
UpperCAmelCase__ : List[str] = trainer
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ ):
if control.should_evaluate:
UpperCAmelCase__ : int = deepcopy(UpperCamelCase_ )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix='train' )
return control_copy
def lowerCamelCase ( ):
UpperCAmelCase__ : int = get_args()
set_seed(args.seed )
UpperCAmelCase__ : Optional[int] = load_dataset('codeparrot/codecomplex' ,split='train' )
UpperCAmelCase__ : Tuple = dataset.train_test_split(test_size=0.2 )
UpperCAmelCase__ : List[Any] = train_test['test'].train_test_split(test_size=0.5 )
UpperCAmelCase__ : Tuple = DatasetDict(
{
'train': train_test['train'],
'test': test_validation['train'],
'valid': test_validation['test'],
} )
print('Loading tokenizer and model' )
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(args.model_ckpt )
UpperCAmelCase__ : Optional[Any] = tokenizer.eos_token
UpperCAmelCase__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt ,num_labels=7 )
UpperCAmelCase__ : Dict = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : Optional[Any] = ClassLabel(num_classes=7 ,names=list(set(train_test_validation['train']['complexity'] ) ) )
def tokenize(_snake_case ):
UpperCAmelCase__ : Dict = tokenizer(example['src'] ,truncation=_snake_case ,max_length=1024 )
UpperCAmelCase__ : Any = labels.straint(example['complexity'] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
UpperCAmelCase__ : List[Any] = train_test_validation.map(
_snake_case ,batched=_snake_case ,remove_columns=train_test_validation['train'].column_names ,)
UpperCAmelCase__ : Tuple = DataCollatorWithPadding(tokenizer=_snake_case )
UpperCAmelCase__ : Union[str, Any] = TrainingArguments(
output_dir=args.output_dir ,learning_rate=args.learning_rate ,lr_scheduler_type=args.lr_scheduler_type ,evaluation_strategy='epoch' ,save_strategy='epoch' ,logging_strategy='epoch' ,per_device_train_batch_size=args.batch_size ,per_device_eval_batch_size=args.batch_size ,num_train_epochs=args.num_epochs ,gradient_accumulation_steps=args.gradient_accumulation_steps ,weight_decay=0.01 ,metric_for_best_model='accuracy' ,run_name='complexity-java' ,report_to='wandb' ,)
UpperCAmelCase__ : Dict = Trainer(
model=_snake_case ,args=_snake_case ,train_dataset=tokenized_datasets['train'] ,eval_dataset=tokenized_datasets['valid'] ,tokenizer=_snake_case ,data_collator=_snake_case ,compute_metrics=_snake_case ,)
print('Training...' )
trainer.add_callback(CustomCallback(_snake_case ) )
trainer.train()
if __name__ == "__main__":
main()
| 110 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRobertaXLOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMRobertaXLForCausalLM""",
"""XLMRobertaXLForMaskedLM""",
"""XLMRobertaXLForMultipleChoice""",
"""XLMRobertaXLForQuestionAnswering""",
"""XLMRobertaXLForSequenceClassification""",
"""XLMRobertaXLForTokenClassification""",
"""XLMRobertaXLModel""",
"""XLMRobertaXLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 700 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __UpperCamelCase ( a__ ):
_UpperCAmelCase = ["vqvae"]
def __init__( self ,_A ,_A ,_A ,_A ,):
'''simple docstring'''
super().__init__()
self.register_modules(unet=_A ,scheduler=_A ,mel=_A ,vqvae=_A )
def __lowerCamelCase ( self ):
'''simple docstring'''
return 50 if isinstance(self.scheduler ,_A ) else 1000
@torch.no_grad()
def __call__( self ,_A = 1 ,_A = None ,_A = None ,_A = 0 ,_A = 0 ,_A = None ,_A = None ,_A = 0 ,_A = 0 ,_A = None ,_A = 0 ,_A = None ,_A = None ,_A=True ,):
'''simple docstring'''
_lowerCAmelCase : List[str] = steps or self.get_default_steps()
self.scheduler.set_timesteps(_A )
_lowerCAmelCase : Optional[Any] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
_lowerCAmelCase : Tuple = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_lowerCAmelCase : Optional[Any] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) ,generator=_A ,device=self.device ,)
_lowerCAmelCase : Dict = noise
_lowerCAmelCase : Optional[Any] = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(_A ,_A )
_lowerCAmelCase : Union[str, Any] = self.mel.audio_slice_to_image(_A )
_lowerCAmelCase : int = np.frombuffer(input_image.tobytes() ,dtype='uint8' ).reshape(
(input_image.height, input_image.width) )
_lowerCAmelCase : int = (input_image / 255) * 2 - 1
_lowerCAmelCase : str = torch.tensor(input_image[np.newaxis, :, :] ,dtype=torch.float ).to(self.device )
if self.vqvae is not None:
_lowerCAmelCase : List[Any] = self.vqvae.encode(torch.unsqueeze(_A ,0 ) ).latent_dist.sample(
generator=_A )[0]
_lowerCAmelCase : Tuple = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_lowerCAmelCase : List[Any] = self.scheduler.add_noise(_A ,_A ,self.scheduler.timesteps[start_step - 1] )
_lowerCAmelCase : Optional[Any] = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_lowerCAmelCase : Optional[Any] = int(mask_start_secs * pixels_per_second )
_lowerCAmelCase : Optional[int] = int(mask_end_secs * pixels_per_second )
_lowerCAmelCase : int = self.scheduler.add_noise(_A ,_A ,torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet ,_A ):
_lowerCAmelCase : str = self.unet(_A ,_A ,_A )['sample']
else:
_lowerCAmelCase : Any = self.unet(_A ,_A )['sample']
if isinstance(self.scheduler ,_A ):
_lowerCAmelCase : Union[str, Any] = self.scheduler.step(
model_output=_A ,timestep=_A ,sample=_A ,eta=_A ,generator=_A ,)['prev_sample']
else:
_lowerCAmelCase : Any = self.scheduler.step(
model_output=_A ,timestep=_A ,sample=_A ,generator=_A ,)['prev_sample']
if mask is not None:
if mask_start > 0:
_lowerCAmelCase : Any = mask[:, step, :, :mask_start]
if mask_end > 0:
_lowerCAmelCase : Optional[Any] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_lowerCAmelCase : Union[str, Any] = 1 / self.vqvae.config.scaling_factor * images
_lowerCAmelCase : Any = self.vqvae.decode(_A )['sample']
_lowerCAmelCase : Any = (images / 2 + 0.5).clamp(0 ,1 )
_lowerCAmelCase : Tuple = images.cpu().permute(0 ,2 ,3 ,1 ).numpy()
_lowerCAmelCase : Any = (images * 255).round().astype('uint8' )
_lowerCAmelCase : Any = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_A ,mode='RGB' ).convert('L' ) for _ in images) )
_lowerCAmelCase : Dict = [self.mel.image_to_audio(_A ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(_A )[:, np.newaxis, :] ) ,**ImagePipelineOutput(_A ) )
@torch.no_grad()
def __lowerCamelCase ( self ,_A ,_A = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler ,_A )
self.scheduler.set_timesteps(_A )
_lowerCAmelCase : Dict = np.array(
[np.frombuffer(image.tobytes() ,dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
_lowerCAmelCase : Dict = (sample / 255) * 2 - 1
_lowerCAmelCase : List[str] = torch.Tensor(_A ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps ,(0,) ) ):
_lowerCAmelCase : Tuple = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_lowerCAmelCase : Optional[int] = self.scheduler.alphas_cumprod[t]
_lowerCAmelCase : Dict = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_lowerCAmelCase : Union[str, Any] = 1 - alpha_prod_t
_lowerCAmelCase : Union[str, Any] = self.unet(_A ,_A )['sample']
_lowerCAmelCase : Optional[int] = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_lowerCAmelCase : Any = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_lowerCAmelCase : Dict = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __lowerCamelCase ( _A ,_A ,_A ):
'''simple docstring'''
_lowerCAmelCase : int = acos(torch.dot(torch.flatten(_A ) ,torch.flatten(_A ) ) / torch.norm(_A ) / torch.norm(_A ) )
return sin((1 - alpha) * theta ) * xa / sin(_A ) + sin(alpha * theta ) * xa / sin(_A )
| 16 | 0 |
import baseaa
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
return baseaa.aaadecode(SCREAMING_SNAKE_CASE__ ).decode('''utf-8''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 445 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaadecode(SCREAMING_SNAKE_CASE__ ).decode("utf-8" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 672 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _SCREAMING_SNAKE_CASE ( A : Tuple ) -> List[Any]:
"""simple docstring"""
__snake_case : Optional[int] = 3_84
if "tiny" in model_name:
__snake_case : Any = [3, 3, 9, 3]
__snake_case : Union[str, Any] = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
__snake_case : int = [3, 3, 27, 3]
__snake_case : Dict = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
__snake_case : Tuple = [3, 3, 27, 3]
__snake_case : List[Any] = [1_28, 2_56, 5_12, 10_24]
__snake_case : Union[str, Any] = 5_12
if "large" in model_name:
__snake_case : int = [3, 3, 27, 3]
__snake_case : Union[str, Any] = [1_92, 3_84, 7_68, 15_36]
__snake_case : Optional[Any] = 7_68
if "xlarge" in model_name:
__snake_case : Dict = [3, 3, 27, 3]
__snake_case : str = [2_56, 5_12, 10_24, 20_48]
__snake_case : List[str] = 10_24
# set label information
__snake_case : Any = 1_50
__snake_case : Optional[int] = '''huggingface/label-files'''
__snake_case : Union[str, Any] = '''ade20k-id2label.json'''
__snake_case : List[Any] = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='dataset' ) , 'r' ) )
__snake_case : Any = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
__snake_case : Any = {v: k for k, v in idalabel.items()}
__snake_case : int = ConvNextConfig(
depths=__UpperCamelCase , hidden_sizes=__UpperCamelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
__snake_case : int = UperNetConfig(
backbone_config=__UpperCamelCase , auxiliary_in_channels=__UpperCamelCase , num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase , )
return config
def _SCREAMING_SNAKE_CASE ( A : Dict ) -> Dict:
"""simple docstring"""
__snake_case : Optional[Any] = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.stages.{i}.{j}.gamma""", F"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.norm.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.norm.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""") )
if i > 0:
rename_keys.append((F"""backbone.downsample_layers.{i}.0.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.0.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.1.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.1.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def _SCREAMING_SNAKE_CASE ( A : Any , A : List[Any] , A : Optional[int] ) -> Dict:
"""simple docstring"""
__snake_case : Optional[Any] = dct.pop(__UpperCamelCase )
__snake_case : Dict = val
def _SCREAMING_SNAKE_CASE ( A : List[Any] , A : str , A : Optional[Any] ) -> Tuple:
"""simple docstring"""
__snake_case : int = {
'''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
'''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
'''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
'''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
'''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
}
__snake_case : Dict = model_name_to_url[model_name]
__snake_case : str = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location='cpu' )['''state_dict''']
__snake_case : Dict = get_upernet_config(__UpperCamelCase )
__snake_case : Union[str, Any] = UperNetForSemanticSegmentation(__UpperCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__snake_case : int = state_dict.pop(__UpperCamelCase )
if "bn" in key:
__snake_case : Union[str, Any] = key.replace('bn' , 'batch_norm' )
__snake_case : Union[str, Any] = val
# rename keys
__snake_case : List[str] = create_rename_keys(__UpperCamelCase )
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
# verify on image
__snake_case : List[Any] = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
__snake_case : int = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw ).convert('RGB' )
__snake_case : Union[str, Any] = SegformerImageProcessor()
__snake_case : Union[str, Any] = processor(__UpperCamelCase , return_tensors='pt' ).pixel_values
with torch.no_grad():
__snake_case : Dict = model(__UpperCamelCase )
if model_name == "upernet-convnext-tiny":
__snake_case : Union[str, Any] = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
__snake_case : List[str] = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
__snake_case : Any = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
__snake_case : Union[str, Any] = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
__snake_case : List[Any] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __UpperCamelCase , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCamelCase )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print(F"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(F"""openmmlab/{model_name}""" )
processor.push_to_hub(F"""openmmlab/{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''upernet-convnext-tiny''',
type=str,
choices=[f'''upernet-convnext-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
help='''Name of the ConvNext UperNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__A = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 711 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a_ ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
_snake_case = VQModel
_snake_case = """sample"""
@property
def SCREAMING_SNAKE_CASE__ (self , __a=(3_2, 3_2)) -> str:
"""simple docstring"""
__snake_case : Dict = 4
__snake_case : Optional[int] = 3
__snake_case : str = floats_tensor((batch_size, num_channels) + sizes).to(__a)
return {"sample": image}
@property
def SCREAMING_SNAKE_CASE__ (self) -> Union[str, Any]:
"""simple docstring"""
return (3, 3_2, 3_2)
@property
def SCREAMING_SNAKE_CASE__ (self) -> List[Any]:
"""simple docstring"""
return (3, 3_2, 3_2)
def SCREAMING_SNAKE_CASE__ (self) -> Optional[int]:
"""simple docstring"""
__snake_case : Optional[Any] = {
'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': 3,
}
__snake_case : List[Any] = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE__ (self) -> Any:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ (self) -> Union[str, Any]:
"""simple docstring"""
__snake_case ,__snake_case : List[Any] = VQModel.from_pretrained('fusing/vqgan-dummy' , output_loading_info=__a)
self.assertIsNotNone(__a)
self.assertEqual(len(loading_info['missing_keys']) , 0)
model.to(__a)
__snake_case : Any = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE__ (self) -> Optional[int]:
"""simple docstring"""
__snake_case : Union[str, Any] = VQModel.from_pretrained('fusing/vqgan-dummy')
model.to(__a).eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
__snake_case : Tuple = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size)
__snake_case : Optional[int] = image.to(__a)
with torch.no_grad():
__snake_case : List[Any] = model(__a).sample
__snake_case : int = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
__snake_case : int = torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
# fmt: on
self.assertTrue(torch.allclose(__a , __a , atol=1E-3)) | 61 | 0 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_lowercase = False
_lowercase = logging.get_logger(__name__)
_lowercase = "ybelkada/fonts"
def lowerCAmelCase__ ( ) ->Optional[Any]:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"Pix2StructImageProcessor. Please upgrade torch." )
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ ) ->str:
requires_backends(_lowerCAmelCase , ["torch"] )
_check_torch_version()
__lowercase = image_tensor.unsqueeze(0 )
__lowercase = torch.nn.functional.unfold(_lowerCAmelCase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
__lowercase = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , _lowerCAmelCase , _lowerCAmelCase , -1 )
__lowercase = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ = 3_6 , __magic_name__ = "black" , __magic_name__ = "white" , __magic_name__ = 5 , __magic_name__ = 5 , __magic_name__ = 5 , __magic_name__ = 5 , __magic_name__ = None , __magic_name__ = None , ) ->str:
requires_backends(_lowerCAmelCase , "vision" )
# Add new lines so that each line is no more than 80 characters.
__lowercase = textwrap.TextWrapper(width=8_0 )
__lowercase = wrapper.wrap(text=_lowerCAmelCase )
__lowercase = '\n'.join(_lowerCAmelCase )
if font_bytes is not None and font_path is None:
__lowercase = io.BytesIO(_lowerCAmelCase )
elif font_path is not None:
__lowercase = font_path
else:
__lowercase = hf_hub_download(_lowerCAmelCase , "Arial.TTF" )
__lowercase = ImageFont.truetype(_lowerCAmelCase , encoding="UTF-8" , size=_lowerCAmelCase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
__lowercase = ImageDraw.Draw(Image.new("RGB" , (1, 1) , _lowerCAmelCase ) )
__lowercase = temp_draw.textbbox((0, 0) , _lowerCAmelCase , _lowerCAmelCase )
# Create the actual image with a bit of padding around the text.
__lowercase = text_width + left_padding + right_padding
__lowercase = text_height + top_padding + bottom_padding
__lowercase = Image.new("RGB" , (image_width, image_height) , _lowerCAmelCase )
__lowercase = ImageDraw.Draw(_lowerCAmelCase )
draw.text(xy=(left_padding, top_padding) , text=_lowerCAmelCase , fill=_lowerCAmelCase , font=_lowerCAmelCase )
return image
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ , **__magic_name__ ) ->str:
requires_backends(_lowerCAmelCase , "vision" )
# Convert to PIL image if necessary
__lowercase = to_pil_image(_lowerCAmelCase )
__lowercase = render_text(_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = max(header_image.width , image.width )
__lowercase = int(image.height * (new_width / image.width) )
__lowercase = int(header_image.height * (new_width / header_image.width) )
__lowercase = Image.new("RGB" , (new_width, new_height + new_header_height) , "white" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
__lowercase = to_numpy_array(_lowerCAmelCase )
if infer_channel_dimension_format(_lowerCAmelCase ) == ChannelDimension.LAST:
__lowercase = to_channel_dimension_format(_lowerCAmelCase , ChannelDimension.LAST )
return new_image
class __a ( _a ):
'''simple docstring'''
_lowerCamelCase : Any = ["flattened_patches"]
def __init__( self , _lowerCamelCase = True , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 2_048 , _lowerCamelCase = False , **_lowerCamelCase , ) -> None:
'''simple docstring'''
super().__init__(**_lowerCamelCase )
__lowercase = patch_size if patch_size is not None else {'height': 16, 'width': 16}
__lowercase = do_normalize
__lowercase = do_convert_rgb
__lowercase = max_patches
__lowercase = is_vqa
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) -> np.ndarray:
'''simple docstring'''
requires_backends(self.extract_flattened_patches , "torch" )
_check_torch_version()
# convert to torch
__lowercase = to_channel_dimension_format(_lowerCamelCase , ChannelDimension.FIRST )
__lowercase = torch.from_numpy(_lowerCamelCase )
__lowercase = patch_size['height'], patch_size['width']
__lowercase = get_image_size(_lowerCamelCase )
# maximize scale s.t.
__lowercase = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
__lowercase = max(min(math.floor(scale * image_height / patch_height ) , _lowerCamelCase ) , 1 )
__lowercase = max(min(math.floor(scale * image_width / patch_width ) , _lowerCamelCase ) , 1 )
__lowercase = max(num_feasible_rows * patch_height , 1 )
__lowercase = max(num_feasible_cols * patch_width , 1 )
__lowercase = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="bilinear" , align_corners=_lowerCamelCase , antialias=_lowerCamelCase , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
__lowercase = torch_extract_patches(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__lowercase = patches.shape
__lowercase = patches_shape[1]
__lowercase = patches_shape[2]
__lowercase = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
__lowercase = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
__lowercase = torch.arange(_lowerCamelCase ).reshape([rows, 1] ).repeat(1 , _lowerCamelCase ).reshape([rows * columns, 1] )
__lowercase = torch.arange(_lowerCamelCase ).reshape([1, columns] ).repeat(_lowerCamelCase , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
__lowercase = row_ids.to(torch.floataa )
__lowercase = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
__lowercase = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
__lowercase = torch.nn.functional.pad(_lowerCamelCase , [0, 0, 0, max_patches - (rows * columns)] ).float()
__lowercase = to_numpy_array(_lowerCamelCase )
return result
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase ) -> np.ndarray:
'''simple docstring'''
if image.dtype == np.uinta:
__lowercase = image.astype(np.floataa )
# take mean across the whole `image`
__lowercase = np.mean(_lowerCamelCase )
__lowercase = np.std(_lowerCamelCase )
__lowercase = max(_lowerCamelCase , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = ChannelDimension.FIRST , **_lowerCamelCase , ) -> ImageInput:
'''simple docstring'''
__lowercase = do_normalize if do_normalize is not None else self.do_normalize
__lowercase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowercase = patch_size if patch_size is not None else self.patch_size
__lowercase = max_patches if max_patches is not None else self.max_patches
__lowercase = self.is_vqa
if kwargs.get("data_format" , _lowerCamelCase ) is not None:
raise ValueError("data_format is not an accepted input as the outputs are " )
__lowercase = make_list_of_images(_lowerCamelCase )
if not valid_images(_lowerCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowercase = [convert_to_rgb(_lowerCamelCase ) for image in images]
# All transformations expect numpy arrays.
__lowercase = [to_numpy_array(_lowerCamelCase ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models." )
__lowercase = kwargs.pop("font_bytes" , _lowerCamelCase )
__lowercase = kwargs.pop("font_path" , _lowerCamelCase )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__lowercase = [header_text] * len(_lowerCamelCase )
__lowercase = [
render_header(_lowerCamelCase , header_text[i] , font_bytes=_lowerCamelCase , font_path=_lowerCamelCase )
for i, image in enumerate(_lowerCamelCase )
]
if do_normalize:
__lowercase = [self.normalize(image=_lowerCamelCase ) for image in images]
# convert to torch tensor and permute
__lowercase = [
self.extract_flattened_patches(image=_lowerCamelCase , max_patches=_lowerCamelCase , patch_size=_lowerCamelCase )
for image in images
]
# create attention mask in numpy
__lowercase = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
__lowercase = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=_lowerCamelCase )
return encoded_outputs
| 118 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Optional[Any] = {
"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 : List[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 : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 599 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_A = logging.get_logger(__name__)
def lowercase_ ( __UpperCAmelCase ) -> List[List[ImageInput]]:
if isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__SCREAMING_SNAKE_CASE ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class _lowerCamelCase ( lowercase__ ):
_lowerCamelCase :List[Any] = ['pixel_values']
def __init__( self : Union[str, Any] , UpperCamelCase : Union[str, Any] = True , UpperCamelCase : int = None , UpperCamelCase : int = PILImageResampling.BILINEAR , UpperCamelCase : Dict = True , UpperCamelCase : str = None , UpperCamelCase : int = True , UpperCamelCase : Tuple = 1 / 2_55 , UpperCamelCase : List[Any] = True , UpperCamelCase : Optional[int] = None , UpperCamelCase : Dict = None , **UpperCamelCase : Optional[int] , ) -> int:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : str = size if size is not None else {"""shortest_edge""": 2_24}
lowerCAmelCase__ : Optional[int] = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
lowerCAmelCase__ : Dict = get_size_dict(UpperCamelCase , param_name="""crop_size""" )
lowerCAmelCase__ : int = do_resize
lowerCAmelCase__ : Any = size
lowerCAmelCase__ : Any = do_center_crop
lowerCAmelCase__ : Any = crop_size
lowerCAmelCase__ : Optional[Any] = resample
lowerCAmelCase__ : int = do_rescale
lowerCAmelCase__ : Optional[int] = rescale_factor
lowerCAmelCase__ : Optional[int] = do_normalize
lowerCAmelCase__ : List[str] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[int] , UpperCamelCase : Any = PILImageResampling.BILINEAR , UpperCamelCase : List[Any] = None , **UpperCamelCase : Optional[Any] , ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
if "shortest_edge" in size:
lowerCAmelCase__ : int = get_resize_output_image_size(UpperCamelCase , size["""shortest_edge"""] , default_to_square=UpperCamelCase )
elif "height" in size and "width" in size:
lowerCAmelCase__ : int = (size["""height"""], size["""width"""])
else:
raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}""" )
return resize(UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : int , UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : Tuple = None , **UpperCamelCase : int , ) -> int:
"""simple docstring"""
lowerCAmelCase__ : int = get_size_dict(UpperCamelCase )
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(UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : int , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Any = None , **UpperCamelCase : str , ) -> Optional[Any]:
"""simple docstring"""
return rescale(UpperCamelCase , scale=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : str , UpperCamelCase : str , UpperCamelCase : Dict , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] = None , **UpperCamelCase : Union[str, Any] , ) -> Tuple:
"""simple docstring"""
return normalize(UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : Dict , UpperCamelCase : str , UpperCamelCase : str = None , UpperCamelCase : List[Any] = None , UpperCamelCase : Tuple = None , UpperCamelCase : Dict = None , UpperCamelCase : str = None , UpperCamelCase : Any = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : Tuple = None , UpperCamelCase : Union[str, Any] = None , UpperCamelCase : List[str] = None , UpperCamelCase : Optional[int] = ChannelDimension.FIRST , ) -> List[Any]:
"""simple docstring"""
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.""" )
# All transformations expect numpy arrays.
lowerCAmelCase__ : Union[str, Any] = to_numpy_array(UpperCamelCase )
if do_resize:
lowerCAmelCase__ : Optional[int] = self.resize(image=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase )
if do_center_crop:
lowerCAmelCase__ : Optional[Any] = self.center_crop(UpperCamelCase , size=UpperCamelCase )
if do_rescale:
lowerCAmelCase__ : List[str] = self.rescale(image=UpperCamelCase , scale=UpperCamelCase )
if do_normalize:
lowerCAmelCase__ : Optional[int] = self.normalize(image=UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase )
lowerCAmelCase__ : List[Any] = to_channel_dimension_format(UpperCamelCase , UpperCamelCase )
return image
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : Any , UpperCamelCase : List[Any] = None , UpperCamelCase : Any = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : List[str] = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : Optional[Any] = None , UpperCamelCase : Any = None , UpperCamelCase : List[str] = None , UpperCamelCase : Dict = None , UpperCamelCase : Tuple = None , UpperCamelCase : List[str] = None , UpperCamelCase : Optional[int] = ChannelDimension.FIRST , **UpperCamelCase : Tuple , ) -> str:
"""simple docstring"""
lowerCAmelCase__ : str = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ : Any = resample if resample is not None else self.resample
lowerCAmelCase__ : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ : Tuple = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ : int = image_std if image_std is not None else self.image_std
lowerCAmelCase__ : Optional[Any] = size if size is not None else self.size
lowerCAmelCase__ : List[Any] = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
lowerCAmelCase__ : Any = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ : List[str] = get_size_dict(UpperCamelCase , param_name="""crop_size""" )
if not valid_images(UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowerCAmelCase__ : Optional[Any] = make_batched(UpperCamelCase )
lowerCAmelCase__ : Any = [
[
self._preprocess_image(
image=UpperCamelCase , do_resize=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , do_center_crop=UpperCamelCase , crop_size=UpperCamelCase , do_rescale=UpperCamelCase , rescale_factor=UpperCamelCase , do_normalize=UpperCamelCase , image_mean=UpperCamelCase , image_std=UpperCamelCase , data_format=UpperCamelCase , )
for img in video
]
for video in videos
]
lowerCAmelCase__ : List[str] = {"""pixel_values""": videos}
return BatchFeature(data=UpperCamelCase , tensor_type=UpperCamelCase )
| 701 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _lowerCamelCase ( a_ , unittest.TestCase ):
_lowerCamelCase :Dict = FlaxAutoencoderKL
@property
def _lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = 4
lowerCAmelCase__ : Tuple = 3
lowerCAmelCase__ : Union[str, Any] = (32, 32)
lowerCAmelCase__ : Dict = jax.random.PRNGKey(0 )
lowerCAmelCase__ : Optional[int] = jax.random.uniform(UpperCamelCase , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Dict = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
lowerCAmelCase__ : List[Any] = self.dummy_input
return init_dict, inputs_dict
| 507 | 0 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( a__ , a__) -> List[str]:
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_) or not all(isinstance(UpperCAmelCase_ , UpperCAmelCase_) for day in days):
raise ValueError('The parameter days should be a list of integers')
if len(UpperCAmelCase_) != 3 or not all(isinstance(UpperCAmelCase_ , UpperCAmelCase_) for cost in costs):
raise ValueError('The parameter costs should be a list of three integers')
if len(UpperCAmelCase_) == 0:
return 0
if min(UpperCAmelCase_) <= 0:
raise ValueError('All days elements should be greater than 0')
if max(UpperCAmelCase_) >= 3_6_6:
raise ValueError('All days elements should be less than 366')
_snake_case : str = set(UpperCAmelCase_)
@functools.cache
def dynamic_programming(a__) -> int:
if index > 3_6_5:
return 0
if index not in days_set:
return dynamic_programming(index + 1)
return min(
costs[0] + dynamic_programming(index + 1) , costs[1] + dynamic_programming(index + 7) , costs[2] + dynamic_programming(index + 3_0) , )
return dynamic_programming(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 517 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
"""simple docstring"""
snake_case__ , snake_case__ : List[Any] = coefficient_matrix.shape
snake_case__ , snake_case__ : Optional[Any] = constant_matrix.shape
if rowsa != colsa:
snake_case__ : List[str] = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
raise ValueError(UpperCAmelCase_)
if colsa != 1:
snake_case__ : Tuple = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
raise ValueError(UpperCAmelCase_)
if rowsa != rowsa:
snake_case__ : List[Any] = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
)
raise ValueError(UpperCAmelCase_)
if len(UpperCAmelCase_) != rowsa:
snake_case__ : Any = (
"""Number of initial values must be equal to number of rows in coefficient """
F'matrix but received {len(UpperCAmelCase_)} and {rowsa}'
)
raise ValueError(UpperCAmelCase_)
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""")
snake_case__ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1)
snake_case__ , snake_case__ : Optional[int] = table.shape
strictly_diagonally_dominant(UpperCAmelCase_)
# Iterates the whole matrix for given number of times
for _ in range(UpperCAmelCase_):
snake_case__ : Union[str, Any] = []
for row in range(UpperCAmelCase_):
snake_case__ : Union[str, Any] = 0
for col in range(UpperCAmelCase_):
if col == row:
snake_case__ : List[Any] = table[row][col]
elif col == cols - 1:
snake_case__ : Any = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
snake_case__ : Union[str, Any] = (temp + val) / denom
new_val.append(UpperCAmelCase_)
snake_case__ : List[Any] = new_val
return [float(UpperCAmelCase_) for i in new_val]
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ , snake_case__ : Optional[int] = table.shape
snake_case__ : Union[str, Any] = True
for i in range(0 , UpperCAmelCase_):
snake_case__ : Tuple = 0
for j in range(0 , cols - 1):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""")
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 648 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(a_ )
class _snake_case ( a_ ):
def __init__( self , **_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(**UpperCamelCase_ )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return super().__call__(UpperCamelCase_ , **UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = {}
if "candidate_labels" in kwargs:
lowerCAmelCase = kwargs['candidate_labels']
if "hypothesis_template" in kwargs:
lowerCAmelCase = kwargs['hypothesis_template']
return preprocess_params, {}, {}
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="This is a photo of {}." ):
'''simple docstring'''
lowerCAmelCase = load_image(UpperCamelCase_ )
lowerCAmelCase = self.image_processor(images=[image] , return_tensors=self.framework )
lowerCAmelCase = candidate_labels
lowerCAmelCase = [hypothesis_template.format(UpperCamelCase_ ) for x in candidate_labels]
lowerCAmelCase = self.tokenizer(UpperCamelCase_ , return_tensors=self.framework , padding=UpperCamelCase_ )
lowerCAmelCase = [text_inputs]
return inputs
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = model_inputs.pop('candidate_labels' )
lowerCAmelCase = model_inputs.pop('text_inputs' )
if isinstance(text_inputs[0] , UpperCamelCase_ ):
lowerCAmelCase = text_inputs[0]
else:
# Batching case.
lowerCAmelCase = text_inputs[0][0]
lowerCAmelCase = self.model(**UpperCamelCase_ , **UpperCamelCase_ )
lowerCAmelCase = {
'candidate_labels': candidate_labels,
'logits': outputs.logits_per_image,
}
return model_outputs
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = model_outputs.pop('candidate_labels' )
lowerCAmelCase = model_outputs['logits'][0]
if self.framework == "pt":
lowerCAmelCase = logits.softmax(dim=-1 ).squeeze(-1 )
lowerCAmelCase = probs.tolist()
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowerCAmelCase = [scores]
elif self.framework == "tf":
lowerCAmelCase = stable_softmax(UpperCamelCase_ , axis=-1 )
lowerCAmelCase = probs.numpy().tolist()
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
lowerCAmelCase = [
{'score': score, 'label': candidate_label}
for score, candidate_label in sorted(zip(UpperCamelCase_ , UpperCamelCase_ ) , key=lambda _SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 704 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : str = logging.get_logger(__name__)
_UpperCamelCase : Any = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
_UpperCamelCase : Dict = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
_UpperCamelCase : Optional[Any] = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
@lru_cache()
def snake_case ( ) -> int:
"""simple docstring"""
lowerCAmelCase = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
lowerCAmelCase = bs[:]
lowerCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(snake_case )
cs.append(2**8 + n )
n += 1
lowerCAmelCase = [chr(snake_case ) for n in cs]
return dict(zip(snake_case , snake_case ) )
def snake_case ( snake_case : Any ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = set()
lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase = char
return pairs
class _snake_case ( a_ ):
SCREAMING_SNAKE_CASE : List[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : Optional[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="replace" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ):
'''simple docstring'''
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else bos_token
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else eos_token
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else sep_token
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else cls_token
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else unk_token
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
errors=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as vocab_handle:
lowerCAmelCase = json.load(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = {v: k for k, v in self.encoder.items()}
lowerCAmelCase = errors # how to handle errors in decoding
lowerCAmelCase = bytes_to_unicode()
lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as merges_handle:
lowerCAmelCase = merges_handle.read().split('\n' )[1:-1]
lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase = dict(zip(_SCREAMING_SNAKE_CASE , range(len(_SCREAMING_SNAKE_CASE ) ) ) )
lowerCAmelCase = {}
lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase = re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@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 , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase = tuple(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = get_pairs(_SCREAMING_SNAKE_CASE )
if not pairs:
return token
while True:
lowerCAmelCase = min(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase , lowerCAmelCase = bigram
lowerCAmelCase = []
lowerCAmelCase = 0
while i < len(_SCREAMING_SNAKE_CASE ):
try:
lowerCAmelCase = word.index(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase = j
if word[i] == first and i < len(_SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase = tuple(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = new_word
if len(_SCREAMING_SNAKE_CASE ) == 1:
break
else:
lowerCAmelCase = get_pairs(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = ' '.join(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = word
return word
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = []
for token in re.findall(self.pat , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE ).split(' ' ) )
return bpe_tokens
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.encoder.get(_SCREAMING_SNAKE_CASE , self.encoder.get(self.unk_token ) )
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.decoder.get(_SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = ''.join(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE ) + '\n' )
lowerCAmelCase = 0
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _SCREAMING_SNAKE_CASE : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
' Please check that the tokenizer is not corrupted!' )
lowerCAmelCase = token_index
writer.write(' '.join(_SCREAMING_SNAKE_CASE ) + '\n' )
index += 1
return vocab_file, merge_file
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 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 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
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]
def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE ) > 0 and not text[0].isspace()):
lowerCAmelCase = ' ' + text
return (text, kwargs)
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