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import math
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowerCamelCase )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("""This should never happen""" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
UpperCAmelCase_ : Any = "Enter the base and the power separated by a comma: "
UpperCAmelCase_ , UpperCAmelCase_ : Any = map(int, input(prompt).split(","))
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = map(int, input(prompt).split(","))
# We find the log of each number, using the function res(), which takes two
# arguments.
UpperCAmelCase_ : Union[str, Any] = res(xa, ya)
UpperCAmelCase_ : List[str] = res(xa, ya)
# We check for the largest number
if resa > resa:
print("Largest number is", xa, "^", ya)
elif resa > resa:
print("Largest number is", xa, "^", ya)
else:
print("Both are equal")
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
__magic_name__ : Tuple =tau * frequency / samplerate
__magic_name__ : int =sin(lowerCamelCase )
__magic_name__ : Optional[Any] =cos(lowerCamelCase )
__magic_name__ : str =_sin / (2 * q_factor)
__magic_name__ : Tuple =(1 - _cos) / 2
__magic_name__ : int =1 - _cos
__magic_name__ : str =1 + alpha
__magic_name__ : int =-2 * _cos
__magic_name__ : Dict =1 - alpha
__magic_name__ : str =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
__magic_name__ : List[Any] =tau * frequency / samplerate
__magic_name__ : Tuple =sin(lowerCamelCase )
__magic_name__ : List[str] =cos(lowerCamelCase )
__magic_name__ : Optional[Any] =_sin / (2 * q_factor)
__magic_name__ : Optional[Any] =(1 + _cos) / 2
__magic_name__ : List[str] =-1 - _cos
__magic_name__ : Any =1 + alpha
__magic_name__ : List[Any] =-2 * _cos
__magic_name__ : Any =1 - alpha
__magic_name__ : Optional[int] =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
__magic_name__ : Optional[Any] =tau * frequency / samplerate
__magic_name__ : int =sin(lowerCamelCase )
__magic_name__ : Optional[int] =cos(lowerCamelCase )
__magic_name__ : str =_sin / (2 * q_factor)
__magic_name__ : Dict =_sin / 2
__magic_name__ : Dict =0
__magic_name__ : Union[str, Any] =-ba
__magic_name__ : Union[str, Any] =1 + alpha
__magic_name__ : Optional[Any] =-2 * _cos
__magic_name__ : Optional[int] =1 - alpha
__magic_name__ : Dict =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
__magic_name__ : Any =tau * frequency / samplerate
__magic_name__ : Tuple =sin(lowerCamelCase )
__magic_name__ : int =cos(lowerCamelCase )
__magic_name__ : Optional[int] =_sin / (2 * q_factor)
__magic_name__ : List[Any] =1 - alpha
__magic_name__ : Optional[int] =-2 * _cos
__magic_name__ : Union[str, Any] =1 + alpha
__magic_name__ : Optional[Any] =IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
__magic_name__ : Dict =tau * frequency / samplerate
__magic_name__ : List[str] =sin(lowerCamelCase )
__magic_name__ : List[str] =cos(lowerCamelCase )
__magic_name__ : List[Any] =_sin / (2 * q_factor)
__magic_name__ : Union[str, Any] =10 ** (gain_db / 40)
__magic_name__ : Any =1 + alpha * big_a
__magic_name__ : List[Any] =-2 * _cos
__magic_name__ : Union[str, Any] =1 - alpha * big_a
__magic_name__ : Tuple =1 + alpha / big_a
__magic_name__ : List[str] =-2 * _cos
__magic_name__ : Dict =1 - alpha / big_a
__magic_name__ : int =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
__magic_name__ : Any =tau * frequency / samplerate
__magic_name__ : int =sin(lowerCamelCase )
__magic_name__ : Any =cos(lowerCamelCase )
__magic_name__ : Optional[Any] =_sin / (2 * q_factor)
__magic_name__ : List[Any] =10 ** (gain_db / 40)
__magic_name__ : List[str] =(big_a + 1) - (big_a - 1) * _cos
__magic_name__ : Dict =(big_a + 1) + (big_a - 1) * _cos
__magic_name__ : List[str] =(big_a - 1) - (big_a + 1) * _cos
__magic_name__ : int =(big_a - 1) + (big_a + 1) * _cos
__magic_name__ : List[str] =2 * sqrt(lowerCamelCase ) * alpha
__magic_name__ : Optional[int] =big_a * (pmc + aaa)
__magic_name__ : Any =2 * big_a * mpc
__magic_name__ : Optional[int] =big_a * (pmc - aaa)
__magic_name__ : str =ppmc + aaa
__magic_name__ : Optional[Any] =-2 * pmpc
__magic_name__ : Dict =ppmc - aaa
__magic_name__ : Dict =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
__magic_name__ : Optional[int] =tau * frequency / samplerate
__magic_name__ : Dict =sin(lowerCamelCase )
__magic_name__ : Tuple =cos(lowerCamelCase )
__magic_name__ : Any =_sin / (2 * q_factor)
__magic_name__ : Tuple =10 ** (gain_db / 40)
__magic_name__ : Optional[int] =(big_a + 1) - (big_a - 1) * _cos
__magic_name__ : Optional[int] =(big_a + 1) + (big_a - 1) * _cos
__magic_name__ : Dict =(big_a - 1) - (big_a + 1) * _cos
__magic_name__ : Optional[int] =(big_a - 1) + (big_a + 1) * _cos
__magic_name__ : Dict =2 * sqrt(lowerCamelCase ) * alpha
__magic_name__ : int =big_a * (ppmc + aaa)
__magic_name__ : Optional[int] =-2 * big_a * pmpc
__magic_name__ : Optional[Any] =big_a * (ppmc - aaa)
__magic_name__ : int =pmc + aaa
__magic_name__ : Union[str, Any] =2 * mpc
__magic_name__ : Tuple =pmc - aaa
__magic_name__ : List[Any] =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 21 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """gptsan-japanese"""
UpperCamelCase = [
"""past_key_values""",
]
UpperCamelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self :str , __snake_case :str=3_60_00 , __snake_case :List[Any]=12_80 , __snake_case :Tuple=10_24 , __snake_case :Union[str, Any]=81_92 , __snake_case :Dict=40_96 , __snake_case :Tuple=1_28 , __snake_case :Union[str, Any]=10 , __snake_case :List[Any]=0 , __snake_case :int=16 , __snake_case :Tuple=16 , __snake_case :int=1_28 , __snake_case :Optional[Any]=0.0 , __snake_case :Any=1E-5 , __snake_case :str=False , __snake_case :Dict=0.0 , __snake_case :str="float32" , __snake_case :int=False , __snake_case :int=False , __snake_case :Optional[int]=False , __snake_case :Tuple=0.002 , __snake_case :Any=False , __snake_case :Optional[Any]=True , __snake_case :Optional[int]=3_59_98 , __snake_case :Dict=3_59_95 , __snake_case :Optional[int]=3_59_99 , **__snake_case :Union[str, Any] , ):
'''simple docstring'''
__magic_name__ : List[str] =vocab_size
__magic_name__ : Any =max_position_embeddings
__magic_name__ : int =d_model
__magic_name__ : Any =d_ff
__magic_name__ : Dict =d_ext
__magic_name__ : Union[str, Any] =d_spout
__magic_name__ : List[Any] =num_switch_layers
__magic_name__ : int =num_ext_layers
__magic_name__ : Optional[int] =num_switch_layers + num_ext_layers
__magic_name__ : Union[str, Any] =num_heads
__magic_name__ : Any =num_experts
__magic_name__ : Optional[int] =expert_capacity
__magic_name__ : Union[str, Any] =dropout_rate
__magic_name__ : Any =layer_norm_epsilon
__magic_name__ : Union[str, Any] =router_bias
__magic_name__ : int =router_jitter_noise
__magic_name__ : str =router_dtype
__magic_name__ : Optional[int] =router_ignore_padding_tokens
__magic_name__ : str =output_hidden_states
__magic_name__ : Dict =output_attentions
__magic_name__ : Dict =initializer_factor
__magic_name__ : List[str] =output_router_logits
__magic_name__ : int =use_cache
super().__init__(
separator_token_id=__snake_case , pad_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case , )
| 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__magic_name__ : Dict =F"{src_lang}-{tgt_lang}"
print(F"Converting {dataset}-{pair}" )
__magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__magic_name__ : Optional[int] =F"{dataset}-{pair}"
__magic_name__ : int =Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
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
__magic_name__ : Dict ="""val""" if split == """validation""" else split
__magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" )
__magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" )
__magic_name__ : Optional[Any] =src_path.open("""w+""" )
__magic_name__ : List[Any] =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] ):
__magic_name__ : str =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)
| 21 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __A :
def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ):
'''simple docstring'''
__magic_name__ : Optional[int] =parent
__magic_name__ : List[Any] =batch_size
__magic_name__ : List[str] =is_training
__magic_name__ : List[str] =use_auxiliary_loss
__magic_name__ : Union[str, Any] =num_queries
__magic_name__ : str =num_channels
__magic_name__ : Union[str, Any] =min_size
__magic_name__ : Union[str, Any] =max_size
__magic_name__ : Optional[int] =num_labels
__magic_name__ : Tuple =hidden_dim
__magic_name__ : Any =hidden_dim
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
__magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
__magic_name__ : List[str] =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
__magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
__magic_name__ : str =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__magic_name__ : str =self.num_queries
__magic_name__ : Dict =self.num_labels
__magic_name__ : int =[1, 1, 1, 1]
__magic_name__ : List[str] =self.num_channels
__magic_name__ : str =64
__magic_name__ : List[str] =1_28
__magic_name__ : Optional[Any] =self.hidden_dim
__magic_name__ : Tuple =self.hidden_dim
__magic_name__ : Optional[int] =self.hidden_dim
return config
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =output.encoder_hidden_states
__magic_name__ : List[str] =output.pixel_decoder_hidden_states
__magic_name__ : int =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_layers )
def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ):
'''simple docstring'''
with torch.no_grad():
__magic_name__ : List[str] =MaskaFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__snake_case , __snake_case )
def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case :List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : List[str] =model(__snake_case )
comm_check_on_output(__snake_case )
__magic_name__ : Any =model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Any =MaskaFormerModelTester(self )
__magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def A__ ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def A__ ( self :int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple =model_class(__snake_case )
__magic_name__ : Optional[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple =[*signature.parameters.keys()]
__magic_name__ : Optional[Any] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =(self.model_tester.min_size,) * 2
__magic_name__ : Union[str, Any] ={
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
__magic_name__ : Optional[Any] =self.model_tester.get_config()
__magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case )
__magic_name__ : Any =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case )
__magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def A__ ( self :int ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ : List[Any] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Dict =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Tuple =True
__magic_name__ : Optional[int] =True
__magic_name__ : int =model_class(__snake_case ).to(__snake_case )
model.train()
__magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
__magic_name__ : Optional[int] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__magic_name__ : Optional[int] =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase_ : Dict = 1e-4
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case )
__magic_name__ : int =self.default_image_processor
__magic_name__ : List[Any] =prepare_img()
__magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Dict =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : List[str] =model(**__snake_case )
__magic_name__ : Any =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Dict =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Any =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Optional[int] =self.default_image_processor
__magic_name__ : Tuple =prepare_img()
__magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
# masks_queries_logits
__magic_name__ : List[Any] =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__magic_name__ : List[Any] =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
__magic_name__ : Any =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__magic_name__ : int =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Any =self.default_image_processor
__magic_name__ : Union[str, Any] =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
__magic_name__ : str =inputs["""pixel_values"""].to(__snake_case )
__magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]]
__magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__magic_name__ : Dict =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 21 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __A ( UpperCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = None
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=0.9_9_9 , lowerCamelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCamelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCamelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
__magic_name__ : List[str] =[]
for i in range(lowerCamelCase ):
__magic_name__ : int =i / num_diffusion_timesteps
__magic_name__ : Optional[int] =(i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCamelCase ) / alpha_bar_fn(lowerCamelCase ) , lowerCamelCase ) )
return torch.tensor(lowerCamelCase , dtype=torch.floataa )
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
@register_to_config
def __init__( self :Union[str, Any] , __snake_case :int = 10_00 , __snake_case :str = "fixed_small_log" , __snake_case :bool = True , __snake_case :Optional[float] = 1.0 , __snake_case :str = "epsilon" , __snake_case :str = "squaredcos_cap_v2" , ):
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
__magic_name__ : Optional[int] =betas_for_alpha_bar(__snake_case )
__magic_name__ : Dict =1.0 - self.betas
__magic_name__ : Tuple =torch.cumprod(self.alphas , dim=0 )
__magic_name__ : List[str] =torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__magic_name__ : Optional[int] =1.0
# setable values
__magic_name__ : str =None
__magic_name__ : List[str] =torch.from_numpy(np.arange(0 , __snake_case )[::-1].copy() )
__magic_name__ : Dict =variance_type
def A__ ( self :List[str] , __snake_case :torch.FloatTensor , __snake_case :Optional[int] = None ):
'''simple docstring'''
return sample
def A__ ( self :str , __snake_case :int , __snake_case :Union[str, torch.device] = None ):
'''simple docstring'''
__magic_name__ : Tuple =num_inference_steps
__magic_name__ : List[str] =(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__magic_name__ : Dict =(np.arange(0 , __snake_case ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__magic_name__ : Union[str, Any] =torch.from_numpy(__snake_case ).to(__snake_case )
def A__ ( self :List[Any] , __snake_case :str , __snake_case :Any=None , __snake_case :List[Any]=None , __snake_case :Union[str, Any]=None ):
'''simple docstring'''
if prev_timestep is None:
__magic_name__ : List[Any] =t - 1
__magic_name__ : Any =self.alphas_cumprod[t]
__magic_name__ : List[str] =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__magic_name__ : int =1 - alpha_prod_t
__magic_name__ : str =1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__magic_name__ : Union[str, Any] =self.betas[t]
else:
__magic_name__ : Optional[Any] =1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__magic_name__ : Optional[Any] =beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__magic_name__ : Dict =self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__magic_name__ : Optional[int] =torch.log(torch.clamp(__snake_case , min=1E-20 ) )
__magic_name__ : Dict =torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__magic_name__ : str =variance.log()
__magic_name__ : str =beta.log()
__magic_name__ : List[str] =(predicted_variance + 1) / 2
__magic_name__ : str =frac * max_log + (1 - frac) * min_log
return variance
def A__ ( self :List[str] , __snake_case :torch.FloatTensor , __snake_case :int , __snake_case :torch.FloatTensor , __snake_case :Optional[int] = None , __snake_case :Dict=None , __snake_case :bool = True , ):
'''simple docstring'''
__magic_name__ : List[str] =timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__magic_name__ , __magic_name__ : int =torch.split(__snake_case , sample.shape[1] , dim=1 )
else:
__magic_name__ : str =None
# 1. compute alphas, betas
if prev_timestep is None:
__magic_name__ : Optional[Any] =t - 1
__magic_name__ : Union[str, Any] =self.alphas_cumprod[t]
__magic_name__ : Optional[int] =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__magic_name__ : Dict =1 - alpha_prod_t
__magic_name__ : Optional[int] =1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__magic_name__ : Optional[Any] =self.betas[t]
__magic_name__ : Any =self.alphas[t]
else:
__magic_name__ : List[Any] =1 - alpha_prod_t / alpha_prod_t_prev
__magic_name__ : Tuple =1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__magic_name__ : Optional[Any] =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__magic_name__ : str =model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__magic_name__ : int =torch.clamp(
__snake_case , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__magic_name__ : Optional[Any] =(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__magic_name__ : Dict =alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__magic_name__ : Any =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__magic_name__ : Any =0
if t > 0:
__magic_name__ : str =randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=__snake_case , device=model_output.device )
__magic_name__ : Optional[Any] =self._get_variance(
__snake_case , predicted_variance=__snake_case , prev_timestep=__snake_case , )
if self.variance_type == "fixed_small_log":
__magic_name__ : List[str] =variance
elif self.variance_type == "learned_range":
__magic_name__ : int =(0.5 * variance).exp()
else:
raise ValueError(
f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
""" for the UnCLIPScheduler.""" )
__magic_name__ : Union[str, Any] =variance * variance_noise
__magic_name__ : str =pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=__snake_case , pred_original_sample=__snake_case )
def A__ ( self :List[str] , __snake_case :torch.FloatTensor , __snake_case :torch.FloatTensor , __snake_case :torch.IntTensor , ):
'''simple docstring'''
__magic_name__ : str =self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__magic_name__ : Union[str, Any] =timesteps.to(original_samples.device )
__magic_name__ : List[Any] =alphas_cumprod[timesteps] ** 0.5
__magic_name__ : Dict =sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__magic_name__ : Union[str, Any] =sqrt_alpha_prod.unsqueeze(-1 )
__magic_name__ : List[Any] =(1 - alphas_cumprod[timesteps]) ** 0.5
__magic_name__ : str =sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__magic_name__ : Union[str, Any] =sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__magic_name__ : List[str] =sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 21 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _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 (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __A :
def __init__( self :List[str] , __snake_case :List[Any] , __snake_case :Any=1_00 , __snake_case :List[Any]=13 , __snake_case :List[str]=30 , __snake_case :List[Any]=2 , __snake_case :str=3 , __snake_case :Optional[int]=True , __snake_case :List[str]=True , __snake_case :Tuple=32 , __snake_case :Optional[Any]=4 , __snake_case :str=4 , __snake_case :Optional[int]=37 , __snake_case :Any="gelu" , __snake_case :Any=0.1 , __snake_case :int=0.1 , __snake_case :Optional[Any]=10 , __snake_case :Dict=0.02 , __snake_case :List[Any]=3 , __snake_case :Union[str, Any]=None , __snake_case :Any=[0, 1, 2, 3] , ):
'''simple docstring'''
__magic_name__ : Any =parent
__magic_name__ : List[str] =1_00
__magic_name__ : str =batch_size
__magic_name__ : str =image_size
__magic_name__ : Any =patch_size
__magic_name__ : int =num_channels
__magic_name__ : Union[str, Any] =is_training
__magic_name__ : List[str] =use_labels
__magic_name__ : Union[str, Any] =hidden_size
__magic_name__ : Dict =num_hidden_layers
__magic_name__ : str =num_attention_heads
__magic_name__ : int =intermediate_size
__magic_name__ : Any =hidden_act
__magic_name__ : Optional[int] =hidden_dropout_prob
__magic_name__ : Any =attention_probs_dropout_prob
__magic_name__ : Tuple =type_sequence_label_size
__magic_name__ : Dict =initializer_range
__magic_name__ : int =scope
__magic_name__ : Union[str, Any] =out_indices
__magic_name__ : int =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__magic_name__ : Union[str, Any] =(image_size // patch_size) ** 2
__magic_name__ : Any =num_patches + 1
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : Optional[Any] =None
__magic_name__ : List[Any] =None
if self.use_labels:
__magic_name__ : Any =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ : int =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__magic_name__ : Optional[int] =self.get_config()
return config, pixel_values, labels, pixel_labels
def A__ ( self :List[str] ):
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__snake_case , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def A__ ( self :Optional[int] , __snake_case :Tuple , __snake_case :Optional[Any] , __snake_case :Dict , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple =BeitModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Optional[Any] =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self :Union[str, Any] , __snake_case :Any , __snake_case :List[Any] , __snake_case :Optional[int] , __snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : str =BeitForMaskedImageModeling(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Dict =model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def A__ ( self :str , __snake_case :List[Any] , __snake_case :Optional[int] , __snake_case :List[Any] , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Tuple =self.type_sequence_label_size
__magic_name__ : Optional[int] =BeitForImageClassification(__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : List[Any] =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__magic_name__ : Optional[Any] =1
__magic_name__ : List[Any] =BeitForImageClassification(__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : List[Any] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ : Dict =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A__ ( self :List[str] , __snake_case :Optional[Any] , __snake_case :List[str] , __snake_case :List[str] , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.num_labels
__magic_name__ : str =BeitForSemanticSegmentation(__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : str =model(__snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
__magic_name__ : Optional[int] =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : int =config_and_inputs
__magic_name__ : int ={"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": BeitModel,
"""image-classification""": BeitForImageClassification,
"""image-segmentation""": BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : List[Any] =BeitModelTester(self )
__magic_name__ : Optional[Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case , hidden_size=37 )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""BEiT does not use inputs_embeds""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :str ):
'''simple docstring'''
pass
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Dict =model_class(__snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__magic_name__ : Union[str, Any] =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__snake_case , nn.Linear ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Any =model_class(__snake_case )
__magic_name__ : Any =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : List[Any] =[*signature.parameters.keys()]
__magic_name__ : List[str] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__snake_case )
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : int =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__snake_case ), BeitForMaskedImageModeling]:
continue
__magic_name__ : Tuple =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : int =self._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
__magic_name__ : Any =model(**__snake_case ).loss
loss.backward()
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__magic_name__ : Optional[int] =False
__magic_name__ : str =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
__magic_name__ : List[Any] =model_class(__snake_case )
model.gradient_checkpointing_enable()
model.to(__snake_case )
model.train()
__magic_name__ : str =self._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
__magic_name__ : str =model(**__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Dict =self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : str =_config_zero_init(__snake_case )
for model_class in self.all_model_classes:
__magic_name__ : str =model_class(config=__snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@slow
def A__ ( self :List[str] ):
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Dict =BeitModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def lowerCAmelCase_ ( ):
__magic_name__ : List[str] =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :Any ):
'''simple docstring'''
return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None
@slow
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" ).to(__snake_case )
__magic_name__ : List[Any] =self.default_image_processor
__magic_name__ : Union[str, Any] =prepare_img()
__magic_name__ : Dict =image_processor(images=__snake_case , return_tensors="""pt""" ).pixel_values.to(__snake_case )
# prepare bool_masked_pos
__magic_name__ : Any =torch.ones((1, 1_96) , dtype=torch.bool ).to(__snake_case )
# forward pass
with torch.no_grad():
__magic_name__ : str =model(pixel_values=__snake_case , bool_masked_pos=__snake_case )
__magic_name__ : Any =outputs.logits
# verify the logits
__magic_name__ : Dict =torch.Size((1, 1_96, 81_92) )
self.assertEqual(logits.shape , __snake_case )
__magic_name__ : int =torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __snake_case , atol=1E-2 ) )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Optional[int] =BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" ).to(__snake_case )
__magic_name__ : str =self.default_image_processor
__magic_name__ : Optional[Any] =prepare_img()
__magic_name__ : Tuple =image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
__magic_name__ : Optional[int] =outputs.logits
# verify the logits
__magic_name__ : Any =torch.Size((1, 10_00) )
self.assertEqual(logits.shape , __snake_case )
__magic_name__ : List[Any] =torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__snake_case )
self.assertTrue(torch.allclose(logits[0, :3] , __snake_case , atol=1E-4 ) )
__magic_name__ : str =2_81
self.assertEqual(logits.argmax(-1 ).item() , __snake_case )
@slow
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : Any =BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" ).to(
__snake_case )
__magic_name__ : str =self.default_image_processor
__magic_name__ : str =prepare_img()
__magic_name__ : Any =image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
__magic_name__ : int =model(**__snake_case )
__magic_name__ : str =outputs.logits
# verify the logits
__magic_name__ : int =torch.Size((1, 2_18_41) )
self.assertEqual(logits.shape , __snake_case )
__magic_name__ : str =torch.tensor([1.6881, -0.2787, 0.5901] ).to(__snake_case )
self.assertTrue(torch.allclose(logits[0, :3] , __snake_case , atol=1E-4 ) )
__magic_name__ : str =23_96
self.assertEqual(logits.argmax(-1 ).item() , __snake_case )
@slow
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
__magic_name__ : List[Any] =model.to(__snake_case )
__magic_name__ : Dict =BeitImageProcessor(do_resize=__snake_case , size=6_40 , do_center_crop=__snake_case )
__magic_name__ : Optional[int] =load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
__magic_name__ : Optional[int] =Image.open(ds[0]["""file"""] )
__magic_name__ : Dict =image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
__magic_name__ : Optional[Any] =model(**__snake_case )
__magic_name__ : Tuple =outputs.logits
# verify the logits
__magic_name__ : Union[str, Any] =torch.Size((1, 1_50, 1_60, 1_60) )
self.assertEqual(logits.shape , __snake_case )
__magic_name__ : Dict =version.parse(PIL.__version__ ) < version.parse("""9.0.0""" )
if is_pillow_less_than_a:
__magic_name__ : Optional[int] =torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__snake_case , )
else:
__magic_name__ : Tuple =torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__snake_case , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __snake_case , atol=1E-4 ) )
@slow
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
__magic_name__ : Union[str, Any] =model.to(__snake_case )
__magic_name__ : int =BeitImageProcessor(do_resize=__snake_case , size=6_40 , do_center_crop=__snake_case )
__magic_name__ : Optional[int] =load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
__magic_name__ : Dict =Image.open(ds[0]["""file"""] )
__magic_name__ : int =image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
__magic_name__ : Any =model(**__snake_case )
__magic_name__ : List[str] =outputs.logits.detach().cpu()
__magic_name__ : Optional[int] =image_processor.post_process_semantic_segmentation(outputs=__snake_case , target_sizes=[(5_00, 3_00)] )
__magic_name__ : Optional[Any] =torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape , __snake_case )
__magic_name__ : List[str] =image_processor.post_process_semantic_segmentation(outputs=__snake_case )
__magic_name__ : Union[str, Any] =torch.Size((1_60, 1_60) )
self.assertEqual(segmentation[0].shape , __snake_case )
| 21 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : List[Any] =max_length
__magic_name__ : Dict =vocab
__magic_name__ : int =merges
__magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__magic_name__ : str =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
return cls(**__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case )
__magic_name__ : Tuple =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__magic_name__ : Tuple =max_length if max_length is not None else self.max_length
if max_length is not None:
__magic_name__ , __magic_name__ : Tuple =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 21 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __A :
def __init__( self :Any , __snake_case :Optional[Any] , __snake_case :Optional[int] , __snake_case :bool = True , __snake_case :bool = False ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =scheduler
__magic_name__ : Optional[Any] =optimizers if isinstance(__snake_case , (list, tuple) ) else [optimizers]
__magic_name__ : Any =split_batches
__magic_name__ : List[str] =step_with_optimizer
__magic_name__ : Dict =GradientState()
def A__ ( self :List[str] , *__snake_case :Optional[Any] , **__snake_case :Any ):
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*__snake_case , **__snake_case )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*__snake_case , **__snake_case )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__magic_name__ : str =AcceleratorState().num_processes
for _ in range(__snake_case ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , """total_steps""" ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*__snake_case , **__snake_case )
else:
self.scheduler.step(*__snake_case , **__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return self.scheduler.get_last_lr()
def A__ ( self :Tuple ):
'''simple docstring'''
return self.scheduler.state_dict()
def A__ ( self :Union[str, Any] , __snake_case :Dict ):
'''simple docstring'''
self.scheduler.load_state_dict(__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
return self.scheduler.get_lr()
def A__ ( self :Any , *__snake_case :int , **__snake_case :Union[str, Any] ):
'''simple docstring'''
return self.scheduler.print_lr(*__snake_case , **__snake_case )
| 21 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( lowerCamelCase = 8 ):
__magic_name__ : Optional[Any] =ascii_letters + digits + punctuation
return "".join(secrets.choice(lowerCamelCase ) for _ in range(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(lowerCamelCase )
__magic_name__ : Union[str, Any] =i // 3
__magic_name__ : List[Any] =i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
__magic_name__ : Union[str, Any] =(
chars_incl
+ random(lowerCamelCase , quotient + remainder )
+ random(lowerCamelCase , lowerCamelCase )
+ random(lowerCamelCase , lowerCamelCase )
)
__magic_name__ : Optional[int] =list(lowerCamelCase )
shuffle(lowerCamelCase )
return "".join(lowerCamelCase )
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return "".join(secrets.choice(lowerCamelCase ) for _ in range(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
pass # Put your code here...
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
pass # Put your code here...
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
pass # Put your code here...
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 8 ):
if len(lowerCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
__magic_name__ : int =any(char in ascii_uppercase for char in password )
__magic_name__ : Optional[int] =any(char in ascii_lowercase for char in password )
__magic_name__ : Any =any(char in digits for char in password )
__magic_name__ : List[Any] =any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase_ ( ):
__magic_name__ : str =int(input("""Please indicate the max length of your password: """ ).strip() )
__magic_name__ : int =input(
"""Please indicate the characters that must be in your password: """ ).strip()
print("""Password generated:""" , password_generator(lowerCamelCase ) )
print(
"""Alternative Password generated:""" , alternative_password_generator(lowerCamelCase , lowerCamelCase ) , )
print("""[If you are thinking of using this passsword, You better save it.]""" )
if __name__ == "__main__":
main()
| 21 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ):
__magic_name__ : Any =nums[i]
__magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip())
UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 21 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
UpperCAmelCase_ : Dict = False
class __A ( unittest.TestCase ):
def A__ ( self :str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A__ ( self :Optional[int] ):
'''simple docstring'''
return 12
@property
def A__ ( self :Dict ):
'''simple docstring'''
return 12
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return 32
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : str =VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : str =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[int] =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__snake_case )
@property
def A__ ( self :int ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[int] =12
__magic_name__ : str =12
__magic_name__ : Optional[int] ={
"""attention_bias""": True,
"""cross_attention_dim""": 32,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 32,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
__magic_name__ : int =TransformeraDModel(**__snake_case )
return model
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Any ="""cpu"""
__magic_name__ : List[str] =self.dummy_vqvae
__magic_name__ : List[Any] =self.dummy_text_encoder
__magic_name__ : List[Any] =self.dummy_tokenizer
__magic_name__ : List[Any] =self.dummy_transformer
__magic_name__ : Tuple =VQDiffusionScheduler(self.num_embed )
__magic_name__ : Optional[int] =LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case )
__magic_name__ : List[str] =VQDiffusionPipeline(
vqvae=__snake_case , text_encoder=__snake_case , tokenizer=__snake_case , transformer=__snake_case , scheduler=__snake_case , learned_classifier_free_sampling_embeddings=__snake_case , )
__magic_name__ : int =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Any ="""teddy bear playing in the pool"""
__magic_name__ : List[str] =torch.Generator(device=__snake_case ).manual_seed(0 )
__magic_name__ : Optional[Any] =pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type="""np""" )
__magic_name__ : Any =output.images
__magic_name__ : List[str] =torch.Generator(device=__snake_case ).manual_seed(0 )
__magic_name__ : Dict =pipe(
[prompt] , generator=__snake_case , output_type="""np""" , return_dict=__snake_case , num_inference_steps=2 )[0]
__magic_name__ : List[str] =image[0, -3:, -3:, -1]
__magic_name__ : int =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__magic_name__ : Optional[int] =np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""cpu"""
__magic_name__ : Union[str, Any] =self.dummy_vqvae
__magic_name__ : str =self.dummy_text_encoder
__magic_name__ : Dict =self.dummy_tokenizer
__magic_name__ : Optional[Any] =self.dummy_transformer
__magic_name__ : Tuple =VQDiffusionScheduler(self.num_embed )
__magic_name__ : int =LearnedClassifierFreeSamplingEmbeddings(
learnable=__snake_case , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__magic_name__ : int =VQDiffusionPipeline(
vqvae=__snake_case , text_encoder=__snake_case , tokenizer=__snake_case , transformer=__snake_case , scheduler=__snake_case , learned_classifier_free_sampling_embeddings=__snake_case , )
__magic_name__ : Dict =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Optional[Any] ="""teddy bear playing in the pool"""
__magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(0 )
__magic_name__ : Tuple =pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type="""np""" )
__magic_name__ : List[str] =output.images
__magic_name__ : Optional[Any] =torch.Generator(device=__snake_case ).manual_seed(0 )
__magic_name__ : str =pipe(
[prompt] , generator=__snake_case , output_type="""np""" , return_dict=__snake_case , num_inference_steps=2 )[0]
__magic_name__ : Any =image[0, -3:, -3:, -1]
__magic_name__ : Tuple =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__magic_name__ : Union[str, Any] =np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
__magic_name__ : int =VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
__magic_name__ : Tuple =pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__magic_name__ : int =torch.Generator(device=__snake_case ).manual_seed(0 )
__magic_name__ : Optional[Any] =pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=__snake_case , output_type="""np""" , )
__magic_name__ : Dict =output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def __call__( self :Union[str, Any] ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None
__magic_name__ : Optional[int] =len(self.languages ) if self.languages else None
def __call__( self :List[str] ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[int] =set(self.languages )
if self.languages and set(__snake_case ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__magic_name__ : Any =[]
for lang, text in translation_dict.items():
if isinstance(__snake_case , __snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) )
return {"language": languages, "translation": translations}
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 21 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCAmelCase_ : Union[str, Any] = logging.getLogger(__name__)
UpperCAmelCase_ : Optional[Any] = 50 # max width of layer names
UpperCAmelCase_ : Optional[int] = 70 # max width of quantizer names
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Dict =parser.add_argument_group("""quant_trainer arguments""" )
group.add_argument("""--wprec""" , type=lowerCamelCase , default=8 , help="""weight precision""" )
group.add_argument("""--aprec""" , type=lowerCamelCase , default=8 , help="""activation precision""" )
group.add_argument("""--quant-per-tensor""" , action="""store_true""" , help="""per tensor weight scaling""" )
group.add_argument("""--quant-disable""" , action="""store_true""" , help="""disable all quantizers""" )
group.add_argument("""--quant-disable-embeddings""" , action="""store_true""" , help="""disable all embeddings quantizers""" )
group.add_argument("""--quant-disable-keyword""" , type=lowerCamelCase , nargs="""+""" , help="""disable quantizers by keyword""" )
group.add_argument("""--quant-disable-layer-module""" , type=lowerCamelCase , help="""disable quantizers by keyword under layer.""" )
group.add_argument("""--quant-enable-layer-module""" , type=lowerCamelCase , help="""enable quantizers by keyword under layer""" )
group.add_argument("""--calibrator""" , default="""max""" , help="""which quantization range calibrator to use""" )
group.add_argument("""--percentile""" , default=lowerCamelCase , type=lowerCamelCase , help="""percentile for PercentileCalibrator""" )
group.add_argument("""--fuse-qkv""" , action="""store_true""" , help="""use the same scale factor for qkv""" )
group.add_argument("""--clip-gelu""" , metavar="""N""" , type=lowerCamelCase , help="""clip gelu output maximum value to N""" )
group.add_argument(
"""--recalibrate-weights""" , action="""store_true""" , help=(
"""recalibrate weight amaxes by taking the max of the weights."""
""" amaxes will be computed with the current quantization granularity (axis)."""
) , )
def lowerCAmelCase_ ( lowerCamelCase ):
if args.calibrator == "max":
__magic_name__ : str ="""max"""
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError("""Specify --percentile when using percentile calibrator""" )
__magic_name__ : str ="""histogram"""
elif args.calibrator == "mse":
__magic_name__ : Dict ="""histogram"""
else:
raise ValueError(F"Invalid calibrator {args.calibrator}" )
__magic_name__ : Optional[Any] =QuantDescriptor(num_bits=args.aprec , calib_method=lowerCamelCase )
__magic_name__ : int =QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(lowerCamelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=False , lowerCamelCase=False ):
logger.info("""Configuring Model for Quantization""" )
logger.info(F"using quantization package {pytorch_quantization.__file__}" )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(lowerCamelCase , ["""embeddings"""] , which="""weight""" , _disabled=lowerCamelCase )
if args.quant_disable:
set_quantizer_by_name(lowerCamelCase , [""""""] , _disabled=lowerCamelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(lowerCamelCase , args.quant_disable_keyword , _disabled=lowerCamelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(lowerCamelCase , [R"""layer.\d+.""" + args.quant_disable_layer_module] , _disabled=lowerCamelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(lowerCamelCase , [R"""layer.\d+.""" + args.quant_enable_layer_module] , _disabled=lowerCamelCase )
if args.recalibrate_weights:
recalibrate_weights(lowerCamelCase )
if args.fuse_qkv:
fuse_qkv(lowerCamelCase , lowerCamelCase )
if args.clip_gelu:
clip_gelu(lowerCamelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
logger.info("""Enabling Calibration""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F"{name:80}: {module}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
logger.info("""Loading calibrated amax""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax("""percentile""" , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
def fusea(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for mod in [qq, qk, qv]:
if not hasattr(lowerCamelCase , """_amax""" ):
print(""" WARNING: NO AMAX BUFFER""" )
return
__magic_name__ : Optional[int] =qq._amax.detach().item()
__magic_name__ : List[str] =qk._amax.detach().item()
__magic_name__ : List[Any] =qv._amax.detach().item()
__magic_name__ : Optional[int] =max(lowerCamelCase , lowerCamelCase , lowerCamelCase )
qq._amax.fill_(lowerCamelCase )
qk._amax.fill_(lowerCamelCase )
qv._amax.fill_(lowerCamelCase )
logger.info(F" q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}" )
for name, mod in model.named_modules():
if name.endswith(""".attention.self""" ):
logger.info(F"FUSE_QKV: {name:{name_width}}" )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
for name, mod in model.named_modules():
if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
__magic_name__ : int =mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=lowerCamelCase )
__magic_name__ : Optional[Any] =mod._input_quantizer._amax.data.detach().item()
logger.info(F"CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}" )
def lowerCAmelCase_ ( lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(lowerCamelCase , """_weight_quantizer""" ) and mod._weight_quantizer.axis is not None:
__magic_name__ : int =mod.weight.shape[0]
__magic_name__ : int =mod._weight_quantizer._amax.detach()
__magic_name__ : Tuple =torch.ones(lowerCamelCase , dtype=amax.dtype , device=amax.device ) * amax
print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}" )
def lowerCAmelCase_ ( lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(lowerCamelCase , """_weight_quantizer""" ):
if not hasattr(mod.weight_quantizer , """_amax""" ):
print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER""" )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
__magic_name__ : List[str] =set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
__magic_name__ : List[str] =set(range(len(mod.weight.size() ) ) ) - axis_set
__magic_name__ : List[str] =pytorch_quantization.utils.reduce_amax(mod.weight , axis=lowerCamelCase , keepdims=lowerCamelCase ).detach()
logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}" )
__magic_name__ : Union[str, Any] =amax
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=25 , lowerCamelCase=180 , lowerCamelCase=None ):
if ignore is None:
__magic_name__ : int =[]
elif not isinstance(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =[ignore]
__magic_name__ : Tuple =0
for name, mod in model.named_modules():
if not hasattr(lowerCamelCase , """weight""" ):
continue
__magic_name__ : Union[str, Any] =max(lowerCamelCase , len(lowerCamelCase ) )
for name, mod in model.named_modules():
__magic_name__ : int =getattr(lowerCamelCase , """_input_quantizer""" , lowerCamelCase )
__magic_name__ : Tuple =getattr(lowerCamelCase , """_weight_quantizer""" , lowerCamelCase )
if not hasattr(lowerCamelCase , """weight""" ):
continue
if type(lowerCamelCase ) in ignore:
continue
if [True for s in ignore if type(lowerCamelCase ) is str and s in name]:
continue
__magic_name__ : List[str] =F"Act:{input_q.extra_repr()}"
__magic_name__ : Dict =F"Wgt:{weight_q.extra_repr()}"
__magic_name__ : Optional[Any] =F"{name:{name_width}} {act_str} {wgt_str}"
if len(lowerCamelCase ) <= line_width:
logger.info(lowerCamelCase )
else:
logger.info(F"{name:{name_width}} {act_str}" )
logger.info(F"{' ':{name_width}} {wgt_str}" )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Union[str, Any] =0
for name, mod in model.named_modules():
if isinstance(lowerCamelCase , pytorch_quantization.nn.TensorQuantizer ):
print(F"{name:80} {mod}" )
count += 1
print(F"{count} TensorQuantizers found in model" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[str] =getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if quantizer_mod is not None:
assert hasattr(lowerCamelCase , lowerCamelCase )
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
logger.warning(F"{name} has no {quantizer}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase="both" , **lowerCamelCase ):
__magic_name__ : str =F"Warning: changing {which} quantizers of {name:{qname_width}}"
for k, v in kwargs.items():
s += F" {k}={v}"
if which in ["input", "both"]:
set_quantizer(lowerCamelCase , lowerCamelCase , """_input_quantizer""" , lowerCamelCase , lowerCamelCase )
if which in ["weight", "both"]:
set_quantizer(lowerCamelCase , lowerCamelCase , """_weight_quantizer""" , lowerCamelCase , lowerCamelCase )
logger.info(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(lowerCamelCase , """_input_quantizer""" ) or hasattr(lowerCamelCase , """_weight_quantizer""" ):
for n in names:
if re.search(lowerCamelCase , lowerCamelCase ):
set_quantizers(lowerCamelCase , lowerCamelCase , **lowerCamelCase )
elif name.endswith("""_quantizer""" ):
for n in names:
if re.search(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =F"Warning: changing {name:{name_width}}"
for k, v in kwargs.items():
s += F" {k}={v}"
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
logger.info(lowerCamelCase )
| 21 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = """encoder-decoder"""
UpperCamelCase = True
def __init__( self :str , **__snake_case :Union[str, Any] ):
'''simple docstring'''
super().__init__(**__snake_case )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
__magic_name__ : List[Any] =kwargs.pop("""encoder""" )
__magic_name__ : int =encoder_config.pop("""model_type""" )
__magic_name__ : Tuple =kwargs.pop("""decoder""" )
__magic_name__ : Dict =decoder_config.pop("""model_type""" )
from ..auto.configuration_auto import AutoConfig
__magic_name__ : Dict =AutoConfig.for_model(__snake_case , **__snake_case )
__magic_name__ : int =AutoConfig.for_model(__snake_case , **__snake_case )
__magic_name__ : Tuple =True
@classmethod
def A__ ( cls :Tuple , __snake_case :PretrainedConfig , __snake_case :PretrainedConfig , **__snake_case :List[Any] ):
'''simple docstring'''
logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
__magic_name__ : List[Any] =True
__magic_name__ : Union[str, Any] =True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =copy.deepcopy(self.__dict__ )
__magic_name__ : str =self.encoder.to_dict()
__magic_name__ : str =self.decoder.to_dict()
__magic_name__ : int =self.__class__.model_type
return output
| 21 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
return config
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ):
if conf_path is None:
__magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml"""
__magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase )
__magic_name__ : Tuple =VQModel(**config.model.params )
if ckpt_path is None:
__magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt"""
__magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase )
if ".ckpt" in ckpt_path:
__magic_name__ : Any =sd["""state_dict"""]
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
model.to(lowerCamelCase )
del sd
return model
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__magic_name__ : List[Any] =model.decode(lowerCamelCase )
return xrec
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 )
if reload:
__magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase )
importlib.reload(lowerCamelCase )
return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls )
def lowerCAmelCase_ ( lowerCamelCase ):
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ):
__magic_name__ : str =instantiate_from_config(lowerCamelCase )
if sd is not None:
model.load_state_dict(lowerCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load the specified checkpoint
if ckpt:
__magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Any =pl_sd["""global_step"""]
print(F"loaded model from global step {global_step}." )
else:
__magic_name__ : List[Any] ={"""state_dict""": None}
__magic_name__ : Optional[Any] =None
__magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""]
return model, global_step
| 21 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( ):
__magic_name__ : List[str] ={
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
"""path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
"""content""": ["""a """ * 20, """a """ * 30, """b """ * 7],
}
__magic_name__ : List[Any] =Dataset.from_dict(lowerCamelCase )
return dataset
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : List[Any] =get_dataset()
__magic_name__ : List[str] =make_duplicate_clusters(__snake_case , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =get_dataset()
__magic_name__ , __magic_name__ : List[str] =deduplicate_dataset(__snake_case )
self.assertEqual(len(__snake_case ) , 2 )
print(__snake_case )
self.assertEqual(duplicate_clusters[0][0]["""copies"""] , 2 )
self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""] , __snake_case )
| 21 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_script.main )
def A__ ( self :Dict ):
'''simple docstring'''
debug_launcher(test_ops.main )
| 21 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmelCase_ ( lowerCamelCase ):
if not sequence:
return []
if len(lowerCamelCase ) == 1:
return list(lowerCamelCase )
__magic_name__ : int =0
__magic_name__ : str =len(lowerCamelCase ) - 1
__magic_name__ : Optional[Any] =0
while mid <= high:
if sequence[mid] == colors[0]:
__magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
__magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid]
high -= 1
else:
__magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values"
raise ValueError(lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip()
UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
print(F"""{dutch_national_flag_sort(unsorted)}""")
| 21 | 1 |
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
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
UpperCAmelCase_ : Any = {
"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"
},
}
UpperCAmelCase_ : Optional[Any] = {"facebook/blenderbot-3B": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_ ( ):
__magic_name__ : Tuple =(
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
__magic_name__ : Dict =bs[:]
__magic_name__ : List[Any] =0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCamelCase )
cs.append(2**8 + n )
n += 1
__magic_name__ : List[str] =[chr(lowerCamelCase ) for n in cs]
return dict(zip(lowerCamelCase , lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple =set()
__magic_name__ : Dict =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : List[Any] =char
return pairs
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self :Any , __snake_case :Tuple , __snake_case :str , __snake_case :Union[str, Any]="replace" , __snake_case :Union[str, Any]="<s>" , __snake_case :Optional[int]="</s>" , __snake_case :List[str]="</s>" , __snake_case :Optional[int]="<s>" , __snake_case :int="<unk>" , __snake_case :Optional[Any]="<pad>" , __snake_case :List[Any]="<mask>" , __snake_case :Union[str, Any]=False , **__snake_case :int , ):
'''simple docstring'''
__magic_name__ : List[str] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else bos_token
__magic_name__ : Dict =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else eos_token
__magic_name__ : Tuple =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else sep_token
__magic_name__ : List[str] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else cls_token
__magic_name__ : Tuple =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else unk_token
__magic_name__ : List[str] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__magic_name__ : Optional[int] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token
super().__init__(
errors=__snake_case , bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , add_prefix_space=__snake_case , **__snake_case , )
with open(__snake_case , encoding="""utf-8""" ) as vocab_handle:
__magic_name__ : Tuple =json.load(__snake_case )
__magic_name__ : List[Any] ={v: k for k, v in self.encoder.items()}
__magic_name__ : Optional[Any] =errors # how to handle errors in decoding
__magic_name__ : List[Any] =bytes_to_unicode()
__magic_name__ : Any ={v: k for k, v in self.byte_encoder.items()}
with open(__snake_case , encoding="""utf-8""" ) as merges_handle:
__magic_name__ : List[str] =merges_handle.read().split("""\n""" )[1:-1]
__magic_name__ : Optional[int] =[tuple(merge.split() ) for merge in bpe_merges]
__magic_name__ : Union[str, Any] =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__magic_name__ : List[str] ={}
__magic_name__ : str =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__magic_name__ : Dict =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 A__ ( self :Dict ):
'''simple docstring'''
return len(self.encoder )
def A__ ( self :List[Any] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def A__ ( self :Tuple , __snake_case :Dict ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__magic_name__ : List[Any] =tuple(__snake_case )
__magic_name__ : Union[str, Any] =get_pairs(__snake_case )
if not pairs:
return token
while True:
__magic_name__ : str =min(__snake_case , key=lambda __snake_case : self.bpe_ranks.get(__snake_case , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : Dict =bigram
__magic_name__ : int =[]
__magic_name__ : int =0
while i < len(__snake_case ):
try:
__magic_name__ : Any =word.index(__snake_case , __snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : Union[str, Any] =j
if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : List[Any] =tuple(__snake_case )
__magic_name__ : List[str] =new_word
if len(__snake_case ) == 1:
break
else:
__magic_name__ : Union[str, Any] =get_pairs(__snake_case )
__magic_name__ : int =""" """.join(__snake_case )
__magic_name__ : Optional[Any] =word
return word
def A__ ( self :Dict , __snake_case :str ):
'''simple docstring'''
__magic_name__ : List[str] =[]
for token in re.findall(self.pat , __snake_case ):
__magic_name__ : str ="""""".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(__snake_case ).split(""" """ ) )
return bpe_tokens
def A__ ( self :List[Any] , __snake_case :List[str] ):
'''simple docstring'''
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def A__ ( self :Any , __snake_case :str ):
'''simple docstring'''
return self.decoder.get(__snake_case )
def A__ ( self :Tuple , __snake_case :Any ):
'''simple docstring'''
__magic_name__ : int ="""""".join(__snake_case )
__magic_name__ : Any =bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def A__ ( self :Any , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(__snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__magic_name__ : Optional[Any] =os.path.join(
__snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__magic_name__ : List[Any] =os.path.join(
__snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__snake_case , ensure_ascii=__snake_case ) + """\n""" )
__magic_name__ : Union[str, Any] =0
with open(__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 __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!""" )
__magic_name__ : Tuple =token_index
writer.write(""" """.join(__snake_case ) + """\n""" )
index += 1
return vocab_file, merge_file
def A__ ( self :Any , __snake_case :List[int] , __snake_case :Optional[List[int]] = None , __snake_case :bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is None:
return [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1]
def A__ ( self :Dict , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : List[str] =[self.sep_token_id]
__magic_name__ : List[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A__ ( self :Any , __snake_case :Union[str, Any] , __snake_case :Union[str, Any]=False , **__snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple =kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__snake_case ) > 0 and not text[0].isspace()):
__magic_name__ : Dict =""" """ + text
return (text, kwargs)
def A__ ( self :Optional[int] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def A__ ( self :int , __snake_case :"Conversation" ):
'''simple docstring'''
__magic_name__ : int =[]
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(__snake_case )
__magic_name__ : Optional[Any] =""" """.join(__snake_case )
__magic_name__ : int =self.encode(__snake_case )
if len(__snake_case ) > self.model_max_length:
__magic_name__ : Union[str, Any] =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
| 21 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = 1
@register_to_config
def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ):
'''simple docstring'''
__magic_name__ : Dict =None
__magic_name__ : List[str] =None
__magic_name__ : str =None
def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case )
def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__magic_name__ : int =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__magic_name__ : str =std.flatten()
while len(std.shape ) < len(score.shape ):
__magic_name__ : List[str] =std.unsqueeze(-1 )
__magic_name__ : Union[str, Any] =-score / std
# compute
__magic_name__ : Tuple =-1.0 / len(self.timesteps )
__magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__magic_name__ : Dict =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__magic_name__ : Any =beta_t.unsqueeze(-1 )
__magic_name__ : Dict =-0.5 * beta_t * x
__magic_name__ : Optional[int] =torch.sqrt(__snake_case )
__magic_name__ : int =drift - diffusion**2 * score
__magic_name__ : List[str] =x + drift * dt
# add noise
__magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype )
__magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self :List[Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 21 | 1 |
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 __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = DDIMPipeline
UpperCamelCase = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
UpperCamelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
UpperCamelCase = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
UpperCamelCase = False
def A__ ( self :List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[Any] =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""") , )
__magic_name__ : str =DDIMScheduler()
__magic_name__ : str ={"""unet""": unet, """scheduler""": scheduler}
return components
def A__ ( self :Any , __snake_case :Union[str, Any] , __snake_case :Any=0 ):
'''simple docstring'''
if str(__snake_case ).startswith("""mps""" ):
__magic_name__ : List[str] =torch.manual_seed(__snake_case )
else:
__magic_name__ : List[str] =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__magic_name__ : str ={
"""batch_size""": 1,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Any ="""cpu"""
__magic_name__ : Tuple =self.get_dummy_components()
__magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : str =self.get_dummy_inputs(__snake_case )
__magic_name__ : int =pipe(**__snake_case ).images
__magic_name__ : Any =image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__magic_name__ : List[str] =np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
__magic_name__ : Tuple =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__snake_case , 1E-3 )
def A__ ( self :Tuple ):
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3E-3 )
def A__ ( self :str ):
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A__ ( self :List[str] ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Optional[Any] ="""google/ddpm-cifar10-32"""
__magic_name__ : Dict =UNetaDModel.from_pretrained(__snake_case )
__magic_name__ : Dict =DDIMScheduler()
__magic_name__ : Optional[int] =DDIMPipeline(unet=__snake_case , scheduler=__snake_case )
ddim.to(__snake_case )
ddim.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Optional[Any] =torch.manual_seed(0 )
__magic_name__ : int =ddim(generator=__snake_case , eta=0.0 , output_type="""numpy""" ).images
__magic_name__ : Tuple =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : List[Any] =np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] ="""google/ddpm-ema-bedroom-256"""
__magic_name__ : str =UNetaDModel.from_pretrained(__snake_case )
__magic_name__ : Any =DDIMScheduler.from_pretrained(__snake_case )
__magic_name__ : Any =DDIMPipeline(unet=__snake_case , scheduler=__snake_case )
ddpm.to(__snake_case )
ddpm.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Union[str, Any] =torch.manual_seed(0 )
__magic_name__ : List[str] =ddpm(generator=__snake_case , output_type="""numpy""" ).images
__magic_name__ : Dict =image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__magic_name__ : Union[str, Any] =np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 21 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 | 1 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
def __init__( self :List[Any] ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =nn.Linear(3 , 4 )
__magic_name__ : Union[str, Any] =nn.BatchNormad(4 )
__magic_name__ : List[str] =nn.Linear(4 , 5 )
def A__ ( self :Dict , __snake_case :Tuple ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) )
class __A ( UpperCamelCase__ ):
def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ):
'''simple docstring'''
return output + 1
class __A ( unittest.TestCase ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : Tuple =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
self.assertEqual(test_model._hf_hook , __snake_case )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : List[str] =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
add_hook_to_module(__snake_case , __snake_case , append=__snake_case )
self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
__magic_name__ : Any =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(x + 1 )
__magic_name__ : Optional[Any] =test_model(x + 2 )
__magic_name__ : int =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : int =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : str =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : List[str] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
assert torch.allclose(__snake_case , __snake_case , atol=1E-5 )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
__magic_name__ : Dict =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(__snake_case )
__magic_name__ : Dict =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Optional[int] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
assert torch.allclose(__snake_case , output + 2 , atol=1E-5 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =ModelForTest()
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Dict =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__magic_name__ : Any =True
__magic_name__ : Any =test_model(__snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[Any] =model(__snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) )
__magic_name__ : int =torch.randn(2 , 3 ).to(0 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : Union[str, Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
__magic_name__ : Tuple ={
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Tuple =torch.randn(2 , 3 )
__magic_name__ : int =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : str =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Optional[int] =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Any =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 21 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple =[]
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
F"stage{idx}.patch_embed.proj.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
F"stage{idx}.patch_embed.proj.bias",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
F"stage{idx}.patch_embed.norm.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
F"stage{idx}.patch_embed.norm.bias",
) )
return embed
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[Any] =[]
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
F"stage{idx}.blocks.{cnt}.attn.proj.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
F"stage{idx}.blocks.{cnt}.attn.proj.bias",
) )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias") )
return attention_weights
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =[]
token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""") )
return token
def lowerCAmelCase_ ( ):
__magic_name__ : Any =[]
head.append(("""layernorm.weight""", """norm.weight""") )
head.append(("""layernorm.bias""", """norm.bias""") )
head.append(("""classifier.weight""", """head.weight""") )
head.append(("""classifier.bias""", """head.bias""") )
return head
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Dict ="""imagenet-1k-id2label.json"""
__magic_name__ : Union[str, Any] =1000
__magic_name__ : int ="""huggingface/label-files"""
__magic_name__ : Optional[Any] =num_labels
__magic_name__ : str =json.load(open(cached_download(hf_hub_url(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
__magic_name__ : Optional[Any] ={int(lowerCamelCase ): v for k, v in idalabel.items()}
__magic_name__ : List[str] =idalabel
__magic_name__ : Optional[Any] ={v: k for k, v in idalabel.items()}
__magic_name__ : Dict =CvtConfig(num_labels=lowerCamelCase , idalabel=lowerCamelCase , labelaid=lowerCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "13":
__magic_name__ : Tuple =[1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "21":
__magic_name__ : Optional[Any] =[1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__magic_name__ : int =[2, 2, 20]
__magic_name__ : Tuple =[3, 12, 16]
__magic_name__ : Optional[Any] =[192, 768, 1024]
__magic_name__ : str =CvtForImageClassification(lowerCamelCase )
__magic_name__ : Dict =AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
__magic_name__ : str =image_size
__magic_name__ : List[str] =torch.load(lowerCamelCase , map_location=torch.device("""cpu""" ) )
__magic_name__ : int =OrderedDict()
__magic_name__ : Optional[Any] =[]
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__magic_name__ : str =list_of_state_dict + cls_token(lowerCamelCase )
__magic_name__ : List[str] =list_of_state_dict + embeddings(lowerCamelCase )
for cnt in range(config.depth[idx] ):
__magic_name__ : Optional[Any] =list_of_state_dict + attention(lowerCamelCase , lowerCamelCase )
__magic_name__ : List[Any] =list_of_state_dict + final()
for gg in list_of_state_dict:
print(lowerCamelCase )
for i in range(len(lowerCamelCase ) ):
__magic_name__ : Optional[int] =original_weights[list_of_state_dict[i][1]]
model.load_state_dict(lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"--cvt_model",
default="cvt-w24",
type=str,
help="Name of the cvt model you'd like to convert.",
)
parser.add_argument(
"--image_size",
default=384,
type=int,
help="Input Image Size",
)
parser.add_argument(
"--cvt_file_name",
default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
type=str,
help="Input Image Size",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = KandinskyInpaintPipeline
UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase = False
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def A__ ( self :Dict ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return 1_00
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def A__ ( self :str ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : str =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__magic_name__ : Tuple =MultilingualCLIP(__snake_case )
__magic_name__ : Optional[int] =text_encoder.eval()
return text_encoder
@property
def A__ ( self :Dict ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[Any] ={
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case )
return model
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A__ ( self :Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs )
return model
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[str] =self.dummy_text_encoder
__magic_name__ : Optional[Any] =self.dummy_tokenizer
__magic_name__ : Optional[Any] =self.dummy_unet
__magic_name__ : Tuple =self.dummy_movq
__magic_name__ : List[str] =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , )
__magic_name__ : str ={
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case )
# create init_image
__magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa )
__magic_name__ : Any =0
if str(__snake_case ).startswith("""mps""" ):
__magic_name__ : Dict =torch.manual_seed(__snake_case )
else:
__magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__magic_name__ : List[Any] ={
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple ="""cpu"""
__magic_name__ : List[Any] =self.get_dummy_components()
__magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case )
__magic_name__ : Tuple =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) )
__magic_name__ : List[Any] =output.images
__magic_name__ : Any =pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
__magic_name__ : int =image[0, -3:, -3:, -1]
__magic_name__ : str =image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Optional[Any] =np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def A__ ( self :Dict ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[str] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__magic_name__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa )
__magic_name__ : Any =0
__magic_name__ : int ="""a hat"""
__magic_name__ : int =KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
__magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__magic_name__ : int =pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 )
__magic_name__ , __magic_name__ : Dict =pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__magic_name__ : Optional[Any] =pipeline(
__snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , )
__magic_name__ : Optional[int] =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 21 | 1 |
UpperCAmelCase_ : Optional[Any] = "\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"
UpperCAmelCase_ : str = [{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase_ : Union[str, Any] = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 21 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __A :
def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ):
'''simple docstring'''
__magic_name__ : Optional[int] =parent
__magic_name__ : List[Any] =batch_size
__magic_name__ : List[str] =is_training
__magic_name__ : List[str] =use_auxiliary_loss
__magic_name__ : Union[str, Any] =num_queries
__magic_name__ : str =num_channels
__magic_name__ : Union[str, Any] =min_size
__magic_name__ : Union[str, Any] =max_size
__magic_name__ : Optional[int] =num_labels
__magic_name__ : Tuple =hidden_dim
__magic_name__ : Any =hidden_dim
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
__magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
__magic_name__ : List[str] =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
__magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
__magic_name__ : str =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__magic_name__ : str =self.num_queries
__magic_name__ : Dict =self.num_labels
__magic_name__ : int =[1, 1, 1, 1]
__magic_name__ : List[str] =self.num_channels
__magic_name__ : str =64
__magic_name__ : List[str] =1_28
__magic_name__ : Optional[Any] =self.hidden_dim
__magic_name__ : Tuple =self.hidden_dim
__magic_name__ : Optional[int] =self.hidden_dim
return config
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =output.encoder_hidden_states
__magic_name__ : List[str] =output.pixel_decoder_hidden_states
__magic_name__ : int =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_layers )
def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ):
'''simple docstring'''
with torch.no_grad():
__magic_name__ : List[str] =MaskaFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__snake_case , __snake_case )
def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case :List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : List[str] =model(__snake_case )
comm_check_on_output(__snake_case )
__magic_name__ : Any =model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Any =MaskaFormerModelTester(self )
__magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def A__ ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def A__ ( self :int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple =model_class(__snake_case )
__magic_name__ : Optional[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple =[*signature.parameters.keys()]
__magic_name__ : Optional[Any] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =(self.model_tester.min_size,) * 2
__magic_name__ : Union[str, Any] ={
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
__magic_name__ : Optional[Any] =self.model_tester.get_config()
__magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case )
__magic_name__ : Any =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case )
__magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def A__ ( self :int ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ : List[Any] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Dict =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Tuple =True
__magic_name__ : Optional[int] =True
__magic_name__ : int =model_class(__snake_case ).to(__snake_case )
model.train()
__magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
__magic_name__ : Optional[int] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__magic_name__ : Optional[int] =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase_ : Dict = 1e-4
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case )
__magic_name__ : int =self.default_image_processor
__magic_name__ : List[Any] =prepare_img()
__magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Dict =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : List[str] =model(**__snake_case )
__magic_name__ : Any =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Dict =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Any =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Optional[int] =self.default_image_processor
__magic_name__ : Tuple =prepare_img()
__magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
# masks_queries_logits
__magic_name__ : List[Any] =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__magic_name__ : List[Any] =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
__magic_name__ : Any =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__magic_name__ : int =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Any =self.default_image_processor
__magic_name__ : Union[str, Any] =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
__magic_name__ : str =inputs["""pixel_values"""].to(__snake_case )
__magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]]
__magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__magic_name__ : Dict =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =word.split()
def justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> str:
__magic_name__ : Optional[Any] =max_width - width
__magic_name__ : Optional[Any] =len(lowerCamelCase )
if len(lowerCamelCase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
__magic_name__ : List[Any] =words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
__magic_name__ : Any =spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
__magic_name__ : Tuple =(
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(lowerCamelCase ):
num_spaces_between_words_list[i] += 1
__magic_name__ : Dict =[]
for i in range(lowerCamelCase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * """ """ )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(lowerCamelCase )
__magic_name__ : Union[str, Any] =[]
__magic_name__ : list[str] =[]
__magic_name__ : Any =0
for word in words:
if width + len(lowerCamelCase ) + len(lowerCamelCase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(lowerCamelCase )
width += len(lowerCamelCase )
else:
# justify the line and add it to result
answer.append(justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
# reset new line and new width
__magic_name__ , __magic_name__ : int =[word], len(lowerCamelCase )
__magic_name__ : Optional[int] =max_width - width - len(lowerCamelCase )
answer.append(""" """.join(lowerCamelCase ) + (remaining_spaces + 1) * """ """ )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 21 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """segformer"""
def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(**__snake_case )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , )
__magic_name__ : Dict =num_channels
__magic_name__ : str =num_encoder_blocks
__magic_name__ : List[Any] =depths
__magic_name__ : Optional[Any] =sr_ratios
__magic_name__ : List[str] =hidden_sizes
__magic_name__ : List[str] =patch_sizes
__magic_name__ : Any =strides
__magic_name__ : Optional[Any] =mlp_ratios
__magic_name__ : str =num_attention_heads
__magic_name__ : int =hidden_act
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : Optional[Any] =attention_probs_dropout_prob
__magic_name__ : Optional[Any] =classifier_dropout_prob
__magic_name__ : List[str] =initializer_range
__magic_name__ : List[str] =drop_path_rate
__magic_name__ : List[Any] =layer_norm_eps
__magic_name__ : List[str] =decoder_hidden_size
__magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case )
__magic_name__ : Dict =semantic_loss_ignore_index
class __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :Any ):
'''simple docstring'''
return 1E-4
@property
def A__ ( self :int ):
'''simple docstring'''
return 12
| 21 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase_ : Union[str, Any] = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
UpperCAmelCase_ : Any = {
"gpt2": 1024,
"gpt2-medium": 1024,
"gpt2-large": 1024,
"gpt2-xl": 1024,
"distilgpt2": 1024,
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
UpperCamelCase = GPTaTokenizer
def __init__( self :Dict , __snake_case :int=None , __snake_case :Union[str, Any]=None , __snake_case :Any=None , __snake_case :List[str]="<|endoftext|>" , __snake_case :int="<|endoftext|>" , __snake_case :Union[str, Any]="<|endoftext|>" , __snake_case :Optional[int]=False , **__snake_case :Optional[Any] , ):
'''simple docstring'''
super().__init__(
__snake_case , __snake_case , tokenizer_file=__snake_case , unk_token=__snake_case , bos_token=__snake_case , eos_token=__snake_case , add_prefix_space=__snake_case , **__snake_case , )
__magic_name__ : Union[str, Any] =kwargs.pop("""add_bos_token""" , __snake_case )
__magic_name__ : Dict =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __snake_case ) != add_prefix_space:
__magic_name__ : Dict =getattr(__snake_case , pre_tok_state.pop("""type""" ) )
__magic_name__ : Dict =add_prefix_space
__magic_name__ : List[str] =pre_tok_class(**__snake_case )
__magic_name__ : Union[str, Any] =add_prefix_space
def A__ ( self :Dict , *__snake_case :List[Any] , **__snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =kwargs.get("""is_split_into_words""" , __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(*__snake_case , **__snake_case )
def A__ ( self :str , *__snake_case :List[str] , **__snake_case :Tuple ):
'''simple docstring'''
__magic_name__ : Any =kwargs.get("""is_split_into_words""" , __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(*__snake_case , **__snake_case )
def A__ ( self :Dict , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
__magic_name__ : Tuple =self._tokenizer.model.save(__snake_case , name=__snake_case )
return tuple(__snake_case )
def A__ ( self :Optional[Any] , __snake_case :"Conversation" ):
'''simple docstring'''
__magic_name__ : Dict =[]
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__snake_case , add_special_tokens=__snake_case ) + [self.eos_token_id] )
if len(__snake_case ) > self.model_max_length:
__magic_name__ : Union[str, Any] =input_ids[-self.model_max_length :]
return input_ids
| 21 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
__magic_name__ : Tuple =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0]
chosen_vertices.add(lowerCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__magic_name__ : Tuple =elem[1][1].index(lowerCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[Any] =[0] * len(lowerCamelCase )
__magic_name__ : Optional[Any] =[]
__magic_name__ : str =[1] * len(lowerCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCamelCase ) ):
if indegree[i] == 0:
queue.append(lowerCamelCase )
while queue:
__magic_name__ : List[str] =queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__magic_name__ : Optional[int] =long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(lowerCamelCase )
print(max(lowerCamelCase ) )
# Adjacency list of Graph
UpperCAmelCase_ : List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) )
__magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) )
__magic_name__ , __magic_name__ : Tuple =0, 0
__magic_name__ : Optional[Any] =n - i
__magic_name__ : Union[str, Any] =memo.get(lowerCamelCase )
if sub_memo is not None:
__magic_name__ : int =sub_memo.get(lowerCamelCase )
if jumps is not None and len(lowerCamelCase ) > 0:
# find and make the largest jump without going over
__magic_name__ : Dict =-1
for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__magic_name__ : Optional[Any] =_k
break
if max_jump >= 0:
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump]
# since the difference between jumps is cached, add c
__magic_name__ : Tuple =diff + c
for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ):
__magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 )
if new_c > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__magic_name__ : str =[]
else:
__magic_name__ : List[str] ={c: []}
__magic_name__ : List[str] =sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
__magic_name__ : Tuple =sub_memo[c]
# keep jumps sorted by # of terms skipped
__magic_name__ : List[Any] =0
while j < len(lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if i >= n:
return 0, i
if k > len(lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__magic_name__ : Tuple =i
__magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0
for j in range(len(lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__magic_name__ : Optional[Any] =ds_c + ds_b
diff += addend
__magic_name__ : str =0
for j in range(lowerCamelCase ):
__magic_name__ : int =a_i[j] + addend
__magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return diff, i - start_i
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for j in range(lowerCamelCase , len(lowerCamelCase ) ):
__magic_name__ : Tuple =digits[j] + addend
if s >= 10:
__magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 )
__magic_name__ : int =addend // 10 + quotient
else:
__magic_name__ : Dict =s
__magic_name__ : Any =addend // 10
if addend == 0:
break
while addend > 0:
__magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 )
digits.append(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase = 10**15 ):
__magic_name__ : List[str] =[1]
__magic_name__ : str =1
__magic_name__ : str =0
while True:
__magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
__magic_name__ : int =0
for j in range(len(lowerCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 21 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
UpperCAmelCase_ : Dict = None
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase_ : Dict = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
},
"tokenizer_file": {
"google/bigbird-roberta-base": (
"https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
),
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase_ : Optional[int] = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
UpperCAmelCase_ : Any = "▁"
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = BigBirdTokenizer
UpperCamelCase = ["""input_ids""", """attention_mask"""]
UpperCamelCase = []
def __init__( self :List[str] , __snake_case :List[Any]=None , __snake_case :Tuple=None , __snake_case :Dict="<unk>" , __snake_case :Union[str, Any]="<s>" , __snake_case :List[Any]="</s>" , __snake_case :List[Any]="<pad>" , __snake_case :Optional[Any]="[SEP]" , __snake_case :Union[str, Any]="[MASK]" , __snake_case :Dict="[CLS]" , **__snake_case :Tuple , ):
'''simple docstring'''
__magic_name__ : Dict =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else bos_token
__magic_name__ : Dict =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else eos_token
__magic_name__ : Optional[Any] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else unk_token
__magic_name__ : Optional[Any] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else pad_token
__magic_name__ : Union[str, Any] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else cls_token
__magic_name__ : str =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__magic_name__ : Union[str, Any] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token
super().__init__(
__snake_case , tokenizer_file=__snake_case , 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 , )
__magic_name__ : List[str] =vocab_file
__magic_name__ : List[str] =False if not self.vocab_file else True
def A__ ( self :Dict , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Dict =[self.sep_token_id]
__magic_name__ : 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 A__ ( self :int , __snake_case :List[int] , __snake_case :Optional[List[int]] = None , __snake_case :bool = 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 None:
return [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case )) + [1]
def A__ ( self :Tuple , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =[self.sep_token_id]
__magic_name__ : Optional[int] =[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 A__ ( self :Dict , __snake_case :str , __snake_case :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(__snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__magic_name__ : Dict =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,)
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A ( unittest.TestCase ):
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Tuple =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.dummy_uncond_unet
__magic_name__ : Tuple =KarrasVeScheduler()
__magic_name__ : List[str] =KarrasVePipeline(unet=__snake_case , scheduler=__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : str =torch.manual_seed(0 )
__magic_name__ : int =pipe(num_inference_steps=2 , generator=__snake_case , output_type="""numpy""" ).images
__magic_name__ : Optional[Any] =torch.manual_seed(0 )
__magic_name__ : Union[str, Any] =pipe(num_inference_steps=2 , generator=__snake_case , output_type="""numpy""" , return_dict=__snake_case )[0]
__magic_name__ : Tuple =image[0, -3:, -3:, -1]
__magic_name__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : List[Any] =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Dict ="""google/ncsnpp-celebahq-256"""
__magic_name__ : str =UNetaDModel.from_pretrained(__snake_case )
__magic_name__ : int =KarrasVeScheduler()
__magic_name__ : List[Any] =KarrasVePipeline(unet=__snake_case , scheduler=__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : List[str] =torch.manual_seed(0 )
__magic_name__ : List[str] =pipe(num_inference_steps=20 , generator=__snake_case , output_type="""numpy""" ).images
__magic_name__ : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__magic_name__ : str =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 21 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __A ( UpperCamelCase__ ):
UpperCamelCase = (KDPMaDiscreteScheduler,)
UpperCamelCase = 10
def A__ ( self :List[str] , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[str] ={
"""num_train_timesteps""": 11_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**__snake_case )
return config
def A__ ( self :str ):
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__snake_case , beta_end=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : Dict =self.scheduler_classes[0]
__magic_name__ : Optional[Any] =self.get_scheduler_config(prediction_type="""v_prediction""" )
__magic_name__ : List[Any] =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__magic_name__ : List[str] =self.dummy_model()
__magic_name__ : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
__magic_name__ : Union[str, Any] =sample.to(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
__magic_name__ : Tuple =scheduler.scale_model_input(__snake_case , __snake_case )
__magic_name__ : Optional[Any] =model(__snake_case , __snake_case )
__magic_name__ : Tuple =scheduler.step(__snake_case , __snake_case , __snake_case )
__magic_name__ : Any =output.prev_sample
__magic_name__ : int =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : Optional[Any] =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0002 ) < 1E-3
def A__ ( self :Dict ):
'''simple docstring'''
if torch_device == "mps":
return
__magic_name__ : List[str] =self.scheduler_classes[0]
__magic_name__ : Dict =self.get_scheduler_config()
__magic_name__ : Any =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__magic_name__ : Tuple =self.dummy_model()
__magic_name__ : List[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
__magic_name__ : Tuple =sample.to(__snake_case )
for i, t in enumerate(scheduler.timesteps ):
__magic_name__ : Dict =scheduler.scale_model_input(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =model(__snake_case , __snake_case )
__magic_name__ : Optional[Any] =scheduler.step(__snake_case , __snake_case , __snake_case )
__magic_name__ : str =output.prev_sample
__magic_name__ : List[Any] =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : str =torch.mean(torch.abs(__snake_case ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
def A__ ( self :Any ):
'''simple docstring'''
if torch_device == "mps":
return
__magic_name__ : Tuple =self.scheduler_classes[0]
__magic_name__ : List[str] =self.get_scheduler_config()
__magic_name__ : int =scheduler_class(**__snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=__snake_case )
__magic_name__ : str =self.dummy_model()
__magic_name__ : Optional[int] =self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__magic_name__ : Union[str, Any] =scheduler.scale_model_input(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =model(__snake_case , __snake_case )
__magic_name__ : Tuple =scheduler.step(__snake_case , __snake_case , __snake_case )
__magic_name__ : Optional[Any] =output.prev_sample
__magic_name__ : Dict =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : Union[str, Any] =torch.mean(torch.abs(__snake_case ) )
if str(__snake_case ).startswith("""cpu""" ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1E-2
assert abs(result_mean.item() - 0.0266 ) < 1E-3
| 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
UpperCAmelCase_ : int = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class __A ( unittest.TestCase ):
def __init__( self :Optional[Any] , __snake_case :Dict , __snake_case :List[str]=7 , __snake_case :Union[str, Any]=3 , __snake_case :int=18 , __snake_case :str=30 , __snake_case :int=4_00 , __snake_case :Dict=None , __snake_case :Tuple=True , __snake_case :Any=True , __snake_case :Tuple=None , ):
'''simple docstring'''
__magic_name__ : str =size if size is not None else {"""height""": 20, """width""": 20}
__magic_name__ : Optional[Any] =parent
__magic_name__ : Optional[Any] =batch_size
__magic_name__ : Optional[int] =num_channels
__magic_name__ : List[Any] =image_size
__magic_name__ : Optional[Any] =min_resolution
__magic_name__ : str =max_resolution
__magic_name__ : List[Any] =size
__magic_name__ : Any =do_normalize
__magic_name__ : int =do_convert_rgb
__magic_name__ : Any =[5_12, 10_24, 20_48, 40_96]
__magic_name__ : List[Any] =patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
def A__ ( self :Dict ):
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] ="""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
__magic_name__ : Any =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert("""RGB""" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = PixaStructImageProcessor if is_vision_available() else None
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =PixaStructImageProcessingTester(self )
@property
def A__ ( self :str ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , """do_normalize""" ) )
self.assertTrue(hasattr(__snake_case , """do_convert_rgb""" ) )
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.image_processor_tester.prepare_dummy_image()
__magic_name__ : Dict =self.image_processing_class(**self.image_processor_dict )
__magic_name__ : str =20_48
__magic_name__ : Tuple =image_processor(__snake_case , return_tensors="""pt""" , max_patches=__snake_case )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1E-3 , rtol=1E-3 ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ : Optional[int] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
__magic_name__ : Union[str, Any] =(
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__magic_name__ : Union[str, Any] =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__magic_name__ : List[str] =image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Any =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ : str =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
__magic_name__ : str =(
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
__magic_name__ : List[Any] =True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(__snake_case ):
__magic_name__ : Optional[Any] =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
__magic_name__ : Optional[int] ="""Hello"""
__magic_name__ : List[Any] =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case , header_text=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__magic_name__ : Optional[Any] =image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case , header_text=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__magic_name__ : str =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
__magic_name__ : List[Any] =(
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__magic_name__ : Optional[Any] =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__magic_name__ : str =image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Dict =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__magic_name__ : Union[str, Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
__magic_name__ : Union[str, Any] =(
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__magic_name__ : Tuple =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__magic_name__ : str =image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = PixaStructImageProcessor if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Tuple =PixaStructImageProcessingTester(self , num_channels=4 )
__magic_name__ : Optional[int] =3
@property
def A__ ( self :Any ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , """do_normalize""" ) )
self.assertTrue(hasattr(__snake_case , """do_convert_rgb""" ) )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ : Tuple =prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
__magic_name__ : str =(
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__magic_name__ : Any =image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__magic_name__ : List[Any] =image_processor(
__snake_case , return_tensors="""pt""" , max_patches=__snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__magic_name__ : Dict =F"{src_lang}-{tgt_lang}"
print(F"Converting {dataset}-{pair}" )
__magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__magic_name__ : Optional[int] =F"{dataset}-{pair}"
__magic_name__ : int =Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
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
__magic_name__ : Dict ="""val""" if split == """validation""" else split
__magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" )
__magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" )
__magic_name__ : Optional[Any] =src_path.open("""w+""" )
__magic_name__ : List[Any] =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] ):
__magic_name__ : str =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)
| 21 | 1 |
import argparse
import copy
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple ={}
with open(lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__magic_name__ : Optional[Any] =[]
_list.append([line.split()[1], line.split()[2]] )
__magic_name__ : Dict =_list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
__magic_name__ : List[Any] =[]
_list.append([line.split()[0], line.split()[2]] )
__magic_name__ : Tuple =_list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase ) as f:
__magic_name__ : Optional[int] =f.read(1 )
__magic_name__ : Any =start_node
__magic_name__ : Union[str, Any] =[]
__magic_name__ : Dict =start_node
__magic_name__ : List[Any] =0
while visiting not in first_solution:
__magic_name__ : Dict =10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCamelCase ) and k[0] not in first_solution:
__magic_name__ : Tuple =k[1]
__magic_name__ : str =k[0]
first_solution.append(lowerCamelCase )
__magic_name__ : Optional[Any] =distance_of_first_solution + int(lowerCamelCase )
__magic_name__ : Tuple =best_node
first_solution.append(lowerCamelCase )
__magic_name__ : Optional[Any] =0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
__magic_name__ : Union[str, Any] =(
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =[]
for n in solution[1:-1]:
__magic_name__ : Union[str, Any] =solution.index(lowerCamelCase )
for kn in solution[1:-1]:
__magic_name__ : Union[str, Any] =solution.index(lowerCamelCase )
if n == kn:
continue
__magic_name__ : str =copy.deepcopy(lowerCamelCase )
__magic_name__ : List[str] =kn
__magic_name__ : List[str] =n
__magic_name__ : List[Any] =0
for k in _tmp[:-1]:
__magic_name__ : Optional[int] =_tmp[_tmp.index(lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
__magic_name__ : int =distance + int(i[1] )
_tmp.append(lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
__magic_name__ : List[str] =len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[str] =1
__magic_name__ : List[str] =first_solution
__magic_name__ : int =[]
__magic_name__ : Dict =distance_of_first_solution
__magic_name__ : Union[str, Any] =solution
while count <= iters:
__magic_name__ : Dict =find_neighborhood(lowerCamelCase , lowerCamelCase )
__magic_name__ : Any =0
__magic_name__ : Any =neighborhood[index_of_best_solution]
__magic_name__ : Optional[Any] =len(lowerCamelCase ) - 1
__magic_name__ : List[str] =False
while not found:
__magic_name__ : Any =0
while i < len(lowerCamelCase ):
if best_solution[i] != solution[i]:
__magic_name__ : Any =best_solution[i]
__magic_name__ : Optional[Any] =solution[i]
break
__magic_name__ : int =i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
__magic_name__ : Optional[int] =True
__magic_name__ : List[str] =best_solution[:-1]
__magic_name__ : Optional[Any] =neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
__magic_name__ : List[Any] =cost
__magic_name__ : List[Any] =solution
else:
__magic_name__ : Optional[Any] =index_of_best_solution + 1
__magic_name__ : List[str] =neighborhood[index_of_best_solution]
if len(lowerCamelCase ) >= size:
tabu_list.pop(0 )
__magic_name__ : Optional[int] =count + 1
return best_solution_ever, best_cost
def lowerCAmelCase_ ( lowerCamelCase=None ):
__magic_name__ : int =generate_neighbours(args.File )
__magic_name__ , __magic_name__ : str =generate_first_solution(
args.File , lowerCamelCase )
__magic_name__ , __magic_name__ : List[Any] =tabu_search(
lowerCamelCase , lowerCamelCase , lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 21 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = MgpstrTokenizer
UpperCamelCase = False
UpperCamelCase = {}
UpperCamelCase = False
def A__ ( self :Tuple ):
'''simple docstring'''
super().setUp()
# fmt: off
__magic_name__ : int =["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
__magic_name__ : Union[str, Any] =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__magic_name__ : Optional[int] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__snake_case ) + """\n""" )
def A__ ( self :str , **__snake_case :Tuple ):
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def A__ ( self :List[str] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""tester"""
__magic_name__ : Any ="""tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Tuple =self.get_tokenizers(do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ : Union[str, Any] ="""[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
__magic_name__ : Optional[Any] =tokenizer.encode([special_token] , add_special_tokens=__snake_case )
self.assertEqual(len(__snake_case ) , 1 )
__magic_name__ : int =tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
self.assertTrue(special_token not in decoded )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ , __magic_name__ : List[str] =self.get_input_output_texts(__snake_case )
__magic_name__ : Dict =tokenizer.tokenize(__snake_case )
__magic_name__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__snake_case )
__magic_name__ : Dict =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
__magic_name__ : Tuple =tokenizer.convert_ids_to_tokens(__snake_case )
self.assertNotEqual(len(__snake_case ) , 0 )
__magic_name__ : Optional[Any] =tokenizer.decode(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(text_a.replace(""" """ , """""" ) , __snake_case )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
| 21 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
try:
__magic_name__ : Union[str, Any] =os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__magic_name__ : List[str] =default
else:
# KEY is set, convert it to True or False.
try:
__magic_name__ : int =strtobool(lowerCamelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"If set, {key} must be yes or no." )
return _value
UpperCAmelCase_ : Dict = parse_flag_from_env("RUN_SLOW", default=False)
UpperCAmelCase_ : str = parse_flag_from_env("RUN_REMOTE", default=False)
UpperCAmelCase_ : int = parse_flag_from_env("RUN_LOCAL", default=True)
UpperCAmelCase_ : Any = parse_flag_from_env("RUN_PACKAGED", default=True)
# Compression
UpperCAmelCase_ : Any = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
UpperCAmelCase_ : Tuple = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
UpperCAmelCase_ : Optional[int] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")
# Audio
UpperCAmelCase_ : List[Any] = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("soundfile") is None or version.parse(importlib_metadata.version("soundfile")) < version.parse("0.12.0"),
reason="test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; ",
)
# Beam
UpperCAmelCase_ : str = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("0.3.2"),
reason="test requires apache-beam and a compatible dill version",
)
# Dill-cloudpickle compatibility
UpperCAmelCase_ : Optional[int] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("0.3.2"),
reason="test requires dill>0.3.2 for cloudpickle compatibility",
)
# Windows
UpperCAmelCase_ : Tuple = pytest.mark.skipif(
sys.platform == "win32",
reason="test should not be run on Windows",
)
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import faiss # noqa
except ImportError:
__magic_name__ : List[str] =unittest.skip("""test requires faiss""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import regex # noqa
except ImportError:
__magic_name__ : Union[str, Any] =unittest.skip("""test requires regex""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import elasticsearch # noqa
except ImportError:
__magic_name__ : Any =unittest.skip("""test requires elasticsearch""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import sqlalchemy # noqa
except ImportError:
__magic_name__ : Tuple =unittest.skip("""test requires sqlalchemy""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not config.TORCH_AVAILABLE:
__magic_name__ : List[Any] =unittest.skip("""test requires PyTorch""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not config.TF_AVAILABLE:
__magic_name__ : List[str] =unittest.skip("""test requires TensorFlow""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not config.JAX_AVAILABLE:
__magic_name__ : Union[str, Any] =unittest.skip("""test requires JAX""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not config.PIL_AVAILABLE:
__magic_name__ : Optional[Any] =unittest.skip("""test requires Pillow""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("""test requires transformers""" )(lowerCamelCase )
else:
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("""test requires tiktoken""" )(lowerCamelCase )
else:
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("""test requires spacy""" )(lowerCamelCase )
else:
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
def _require_spacy_model(lowerCamelCase ):
try:
import spacy # noqa F401
spacy.load(lowerCamelCase )
except ImportError:
return unittest.skip("""test requires spacy""" )(lowerCamelCase )
except OSError:
return unittest.skip("""test requires spacy model '{}'""".format(lowerCamelCase ) )(lowerCamelCase )
else:
return test_case
return _require_spacy_model
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("""test requires pyspark""" )(lowerCamelCase )
else:
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("""test requires joblibspark""" )(lowerCamelCase )
else:
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not _run_slow_tests or _run_slow_tests == 0:
__magic_name__ : List[str] =unittest.skip("""test is slow""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not _run_local_tests or _run_local_tests == 0:
__magic_name__ : Any =unittest.skip("""test is local""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not _run_packaged_tests or _run_packaged_tests == 0:
__magic_name__ : Dict =unittest.skip("""test is packaged""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( lowerCamelCase ):
if not _run_remote_tests or _run_remote_tests == 0:
__magic_name__ : Optional[Any] =unittest.skip("""test requires remote""" )(lowerCamelCase )
return test_case
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(lowerCamelCase ) and name.startswith("""test""" ):
for decorator in decorators:
__magic_name__ : Optional[Any] =decorator(lowerCamelCase )
setattr(cls , lowerCamelCase , lowerCamelCase )
return cls
return decorate
class __A ( UpperCamelCase__ ):
pass
class __A ( UpperCamelCase__ ):
UpperCamelCase = 0
UpperCamelCase = 1
UpperCamelCase = 2
@contextmanager
def lowerCAmelCase_ ( lowerCamelCase=OfflineSimulationMode.CONNECTION_FAILS , lowerCamelCase=1E-16 ):
__magic_name__ : Union[str, Any] =requests.Session().request
def timeout_request(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
# Change the url to an invalid url so that the connection hangs
__magic_name__ : Tuple ="""https://10.255.255.1"""
if kwargs.get("""timeout""" ) is None:
raise RequestWouldHangIndefinitelyError(
F"Tried a call to {url} in offline mode with no timeout set. Please set a timeout." )
__magic_name__ : Tuple =timeout
try:
return online_request(lowerCamelCase , lowerCamelCase , **lowerCamelCase )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
__magic_name__ : Tuple =url
__magic_name__ : List[str] =e.args[0]
__magic_name__ : Optional[int] =(max_retry_error.args[0].replace("""10.255.255.1""" , F"OfflineMock[{url}]" ),)
__magic_name__ : Union[str, Any] =(max_retry_error,)
raise
def raise_connection_error(lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
raise requests.ConnectionError("""Offline mode is enabled.""" , request=lowerCamelCase )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("""requests.Session.send""" , lowerCamelCase ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("""requests.Session.request""" , lowerCamelCase ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowerCamelCase ):
yield
else:
raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" )
@contextmanager
def lowerCAmelCase_ ( *lowerCamelCase , **lowerCamelCase ):
__magic_name__ : Any =str(Path().resolve() )
with tempfile.TemporaryDirectory(*lowerCamelCase , **lowerCamelCase ) as tmp_dir:
try:
os.chdir(lowerCamelCase )
yield
finally:
os.chdir(lowerCamelCase )
@contextmanager
def lowerCAmelCase_ ( ):
import gc
gc.collect()
__magic_name__ : int =pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowerCAmelCase_ ( ):
import gc
gc.collect()
__magic_name__ : List[str] =pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return deepcopy(lowerCamelCase ).integers(0 , 100 , 10 ).tolist() == deepcopy(lowerCamelCase ).integers(0 , 100 , 10 ).tolist()
def lowerCAmelCase_ ( lowerCamelCase ):
import decorator
from requests.exceptions import HTTPError
def _wrapper(lowerCamelCase , *lowerCamelCase , **lowerCamelCase ):
try:
return func(*lowerCamelCase , **lowerCamelCase )
except HTTPError as err:
if str(lowerCamelCase ).startswith("""500""" ) or str(lowerCamelCase ).startswith("""502""" ):
pytest.xfail(str(lowerCamelCase ) )
raise err
return decorator.decorator(_wrapper , lowerCamelCase )
class __A :
def __init__( self :Any , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[Any] =returncode
__magic_name__ : Any =stdout
__magic_name__ : Any =stderr
async def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
while True:
__magic_name__ : Any =await stream.readline()
if line:
callback(lowerCamelCase )
else:
break
async def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=False , lowerCamelCase=False ):
if echo:
print("""\nRunning: """ , """ """.join(lowerCamelCase ) )
__magic_name__ : Optional[int] =await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=lowerCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=lowerCamelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__magic_name__ : Tuple =[]
__magic_name__ : int =[]
def tee(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase="" ):
__magic_name__ : Tuple =line.decode("""utf-8""" ).rstrip()
sink.append(lowerCamelCase )
if not quiet:
print(lowerCamelCase , lowerCamelCase , file=lowerCamelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda lowerCamelCase : tee(lowerCamelCase , lowerCamelCase , sys.stdout , label="""stdout:""" ) ),
_read_stream(p.stderr , lambda lowerCamelCase : tee(lowerCamelCase , lowerCamelCase , sys.stderr , label="""stderr:""" ) ),
] , timeout=lowerCamelCase , )
return _RunOutput(await p.wait() , lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=180 , lowerCamelCase=False , lowerCamelCase=True ):
__magic_name__ : Tuple =asyncio.get_event_loop()
__magic_name__ : Any =loop.run_until_complete(
_stream_subprocess(lowerCamelCase , env=lowerCamelCase , stdin=lowerCamelCase , timeout=lowerCamelCase , quiet=lowerCamelCase , echo=lowerCamelCase ) )
__magic_name__ : Optional[int] =""" """.join(lowerCamelCase )
if result.returncode > 0:
__magic_name__ : List[str] ="""\n""".join(result.stderr )
raise RuntimeError(
F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
F"The combined stderr from workers follows:\n{stderr}" )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F"'{cmd_str}' produced no output." )
return result
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =os.environ.get("""PYTEST_XDIST_WORKER""" , """gw0""" )
__magic_name__ : str =re.sub(R"""^gw""" , """""" , lowerCamelCase , 0 , re.M )
return int(lowerCamelCase )
def lowerCAmelCase_ ( ):
__magic_name__ : Optional[int] =29500
__magic_name__ : int =pytest_xdist_worker_id()
return port + uniq_delta
| 21 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : List[Any] =max_length
__magic_name__ : Dict =vocab
__magic_name__ : int =merges
__magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__magic_name__ : str =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
return cls(**__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case )
__magic_name__ : Tuple =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__magic_name__ : Tuple =max_length if max_length is not None else self.max_length
if max_length is not None:
__magic_name__ , __magic_name__ : Tuple =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 21 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase_ : Optional[int] = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """esm"""
def __init__( self :List[Any] , __snake_case :int=None , __snake_case :List[Any]=None , __snake_case :Tuple=None , __snake_case :List[str]=7_68 , __snake_case :str=12 , __snake_case :int=12 , __snake_case :str=30_72 , __snake_case :int=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :Optional[Any]=10_26 , __snake_case :Dict=0.02 , __snake_case :List[str]=1E-12 , __snake_case :Any="absolute" , __snake_case :Tuple=True , __snake_case :Any=None , __snake_case :Dict=False , __snake_case :List[str]=False , __snake_case :Tuple=None , __snake_case :List[str]=None , **__snake_case :Any , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , mask_token_id=__snake_case , **__snake_case )
__magic_name__ : Dict =vocab_size
__magic_name__ : Optional[Any] =hidden_size
__magic_name__ : Any =num_hidden_layers
__magic_name__ : int =num_attention_heads
__magic_name__ : Any =intermediate_size
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : str =attention_probs_dropout_prob
__magic_name__ : Union[str, Any] =max_position_embeddings
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[Any] =layer_norm_eps
__magic_name__ : Optional[int] =position_embedding_type
__magic_name__ : int =use_cache
__magic_name__ : Any =emb_layer_norm_before
__magic_name__ : List[str] =token_dropout
__magic_name__ : List[str] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
__magic_name__ : Tuple =EsmFoldConfig()
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =EsmFoldConfig(**__snake_case )
__magic_name__ : Dict =esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
__magic_name__ : List[Any] =get_default_vocab_list()
else:
__magic_name__ : Any =vocab_list
else:
__magic_name__ : Optional[Any] =None
__magic_name__ : Optional[Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , __snake_case ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =super().to_dict()
if isinstance(self.esmfold_config , __snake_case ):
__magic_name__ : str =self.esmfold_config.to_dict()
return output
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = 0
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = 128
UpperCamelCase = None
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
if self.trunk is None:
__magic_name__ : Dict =TrunkConfig()
elif isinstance(self.trunk , __snake_case ):
__magic_name__ : Optional[int] =TrunkConfig(**self.trunk )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =asdict(self )
__magic_name__ : str =self.trunk.to_dict()
return output
@dataclass
class __A :
UpperCamelCase = 48
UpperCamelCase = 1024
UpperCamelCase = 128
UpperCamelCase = 32
UpperCamelCase = 32
UpperCamelCase = 32
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = False
UpperCamelCase = 4
UpperCamelCase = 128
UpperCamelCase = None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
if self.structure_module is None:
__magic_name__ : Dict =StructureModuleConfig()
elif isinstance(self.structure_module , __snake_case ):
__magic_name__ : int =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
f" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
__magic_name__ : Union[str, Any] =self.sequence_state_dim // self.sequence_head_width
__magic_name__ : str =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}." )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : Dict =asdict(self )
__magic_name__ : Union[str, Any] =self.structure_module.to_dict()
return output
@dataclass
class __A :
UpperCamelCase = 384
UpperCamelCase = 128
UpperCamelCase = 16
UpperCamelCase = 128
UpperCamelCase = 12
UpperCamelCase = 4
UpperCamelCase = 8
UpperCamelCase = 0.1
UpperCamelCase = 8
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 7
UpperCamelCase = 10
UpperCamelCase = 1e-8
UpperCamelCase = 1e5
def A__ ( self :Optional[int] ):
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( ):
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 21 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ):
__magic_name__ : Any =nums[i]
__magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip())
UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def __call__( self :Union[str, Any] ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None
__magic_name__ : Optional[int] =len(self.languages ) if self.languages else None
def __call__( self :List[str] ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[int] =set(self.languages )
if self.languages and set(__snake_case ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__magic_name__ : Any =[]
for lang, text in translation_dict.items():
if isinstance(__snake_case , __snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) )
return {"language": languages, "translation": translations}
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 21 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
UpperCAmelCase_ : str = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
UpperCAmelCase_ : Union[str, Any] = {
"ctrl": 256,
}
UpperCAmelCase_ : Optional[Any] = {
"Pregnancy": 168629,
"Christianity": 7675,
"Explain": 106423,
"Fitness": 63440,
"Saving": 63163,
"Ask": 27171,
"Ass": 95985,
"Joke": 163509,
"Questions": 45622,
"Thoughts": 49605,
"Retail": 52342,
"Feminism": 164338,
"Writing": 11992,
"Atheism": 192263,
"Netflix": 48616,
"Computing": 39639,
"Opinion": 43213,
"Alone": 44967,
"Funny": 58917,
"Gaming": 40358,
"Human": 4088,
"India": 1331,
"Joker": 77138,
"Diet": 36206,
"Legal": 11859,
"Norman": 4939,
"Tip": 72689,
"Weight": 52343,
"Movies": 46273,
"Running": 23425,
"Science": 2090,
"Horror": 37793,
"Confession": 60572,
"Finance": 12250,
"Politics": 16360,
"Scary": 191985,
"Support": 12654,
"Technologies": 32516,
"Teenage": 66160,
"Event": 32769,
"Learned": 67460,
"Notion": 182770,
"Wikipedia": 37583,
"Books": 6665,
"Extract": 76050,
"Confessions": 102701,
"Conspiracy": 75932,
"Links": 63674,
"Narcissus": 150425,
"Relationship": 54766,
"Relationships": 134796,
"Reviews": 41671,
"News": 4256,
"Translation": 26820,
"multilingual": 128406,
}
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple =set()
__magic_name__ : Dict =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : List[str] =char
__magic_name__ : List[Any] =set(lowerCamelCase )
return pairs
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = CONTROL_CODES
def __init__( self :List[Any] , __snake_case :Dict , __snake_case :int , __snake_case :Optional[int]="<unk>" , **__snake_case :int ):
'''simple docstring'''
super().__init__(unk_token=__snake_case , **__snake_case )
with open(__snake_case , encoding="""utf-8""" ) as vocab_handle:
__magic_name__ : List[str] =json.load(__snake_case )
__magic_name__ : Any ={v: k for k, v in self.encoder.items()}
with open(__snake_case , encoding="""utf-8""" ) as merges_handle:
__magic_name__ : Optional[int] =merges_handle.read().split("""\n""" )[1:-1]
__magic_name__ : Tuple =[tuple(merge.split() ) for merge in merges]
__magic_name__ : Union[str, Any] =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__magic_name__ : str ={}
@property
def A__ ( self :Dict ):
'''simple docstring'''
return len(self.encoder )
def A__ ( self :Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def A__ ( self :List[str] , __snake_case :Tuple ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__magic_name__ : Any =tuple(__snake_case )
__magic_name__ : List[str] =tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__magic_name__ : Tuple =get_pairs(__snake_case )
if not pairs:
return token
while True:
__magic_name__ : List[Any] =min(__snake_case , key=lambda __snake_case : self.bpe_ranks.get(__snake_case , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : List[Any] =bigram
__magic_name__ : Any =[]
__magic_name__ : Union[str, Any] =0
while i < len(__snake_case ):
try:
__magic_name__ : Union[str, Any] =word.index(__snake_case , __snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : int =j
if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : Tuple =tuple(__snake_case )
__magic_name__ : List[str] =new_word
if len(__snake_case ) == 1:
break
else:
__magic_name__ : Any =get_pairs(__snake_case )
__magic_name__ : Any ="""@@ """.join(__snake_case )
__magic_name__ : Optional[Any] =word[:-4]
__magic_name__ : Union[str, Any] =word
return word
def A__ ( self :Optional[int] , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : str =[]
__magic_name__ : Union[str, Any] =re.findall(r"""\S+\n?""" , __snake_case )
for token in words:
split_tokens.extend(list(self.bpe(__snake_case ).split(""" """ ) ) )
return split_tokens
def A__ ( self :Tuple , __snake_case :str ):
'''simple docstring'''
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def A__ ( self :Optional[int] , __snake_case :Optional[Any] ):
'''simple docstring'''
return self.decoder.get(__snake_case , self.unk_token )
def A__ ( self :Dict , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : List[str] =""" """.join(__snake_case ).replace("""@@ """ , """""" ).strip()
return out_string
def A__ ( self :Any , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(__snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__magic_name__ : str =os.path.join(
__snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__magic_name__ : int =os.path.join(
__snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__snake_case , ensure_ascii=__snake_case ) + """\n""" )
__magic_name__ : int =0
with open(__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 __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!""" )
__magic_name__ : List[str] =token_index
writer.write(""" """.join(__snake_case ) + """\n""" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 21 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3317044064679887385961981 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
__magic_name__ : Optional[Any] =[
2047,
1373653,
25326001,
3215031751,
2152302898747,
3474749660383,
341550071728321,
1,
3825123056546413051,
1,
1,
318665857834031151167461,
3317044064679887385961981,
]
__magic_name__ : Dict =[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(lowerCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
__magic_name__ : Union[str, Any] =primes[:idx]
break
__magic_name__ , __magic_name__ : Tuple =n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
__magic_name__ : Tuple =False
for r in range(lowerCamelCase ):
__magic_name__ : int =pow(lowerCamelCase , d * 2**r , lowerCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
__magic_name__ : Optional[int] =True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase_ ( ):
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(838201 )
assert miller_rabin(838207 )
# 1_373_653
assert not miller_rabin(17316001 )
assert miller_rabin(17316017 )
# 25_326_001
assert not miller_rabin(3078386641 )
assert miller_rabin(3078386653 )
# 3_215_031_751
assert not miller_rabin(1713045574801 )
assert miller_rabin(1713045574819 )
# 2_152_302_898_747
assert not miller_rabin(2779799728307 )
assert miller_rabin(2779799728327 )
# 3_474_749_660_383
assert not miller_rabin(113850023909441 )
assert miller_rabin(113850023909527 )
# 341_550_071_728_321
assert not miller_rabin(1275041018848804351 )
assert miller_rabin(1275041018848804391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(79666464458507787791867 )
assert miller_rabin(79666464458507787791951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552840677446647897660333 )
assert miller_rabin(552840677446647897660359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 21 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
return config
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ):
if conf_path is None:
__magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml"""
__magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase )
__magic_name__ : Tuple =VQModel(**config.model.params )
if ckpt_path is None:
__magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt"""
__magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase )
if ".ckpt" in ckpt_path:
__magic_name__ : Any =sd["""state_dict"""]
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
model.to(lowerCamelCase )
del sd
return model
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__magic_name__ : List[Any] =model.decode(lowerCamelCase )
return xrec
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 )
if reload:
__magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase )
importlib.reload(lowerCamelCase )
return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls )
def lowerCAmelCase_ ( lowerCamelCase ):
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ):
__magic_name__ : str =instantiate_from_config(lowerCamelCase )
if sd is not None:
model.load_state_dict(lowerCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load the specified checkpoint
if ckpt:
__magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Any =pl_sd["""global_step"""]
print(F"loaded model from global step {global_step}." )
else:
__magic_name__ : List[Any] ={"""state_dict""": None}
__magic_name__ : Optional[Any] =None
__magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""]
return model, global_step
| 21 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
if b == 0:
return (1, 0)
((__magic_name__) , (__magic_name__)) : Optional[int] =extended_euclid(lowerCamelCase , a % b )
__magic_name__ : Optional[Any] =a // b
return (y, x - k * y)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
((__magic_name__) , (__magic_name__)) : int =extended_euclid(lowerCamelCase , lowerCamelCase )
__magic_name__ : List[str] =na * na
__magic_name__ : Optional[int] =ra * x * na + ra * y * na
return (n % m + m) % m
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
((__magic_name__) , (__magic_name__)) : Optional[int] =extended_euclid(lowerCamelCase , lowerCamelCase )
if b < 0:
__magic_name__ : str =(b % n + n) % n
return b
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ : Dict =invert_modulo(lowerCamelCase , lowerCamelCase ), invert_modulo(lowerCamelCase , lowerCamelCase )
__magic_name__ : Dict =na * na
__magic_name__ : List[Any] =ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| 21 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_script.main )
def A__ ( self :Dict ):
'''simple docstring'''
debug_launcher(test_ops.main )
| 21 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_ : Any = {
"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",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
UpperCAmelCase_ : List[str] = {
"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,
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
UpperCamelCase = BartTokenizer
def __init__( self :List[Any] , __snake_case :str=None , __snake_case :List[Any]=None , __snake_case :Optional[int]=None , __snake_case :Union[str, Any]="replace" , __snake_case :List[str]="<s>" , __snake_case :List[str]="</s>" , __snake_case :Optional[Any]="</s>" , __snake_case :List[str]="<s>" , __snake_case :Optional[Any]="<unk>" , __snake_case :Dict="<pad>" , __snake_case :Union[str, Any]="<mask>" , __snake_case :Optional[Any]=False , __snake_case :int=True , **__snake_case :str , ):
'''simple docstring'''
super().__init__(
__snake_case , __snake_case , tokenizer_file=__snake_case , errors=__snake_case , bos_token=__snake_case , eos_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , add_prefix_space=__snake_case , trim_offsets=__snake_case , **__snake_case , )
__magic_name__ : List[str] =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __snake_case ) != add_prefix_space:
__magic_name__ : Dict =getattr(__snake_case , pre_tok_state.pop("""type""" ) )
__magic_name__ : List[Any] =add_prefix_space
__magic_name__ : Any =pre_tok_class(**__snake_case )
__magic_name__ : Tuple =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__magic_name__ : List[str] ="""post_processor"""
__magic_name__ : Tuple =getattr(self.backend_tokenizer , __snake_case , __snake_case )
if tokenizer_component_instance:
__magic_name__ : Optional[Any] =json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__magic_name__ : int =tuple(state["""sep"""] )
if "cls" in state:
__magic_name__ : Tuple =tuple(state["""cls"""] )
__magic_name__ : Union[str, Any] =False
if state.get("""add_prefix_space""" , __snake_case ) != add_prefix_space:
__magic_name__ : Tuple =add_prefix_space
__magic_name__ : Union[str, Any] =True
if state.get("""trim_offsets""" , __snake_case ) != trim_offsets:
__magic_name__ : Dict =trim_offsets
__magic_name__ : Optional[Any] =True
if changes_to_apply:
__magic_name__ : Optional[Any] =getattr(__snake_case , state.pop("""type""" ) )
__magic_name__ : List[str] =component_class(**__snake_case )
setattr(self.backend_tokenizer , __snake_case , __snake_case )
@property
def A__ ( self :int ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[Any] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else value
__magic_name__ : Dict =value
def A__ ( self :Union[str, Any] , *__snake_case :Tuple , **__snake_case :List[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =kwargs.get("""is_split_into_words""" , __snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*__snake_case , **__snake_case )
def A__ ( self :str , *__snake_case :Optional[Any] , **__snake_case :Dict ):
'''simple docstring'''
__magic_name__ : Dict =kwargs.get("""is_split_into_words""" , __snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*__snake_case , **__snake_case )
def A__ ( self :Optional[Any] , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
__magic_name__ : int =self._tokenizer.model.save(__snake_case , name=__snake_case )
return tuple(__snake_case )
def A__ ( self :List[Any] , __snake_case :Optional[int] , __snake_case :List[str]=None ):
'''simple docstring'''
__magic_name__ : Any =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A__ ( self :List[Any] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Optional[int] =[self.sep_token_id]
__magic_name__ : Optional[int] =[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]
| 21 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmelCase_ ( lowerCamelCase ):
if not sequence:
return []
if len(lowerCamelCase ) == 1:
return list(lowerCamelCase )
__magic_name__ : int =0
__magic_name__ : str =len(lowerCamelCase ) - 1
__magic_name__ : Optional[Any] =0
while mid <= high:
if sequence[mid] == colors[0]:
__magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
__magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid]
high -= 1
else:
__magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values"
raise ValueError(lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip()
UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
print(F"""{dutch_national_flag_sort(unsorted)}""")
| 21 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __A :
UpperCamelCase = BlenderbotConfig
UpperCamelCase = {}
UpperCamelCase = """gelu"""
def __init__( self :Union[str, Any] , __snake_case :Union[str, Any] , __snake_case :Union[str, Any]=13 , __snake_case :Optional[Any]=7 , __snake_case :Optional[int]=True , __snake_case :Optional[Any]=False , __snake_case :Dict=99 , __snake_case :List[str]=32 , __snake_case :List[str]=2 , __snake_case :List[str]=4 , __snake_case :List[str]=37 , __snake_case :Any=0.1 , __snake_case :List[str]=0.1 , __snake_case :Union[str, Any]=20 , __snake_case :int=2 , __snake_case :Dict=1 , __snake_case :Any=0 , ):
'''simple docstring'''
__magic_name__ : Dict =parent
__magic_name__ : Dict =batch_size
__magic_name__ : Dict =seq_length
__magic_name__ : Union[str, Any] =is_training
__magic_name__ : int =use_labels
__magic_name__ : str =vocab_size
__magic_name__ : Optional[int] =hidden_size
__magic_name__ : List[Any] =num_hidden_layers
__magic_name__ : str =num_attention_heads
__magic_name__ : Dict =intermediate_size
__magic_name__ : int =hidden_dropout_prob
__magic_name__ : Tuple =attention_probs_dropout_prob
__magic_name__ : Union[str, Any] =max_position_embeddings
__magic_name__ : int =eos_token_id
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : Optional[Any] =bos_token_id
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : int =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : Tuple =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict =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 , )
__magic_name__ : List[str] =prepare_blenderbot_inputs_dict(__snake_case , __snake_case , __snake_case )
return config, inputs_dict
def A__ ( self :List[str] , __snake_case :Optional[Any] , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : List[str] =TFBlenderbotModel(config=__snake_case ).get_decoder()
__magic_name__ : Union[str, Any] =inputs_dict["""input_ids"""]
__magic_name__ : Any =input_ids[:1, :]
__magic_name__ : List[str] =inputs_dict["""attention_mask"""][:1, :]
__magic_name__ : Dict =inputs_dict["""head_mask"""]
__magic_name__ : Optional[Any] =1
# first forward pass
__magic_name__ : Tuple =model(__snake_case , attention_mask=__snake_case , head_mask=__snake_case , use_cache=__snake_case )
__magic_name__ , __magic_name__ : List[str] =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Any =ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : int =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Any =tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : Optional[Any] =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : Optional[int] =model(__snake_case , attention_mask=__snake_case )[0]
__magic_name__ : Dict =model(__snake_case , attention_mask=__snake_case , past_key_values=__snake_case )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : Union[str, Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Any =output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : Union[str, Any] =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__snake_case , __snake_case , rtol=1E-3 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
if attention_mask is None:
__magic_name__ : Optional[int] =tf.cast(tf.math.not_equal(lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Optional[Any] =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Tuple =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__magic_name__ : Any =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
UpperCamelCase = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =TFBlenderbotModelTester(self )
__magic_name__ : Dict =ConfigTester(self , config_class=__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__snake_case )
@require_tokenizers
@require_tf
class __A ( unittest.TestCase ):
UpperCamelCase = ["""My friends are cool but they eat too many carbs."""]
UpperCamelCase = """facebook/blenderbot-400M-distill"""
@cached_property
def A__ ( self :List[str] ):
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Tuple =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : int =self.tokenizer(self.src_text , return_tensors="""tf""" )
__magic_name__ : Optional[int] =self.model.generate(
model_inputs.input_ids , )
__magic_name__ : Union[str, Any] =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__snake_case )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 21 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = 1
@register_to_config
def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ):
'''simple docstring'''
__magic_name__ : Dict =None
__magic_name__ : List[str] =None
__magic_name__ : str =None
def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case )
def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__magic_name__ : int =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__magic_name__ : str =std.flatten()
while len(std.shape ) < len(score.shape ):
__magic_name__ : List[str] =std.unsqueeze(-1 )
__magic_name__ : Union[str, Any] =-score / std
# compute
__magic_name__ : Tuple =-1.0 / len(self.timesteps )
__magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__magic_name__ : Dict =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__magic_name__ : Any =beta_t.unsqueeze(-1 )
__magic_name__ : Dict =-0.5 * beta_t * x
__magic_name__ : Optional[int] =torch.sqrt(__snake_case )
__magic_name__ : int =drift - diffusion**2 * score
__magic_name__ : List[str] =x + drift * dt
# add noise
__magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype )
__magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self :List[Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 21 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase_ : str = TypeVar("T")
UpperCAmelCase_ : Optional[int] = Union[List[T], Tuple[T, ...]]
UpperCAmelCase_ : Dict = Union[T, List[T], Dict[str, T]]
UpperCAmelCase_ : Any = Union[str, bytes, os.PathLike]
| 21 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase_ : List[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
UpperCAmelCase_ : int = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
UpperCAmelCase_ : Union[str, Any] = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def lowerCAmelCase_ ( lowerCamelCase ):
def remove_articles(lowerCamelCase ):
__magic_name__ : Optional[Any] =re.compile(R"""\b(a|an|the)\b""" , re.UNICODE )
return re.sub(lowerCamelCase , """ """ , lowerCamelCase )
def white_space_fix(lowerCamelCase ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase ):
__magic_name__ : int =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase ) ) ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return int(normalize_answer(lowerCamelCase ) == normalize_answer(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : int =[any(compute_exact(lowerCamelCase , lowerCamelCase ) for ref in refs ) for pred, refs in zip(lowerCamelCase , lowerCamelCase )]
return (sum(lowerCamelCase ) / len(lowerCamelCase )) * 100
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Any =[rgram for rgrams in rgramslist for rgram in rgrams]
__magic_name__ : Optional[Any] =Counter(lowerCamelCase )
__magic_name__ : Optional[int] =Counter(lowerCamelCase )
__magic_name__ : List[str] =Counter()
for sgram, scount in sgramcounter.items():
__magic_name__ : Optional[Any] =scount * numref
__magic_name__ : List[str] =Counter(lowerCamelCase )
__magic_name__ : List[Any] =Counter()
for cgram, ccount in cgramcounter.items():
__magic_name__ : Optional[Any] =ccount * numref
# KEEP
__magic_name__ : List[Any] =sgramcounter_rep & cgramcounter_rep
__magic_name__ : Tuple =keepgramcounter_rep & rgramcounter
__magic_name__ : List[str] =sgramcounter_rep & rgramcounter
__magic_name__ : Optional[Any] =0
__magic_name__ : Union[str, Any] =0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__magic_name__ : str =1
__magic_name__ : Dict =1
if len(lowerCamelCase ) > 0:
__magic_name__ : List[Any] =keeptmpscorea / len(lowerCamelCase )
if len(lowerCamelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
__magic_name__ : List[str] =keeptmpscorea / sum(keepgramcounterall_rep.values() )
__magic_name__ : List[str] =0
if keepscore_precision > 0 or keepscore_recall > 0:
__magic_name__ : List[Any] =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
__magic_name__ : Optional[int] =sgramcounter_rep - cgramcounter_rep
__magic_name__ : str =delgramcounter_rep - rgramcounter
__magic_name__ : str =sgramcounter_rep - rgramcounter
__magic_name__ : List[str] =0
__magic_name__ : str =0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__magic_name__ : Dict =1
if len(lowerCamelCase ) > 0:
__magic_name__ : Union[str, Any] =deltmpscorea / len(lowerCamelCase )
# ADDITION
__magic_name__ : List[str] =set(lowerCamelCase ) - set(lowerCamelCase )
__magic_name__ : Optional[int] =set(lowerCamelCase ) & set(lowerCamelCase )
__magic_name__ : Dict =set(lowerCamelCase ) - set(lowerCamelCase )
__magic_name__ : Any =0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__magic_name__ : str =1
__magic_name__ : str =1
if len(lowerCamelCase ) > 0:
__magic_name__ : Optional[Any] =addtmpscore / len(lowerCamelCase )
if len(lowerCamelCase ) > 0:
__magic_name__ : Any =addtmpscore / len(lowerCamelCase )
__magic_name__ : Optional[int] =0
if addscore_precision > 0 or addscore_recall > 0:
__magic_name__ : int =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[str] =len(lowerCamelCase )
__magic_name__ : Optional[Any] =ssent.split(""" """ )
__magic_name__ : int =csent.split(""" """ )
__magic_name__ : Any =[]
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : Optional[int] =[]
__magic_name__ : Optional[Any] =[]
__magic_name__ : Any =[]
__magic_name__ : Optional[int] =[]
__magic_name__ : Any =[]
for rsent in rsents:
__magic_name__ : Any =rsent.split(""" """ )
__magic_name__ : Tuple =[]
__magic_name__ : Dict =[]
__magic_name__ : List[str] =[]
ragramslist.append(lowerCamelCase )
for i in range(0 , len(lowerCamelCase ) - 1 ):
if i < len(lowerCamelCase ) - 1:
__magic_name__ : int =ragrams[i] + """ """ + ragrams[i + 1]
ragrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 2:
__magic_name__ : Dict =ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
ragrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 3:
__magic_name__ : int =ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
ragrams.append(lowerCamelCase )
ragramslist.append(lowerCamelCase )
ragramslist.append(lowerCamelCase )
ragramslist.append(lowerCamelCase )
for i in range(0 , len(lowerCamelCase ) - 1 ):
if i < len(lowerCamelCase ) - 1:
__magic_name__ : List[str] =sagrams[i] + """ """ + sagrams[i + 1]
sagrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 2:
__magic_name__ : Any =sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
sagrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 3:
__magic_name__ : Union[str, Any] =sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
sagrams.append(lowerCamelCase )
for i in range(0 , len(lowerCamelCase ) - 1 ):
if i < len(lowerCamelCase ) - 1:
__magic_name__ : List[str] =cagrams[i] + """ """ + cagrams[i + 1]
cagrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 2:
__magic_name__ : List[str] =cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
cagrams.append(lowerCamelCase )
if i < len(lowerCamelCase ) - 3:
__magic_name__ : List[Any] =cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
cagrams.append(lowerCamelCase )
((__magic_name__) , (__magic_name__) , (__magic_name__)) : Any =SARIngram(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
((__magic_name__) , (__magic_name__) , (__magic_name__)) : List[str] =SARIngram(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
((__magic_name__) , (__magic_name__) , (__magic_name__)) : Tuple =SARIngram(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
((__magic_name__) , (__magic_name__) , (__magic_name__)) : Union[str, Any] =SARIngram(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : Any =sum([keepascore, keepascore, keepascore, keepascore] ) / 4
__magic_name__ : Tuple =sum([delascore, delascore, delascore, delascore] ) / 4
__magic_name__ : Dict =sum([addascore, addascore, addascore, addascore] ) / 4
__magic_name__ : int =(avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = True , lowerCamelCase = "13a" , lowerCamelCase = True ):
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
__magic_name__ : Optional[Any] =sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
__magic_name__ : Union[str, Any] =sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase )()(lowerCamelCase )
else:
__magic_name__ : List[Any] =sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase )
elif tokenizer == "moses":
__magic_name__ : Dict =sacremoses.MosesTokenizer().tokenize(lowerCamelCase , return_str=lowerCamelCase , escape=lowerCamelCase )
elif tokenizer == "penn":
__magic_name__ : Any =sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase , return_str=lowerCamelCase )
else:
__magic_name__ : Tuple =sentence
if not return_str:
__magic_name__ : Optional[Any] =normalized_sent.split()
return normalized_sent
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if not (len(lowerCamelCase ) == len(lowerCamelCase ) == len(lowerCamelCase )):
raise ValueError("""Sources length must match predictions and references lengths.""" )
__magic_name__ : List[Any] =0
for src, pred, refs in zip(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
sari_score += SARIsent(normalize(lowerCamelCase ) , normalize(lowerCamelCase ) , [normalize(lowerCamelCase ) for sent in refs] )
__magic_name__ : Any =sari_score / len(lowerCamelCase )
return 100 * sari_score
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase="exp" , lowerCamelCase=None , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=False , ):
__magic_name__ : Tuple =len(references[0] )
if any(len(lowerCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
__magic_name__ : int =[[refs[i] for refs in references] for i in range(lowerCamelCase )]
__magic_name__ : Any =sacrebleu.corpus_bleu(
lowerCamelCase , lowerCamelCase , smooth_method=lowerCamelCase , smooth_value=lowerCamelCase , force=lowerCamelCase , lowercase=lowerCamelCase , use_effective_order=lowerCamelCase , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :Tuple ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=[
"""https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
"""https://github.com/cocoxu/simplification/blob/master/SARI.py""",
"""https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
"""https://github.com/mjpost/sacreBLEU""",
] , reference_urls=[
"""https://www.aclweb.org/anthology/Q16-1029.pdf""",
"""https://github.com/mjpost/sacreBLEU""",
"""https://en.wikipedia.org/wiki/BLEU""",
"""https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
] , )
def A__ ( self :List[str] , __snake_case :str , __snake_case :Dict , __snake_case :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] ={}
result.update({"""sari""": compute_sari(sources=__snake_case , predictions=__snake_case , references=__snake_case )} )
result.update({"""sacrebleu""": compute_sacrebleu(predictions=__snake_case , references=__snake_case )} )
result.update({"""exact""": compute_em(predictions=__snake_case , references=__snake_case )} )
return result
| 21 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
def __init__( self :List[Any] ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =nn.Linear(3 , 4 )
__magic_name__ : Union[str, Any] =nn.BatchNormad(4 )
__magic_name__ : List[str] =nn.Linear(4 , 5 )
def A__ ( self :Dict , __snake_case :Tuple ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) )
class __A ( UpperCamelCase__ ):
def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ):
'''simple docstring'''
return output + 1
class __A ( unittest.TestCase ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : Tuple =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
self.assertEqual(test_model._hf_hook , __snake_case )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : List[str] =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
add_hook_to_module(__snake_case , __snake_case , append=__snake_case )
self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
__magic_name__ : Any =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(x + 1 )
__magic_name__ : Optional[Any] =test_model(x + 2 )
__magic_name__ : int =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : int =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : str =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : List[str] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
assert torch.allclose(__snake_case , __snake_case , atol=1E-5 )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
__magic_name__ : Dict =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(__snake_case )
__magic_name__ : Dict =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Optional[int] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
assert torch.allclose(__snake_case , output + 2 , atol=1E-5 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =ModelForTest()
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Dict =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__magic_name__ : Any =True
__magic_name__ : Any =test_model(__snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[Any] =model(__snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) )
__magic_name__ : int =torch.randn(2 , 3 ).to(0 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : Union[str, Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
__magic_name__ : Tuple ={
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Tuple =torch.randn(2 , 3 )
__magic_name__ : int =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : str =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Optional[int] =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Any =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 21 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """autoformer"""
UpperCamelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self :List[Any] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :str = "student_t" , __snake_case :str = "nll" , __snake_case :int = 1 , __snake_case :List[int] = [1, 2, 3, 4, 5, 6, 7] , __snake_case :bool = True , __snake_case :int = 0 , __snake_case :int = 0 , __snake_case :int = 0 , __snake_case :int = 0 , __snake_case :Optional[List[int]] = None , __snake_case :Optional[List[int]] = None , __snake_case :int = 64 , __snake_case :int = 2 , __snake_case :int = 2 , __snake_case :int = 2 , __snake_case :int = 2 , __snake_case :int = 32 , __snake_case :int = 32 , __snake_case :str = "gelu" , __snake_case :float = 0.1 , __snake_case :float = 0.1 , __snake_case :float = 0.1 , __snake_case :float = 0.1 , __snake_case :float = 0.1 , __snake_case :int = 1_00 , __snake_case :float = 0.02 , __snake_case :bool = True , __snake_case :List[Any]=True , __snake_case :int = 10 , __snake_case :int = 25 , __snake_case :int = 3 , **__snake_case :int , ):
'''simple docstring'''
__magic_name__ : Tuple =prediction_length
__magic_name__ : Any =context_length if context_length is not None else prediction_length
__magic_name__ : Union[str, Any] =distribution_output
__magic_name__ : int =loss
__magic_name__ : Dict =input_size
__magic_name__ : int =num_time_features
__magic_name__ : List[Any] =lags_sequence
__magic_name__ : Optional[int] =scaling
__magic_name__ : Optional[int] =num_dynamic_real_features
__magic_name__ : str =num_static_real_features
__magic_name__ : Optional[Any] =num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(__snake_case ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
__magic_name__ : List[Any] =cardinality
else:
__magic_name__ : Optional[int] =[0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(__snake_case ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
__magic_name__ : Any =embedding_dimension
else:
__magic_name__ : List[str] =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
__magic_name__ : Any =num_parallel_samples
# Transformer architecture configuration
__magic_name__ : Union[str, Any] =input_size * len(self.lags_sequence ) + self._number_of_features
__magic_name__ : str =d_model
__magic_name__ : Optional[int] =encoder_attention_heads
__magic_name__ : Tuple =decoder_attention_heads
__magic_name__ : int =encoder_ffn_dim
__magic_name__ : List[str] =decoder_ffn_dim
__magic_name__ : List[Any] =encoder_layers
__magic_name__ : Dict =decoder_layers
__magic_name__ : str =dropout
__magic_name__ : str =attention_dropout
__magic_name__ : Optional[int] =activation_dropout
__magic_name__ : int =encoder_layerdrop
__magic_name__ : Optional[int] =decoder_layerdrop
__magic_name__ : List[str] =activation_function
__magic_name__ : str =init_std
__magic_name__ : Optional[int] =use_cache
# Autoformer
__magic_name__ : Any =label_length
__magic_name__ : Tuple =moving_average
__magic_name__ : Dict =autocorrelation_factor
super().__init__(is_encoder_decoder=__snake_case , **__snake_case )
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = KandinskyInpaintPipeline
UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase = False
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def A__ ( self :Dict ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return 1_00
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def A__ ( self :str ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : str =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__magic_name__ : Tuple =MultilingualCLIP(__snake_case )
__magic_name__ : Optional[int] =text_encoder.eval()
return text_encoder
@property
def A__ ( self :Dict ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[Any] ={
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case )
return model
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A__ ( self :Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs )
return model
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[str] =self.dummy_text_encoder
__magic_name__ : Optional[Any] =self.dummy_tokenizer
__magic_name__ : Optional[Any] =self.dummy_unet
__magic_name__ : Tuple =self.dummy_movq
__magic_name__ : List[str] =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , )
__magic_name__ : str ={
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case )
# create init_image
__magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa )
__magic_name__ : Any =0
if str(__snake_case ).startswith("""mps""" ):
__magic_name__ : Dict =torch.manual_seed(__snake_case )
else:
__magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__magic_name__ : List[Any] ={
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple ="""cpu"""
__magic_name__ : List[Any] =self.get_dummy_components()
__magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case )
__magic_name__ : Tuple =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) )
__magic_name__ : List[Any] =output.images
__magic_name__ : Any =pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
__magic_name__ : int =image[0, -3:, -3:, -1]
__magic_name__ : str =image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Optional[Any] =np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def A__ ( self :Dict ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[str] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__magic_name__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa )
__magic_name__ : Any =0
__magic_name__ : int ="""a hat"""
__magic_name__ : int =KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
__magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__magic_name__ : int =pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 )
__magic_name__ , __magic_name__ : Dict =pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__magic_name__ : Optional[Any] =pipeline(
__snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , )
__magic_name__ : Optional[int] =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 21 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
UpperCAmelCase_ : Optional[Any] = logging.getLogger(__name__)
UpperCAmelCase_ : Union[str, Any] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase_ : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __A :
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__ )} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
UpperCamelCase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"""--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )
@dataclass
class __A :
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """The input training data file (a text file)."""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
UpperCamelCase = field(
default=5 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated. Default to the max input length of the model."""
)
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
UpperCamelCase = field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
def A__ ( self :Optional[int] ):
'''simple docstring'''
if self.train_file is not None:
__magic_name__ : Dict =self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
__magic_name__ : Union[str, Any] =self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase , """r""" , encoding="""utf-8""" ) as f:
__magic_name__ : Any =[json.loads(lowerCamelCase ) for line in f.read().splitlines() if (len(lowerCamelCase ) > 0 and not line.isspace())]
assert len(lowerCamelCase ) == len(lowerCamelCase )
__magic_name__ : Optional[Any] ={c: dataset[c] for c in dataset.column_names}
__magic_name__ : List[str] =refs
return Dataset.from_dict(lowerCamelCase )
def lowerCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__magic_name__ : str =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__magic_name__ , __magic_name__ , __magic_name__ : int =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__magic_name__ : List[str] =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__magic_name__ : Optional[int] =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ : Union[str, Any] =load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
__magic_name__ : int =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"train[:{data_args.validation_split_percentage}%]" , )
__magic_name__ : Optional[Any] =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"train[{data_args.validation_split_percentage}%:]" , )
else:
__magic_name__ : str ={}
if data_args.train_file is not None:
__magic_name__ : List[Any] =data_args.train_file
if data_args.validation_file is not None:
__magic_name__ : str =data_args.validation_file
__magic_name__ : List[Any] =data_args.train_file.split(""".""" )[-1]
if extension == "txt":
__magic_name__ : List[str] ="""text"""
__magic_name__ : Optional[Any] =load_dataset(lowerCamelCase , data_files=lowerCamelCase )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__magic_name__ : List[Any] ={
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
__magic_name__ : Tuple =AutoConfig.from_pretrained(model_args.config_name , **lowerCamelCase )
elif model_args.model_name_or_path:
__magic_name__ : int =AutoConfig.from_pretrained(model_args.model_name_or_path , **lowerCamelCase )
else:
__magic_name__ : Any =CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
__magic_name__ : Any ={
"""cache_dir""": model_args.cache_dir,
"""use_fast""": model_args.use_fast_tokenizer,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
__magic_name__ : List[str] =AutoTokenizer.from_pretrained(model_args.tokenizer_name , **lowerCamelCase )
elif model_args.model_name_or_path:
__magic_name__ : Dict =AutoTokenizer.from_pretrained(model_args.model_name_or_path , **lowerCamelCase )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )
if model_args.model_name_or_path:
__magic_name__ : List[str] =AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
__magic_name__ : List[str] =AutoModelForMaskedLM.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
__magic_name__ : Union[str, Any] =datasets["""train"""].column_names
else:
__magic_name__ : List[str] =datasets["""validation"""].column_names
__magic_name__ : Optional[Any] ="""text""" if """text""" in column_names else column_names[0]
__magic_name__ : Union[str, Any] ="""max_length""" if data_args.pad_to_max_length else False
def tokenize_function(lowerCamelCase ):
# Remove empty lines
__magic_name__ : Tuple =[line for line in examples["""text"""] if len(lowerCamelCase ) > 0 and not line.isspace()]
return tokenizer(examples["""text"""] , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=data_args.max_seq_length )
__magic_name__ : List[Any] =datasets.map(
lowerCamelCase , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
__magic_name__ : str =add_chinese_references(tokenized_datasets["""train"""] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
__magic_name__ : List[Any] =add_chinese_references(
tokenized_datasets["""validation"""] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
__magic_name__ : str =data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
__magic_name__ : Optional[Any] =False
# Data collator
# This one will take care of randomly masking the tokens.
__magic_name__ : Any =DataCollatorForWholeWordMask(tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__magic_name__ : Tuple =Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None , eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None , tokenizer=lowerCamelCase , data_collator=lowerCamelCase , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__magic_name__ : str =last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
__magic_name__ : Union[str, Any] =model_args.model_name_or_path
else:
__magic_name__ : int =None
__magic_name__ : Dict =trainer.train(resume_from_checkpoint=lowerCamelCase )
trainer.save_model() # Saves the tokenizer too for easy upload
__magic_name__ : List[str] =os.path.join(training_args.output_dir , """train_results.txt""" )
if trainer.is_world_process_zero():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Train results *****""" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# Evaluation
__magic_name__ : Dict ={}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__magic_name__ : Tuple =trainer.evaluate()
__magic_name__ : Optional[int] =math.exp(eval_output["""eval_loss"""] )
__magic_name__ : List[Any] =perplexity
__magic_name__ : Optional[int] =os.path.join(training_args.output_dir , """eval_results_mlm_wwm.txt""" )
if trainer.is_world_process_zero():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in sorted(results.items() ):
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
return results
def lowerCAmelCase_ ( lowerCamelCase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 21 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __A :
def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ):
'''simple docstring'''
__magic_name__ : Optional[int] =parent
__magic_name__ : List[Any] =batch_size
__magic_name__ : List[str] =is_training
__magic_name__ : List[str] =use_auxiliary_loss
__magic_name__ : Union[str, Any] =num_queries
__magic_name__ : str =num_channels
__magic_name__ : Union[str, Any] =min_size
__magic_name__ : Union[str, Any] =max_size
__magic_name__ : Optional[int] =num_labels
__magic_name__ : Tuple =hidden_dim
__magic_name__ : Any =hidden_dim
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
__magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
__magic_name__ : List[str] =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
__magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
__magic_name__ : str =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__magic_name__ : str =self.num_queries
__magic_name__ : Dict =self.num_labels
__magic_name__ : int =[1, 1, 1, 1]
__magic_name__ : List[str] =self.num_channels
__magic_name__ : str =64
__magic_name__ : List[str] =1_28
__magic_name__ : Optional[Any] =self.hidden_dim
__magic_name__ : Tuple =self.hidden_dim
__magic_name__ : Optional[int] =self.hidden_dim
return config
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =output.encoder_hidden_states
__magic_name__ : List[str] =output.pixel_decoder_hidden_states
__magic_name__ : int =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_layers )
def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ):
'''simple docstring'''
with torch.no_grad():
__magic_name__ : List[str] =MaskaFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__snake_case , __snake_case )
def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case :List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : List[str] =model(__snake_case )
comm_check_on_output(__snake_case )
__magic_name__ : Any =model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Any =MaskaFormerModelTester(self )
__magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def A__ ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def A__ ( self :int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple =model_class(__snake_case )
__magic_name__ : Optional[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple =[*signature.parameters.keys()]
__magic_name__ : Optional[Any] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =(self.model_tester.min_size,) * 2
__magic_name__ : Union[str, Any] ={
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
__magic_name__ : Optional[Any] =self.model_tester.get_config()
__magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case )
__magic_name__ : Any =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case )
__magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def A__ ( self :int ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ : List[Any] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Dict =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Tuple =True
__magic_name__ : Optional[int] =True
__magic_name__ : int =model_class(__snake_case ).to(__snake_case )
model.train()
__magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
__magic_name__ : Optional[int] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__magic_name__ : Optional[int] =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase_ : Dict = 1e-4
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case )
__magic_name__ : int =self.default_image_processor
__magic_name__ : List[Any] =prepare_img()
__magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Dict =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : List[str] =model(**__snake_case )
__magic_name__ : Any =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Dict =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Any =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Optional[int] =self.default_image_processor
__magic_name__ : Tuple =prepare_img()
__magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
# masks_queries_logits
__magic_name__ : List[Any] =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__magic_name__ : List[Any] =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
__magic_name__ : Any =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__magic_name__ : int =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Any =self.default_image_processor
__magic_name__ : Union[str, Any] =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
__magic_name__ : str =inputs["""pixel_values"""].to(__snake_case )
__magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]]
__magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__magic_name__ : Dict =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 21 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = ["""input_features""", """is_longer"""]
def __init__( self :Dict , __snake_case :Union[str, Any]=64 , __snake_case :Optional[Any]=4_80_00 , __snake_case :Union[str, Any]=4_80 , __snake_case :int=10 , __snake_case :Union[str, Any]=10_24 , __snake_case :Optional[Any]=0.0 , __snake_case :Union[str, Any]=False , __snake_case :float = 0 , __snake_case :float = 1_40_00 , __snake_case :int = None , __snake_case :str = "fusion" , __snake_case :str = "repeatpad" , **__snake_case :List[str] , ):
'''simple docstring'''
super().__init__(
feature_size=__snake_case , sampling_rate=__snake_case , padding_value=__snake_case , return_attention_mask=__snake_case , **__snake_case , )
__magic_name__ : Tuple =top_db
__magic_name__ : List[str] =truncation
__magic_name__ : Tuple =padding
__magic_name__ : List[str] =fft_window_size
__magic_name__ : Dict =(fft_window_size >> 1) + 1
__magic_name__ : Any =hop_length
__magic_name__ : Optional[Any] =max_length_s
__magic_name__ : List[Any] =max_length_s * sampling_rate
__magic_name__ : Dict =sampling_rate
__magic_name__ : int =frequency_min
__magic_name__ : int =frequency_max
__magic_name__ : Dict =mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__snake_case , min_frequency=__snake_case , max_frequency=__snake_case , sampling_rate=__snake_case , norm=__snake_case , mel_scale="""htk""" , )
__magic_name__ : List[Any] =mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__snake_case , min_frequency=__snake_case , max_frequency=__snake_case , sampling_rate=__snake_case , norm="""slaney""" , mel_scale="""slaney""" , )
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Optional[int] =copy.deepcopy(self.__dict__ )
__magic_name__ : Union[str, Any] =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def A__ ( self :Union[str, Any] , __snake_case :np.array , __snake_case :Optional[np.array] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =spectrogram(
__snake_case , window_function(self.fft_window_size , """hann""" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=__snake_case , log_mel="""dB""" , )
return log_mel_spectrogram.T
def A__ ( self :str , __snake_case :int , __snake_case :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
__magic_name__ : str =np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
__magic_name__ : int =[0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
__magic_name__ : Dict =[0]
# randomly choose index for each part
__magic_name__ : Tuple =np.random.choice(ranges[0] )
__magic_name__ : int =np.random.choice(ranges[1] )
__magic_name__ : Optional[int] =np.random.choice(ranges[2] )
__magic_name__ : int =mel[idx_front : idx_front + chunk_frames, :]
__magic_name__ : List[Any] =mel[idx_middle : idx_middle + chunk_frames, :]
__magic_name__ : List[str] =mel[idx_back : idx_back + chunk_frames, :]
__magic_name__ : Dict =torch.tensor(mel[None, None, :] )
__magic_name__ : List[str] =torch.nn.functional.interpolate(
__snake_case , size=[chunk_frames, 64] , mode="""bilinear""" , align_corners=__snake_case )
__magic_name__ : Dict =mel_shrink[0][0].numpy()
__magic_name__ : Optional[Any] =np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def A__ ( self :str , __snake_case :np.array , __snake_case :Any , __snake_case :Union[str, Any] , __snake_case :Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__magic_name__ : Dict =True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__magic_name__ : Any =len(__snake_case ) - max_length
__magic_name__ : Any =np.random.randint(0 , overflow + 1 )
__magic_name__ : Union[str, Any] =waveform[idx : idx + max_length]
__magic_name__ : Any =self._np_extract_fbank_features(__snake_case , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
__magic_name__ : Any =self._np_extract_fbank_features(__snake_case , self.mel_filters )
__magic_name__ : Dict =max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__magic_name__ : Union[str, Any] =mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__magic_name__ : List[Any] =np.stack([mel, mel, mel, mel] , axis=0 )
__magic_name__ : int =False
else:
__magic_name__ : Any =self._random_mel_fusion(__snake_case , __snake_case , __snake_case )
__magic_name__ : List[str] =True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
__magic_name__ : Dict =False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__magic_name__ : List[Any] =int(max_length / len(__snake_case ) )
__magic_name__ : List[str] =np.stack(np.tile(__snake_case , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
__magic_name__ : str =int(max_length / len(__snake_case ) )
__magic_name__ : List[str] =np.stack(np.tile(__snake_case , __snake_case ) )
__magic_name__ : Optional[Any] =np.pad(__snake_case , (0, max_length - waveform.shape[0]) , mode="""constant""" , constant_values=0 )
if truncation == "fusion":
__magic_name__ : Any =self._np_extract_fbank_features(__snake_case , self.mel_filters )
__magic_name__ : Tuple =np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
__magic_name__ : Optional[Any] =self._np_extract_fbank_features(__snake_case , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self :Optional[Any] , __snake_case :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __snake_case :str = None , __snake_case :Optional[str] = None , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[Union[str, TensorType]] = None , **__snake_case :List[Any] , ):
'''simple docstring'''
__magic_name__ : Any =truncation if truncation is not None else self.truncation
__magic_name__ : str =padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
f" was sampled with {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
__magic_name__ : Tuple =isinstance(__snake_case , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
__magic_name__ : List[Any] =is_batched_numpy or (
isinstance(__snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__magic_name__ : Optional[Any] =[np.asarray(__snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__snake_case , np.ndarray ):
__magic_name__ : Optional[int] =np.asarray(__snake_case , dtype=np.floataa )
elif isinstance(__snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__magic_name__ : Any =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__magic_name__ : Tuple =[np.asarray(__snake_case )]
# convert to mel spectrogram, truncate and pad if needed.
__magic_name__ : List[Any] =[
self._get_input_mel(__snake_case , max_length if max_length else self.nb_max_samples , __snake_case , __snake_case )
for waveform in raw_speech
]
__magic_name__ : Optional[int] =[]
__magic_name__ : str =[]
for mel, longer in padded_inputs:
input_mel.append(__snake_case )
is_longer.append(__snake_case )
if truncation == "fusion" and sum(__snake_case ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__magic_name__ : List[Any] =np.random.randint(0 , len(__snake_case ) )
__magic_name__ : List[str] =True
if isinstance(input_mel[0] , __snake_case ):
__magic_name__ : Dict =[np.asarray(__snake_case , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
__magic_name__ : Tuple =[[longer] for longer in is_longer]
__magic_name__ : Tuple ={"""input_features""": input_mel, """is_longer""": is_longer}
__magic_name__ : str =BatchFeature(__snake_case )
if return_tensors is not None:
__magic_name__ : Union[str, Any] =input_features.convert_to_tensors(__snake_case )
return input_features
| 21 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """segformer"""
def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(**__snake_case )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , )
__magic_name__ : Dict =num_channels
__magic_name__ : str =num_encoder_blocks
__magic_name__ : List[Any] =depths
__magic_name__ : Optional[Any] =sr_ratios
__magic_name__ : List[str] =hidden_sizes
__magic_name__ : List[str] =patch_sizes
__magic_name__ : Any =strides
__magic_name__ : Optional[Any] =mlp_ratios
__magic_name__ : str =num_attention_heads
__magic_name__ : int =hidden_act
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : Optional[Any] =attention_probs_dropout_prob
__magic_name__ : Optional[Any] =classifier_dropout_prob
__magic_name__ : List[str] =initializer_range
__magic_name__ : List[str] =drop_path_rate
__magic_name__ : List[Any] =layer_norm_eps
__magic_name__ : List[str] =decoder_hidden_size
__magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case )
__magic_name__ : Dict =semantic_loss_ignore_index
class __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :Any ):
'''simple docstring'''
return 1E-4
@property
def A__ ( self :int ):
'''simple docstring'''
return 12
| 21 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase_ ( lowerCamelCase ):
return (data["data"], data["target"])
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[Any] =XGBClassifier()
classifier.fit(lowerCamelCase , lowerCamelCase )
return classifier
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =load_iris()
__magic_name__ , __magic_name__ : Union[str, Any] =data_handling(lowerCamelCase )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =train_test_split(
lowerCamelCase , lowerCamelCase , test_size=0.2_5 )
__magic_name__ : Any =iris["""target_names"""]
# Create an XGBoost Classifier from the training data
__magic_name__ : Union[str, Any] =xgboost(lowerCamelCase , lowerCamelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , display_labels=lowerCamelCase , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 21 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
__magic_name__ : Tuple =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0]
chosen_vertices.add(lowerCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__magic_name__ : Tuple =elem[1][1].index(lowerCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 21 | 1 |
from scipy.stats import spearmanr
import datasets
UpperCAmelCase_ : Optional[Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"
UpperCAmelCase_ : Tuple = "\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {'spearmanr': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results['spearmanr'])\n -0.7\n >>> print(round(results['spearmanr_pvalue'], 2))\n 0.19\n"
UpperCAmelCase_ : List[Any] = R"\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""] , )
def A__ ( self :Union[str, Any] , __snake_case :str , __snake_case :Optional[int] , __snake_case :str=False ):
'''simple docstring'''
__magic_name__ : Any =spearmanr(__snake_case , __snake_case )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 21 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) )
__magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) )
__magic_name__ , __magic_name__ : Tuple =0, 0
__magic_name__ : Optional[Any] =n - i
__magic_name__ : Union[str, Any] =memo.get(lowerCamelCase )
if sub_memo is not None:
__magic_name__ : int =sub_memo.get(lowerCamelCase )
if jumps is not None and len(lowerCamelCase ) > 0:
# find and make the largest jump without going over
__magic_name__ : Dict =-1
for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__magic_name__ : Optional[Any] =_k
break
if max_jump >= 0:
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump]
# since the difference between jumps is cached, add c
__magic_name__ : Tuple =diff + c
for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ):
__magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 )
if new_c > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__magic_name__ : str =[]
else:
__magic_name__ : List[str] ={c: []}
__magic_name__ : List[str] =sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
__magic_name__ : Tuple =sub_memo[c]
# keep jumps sorted by # of terms skipped
__magic_name__ : List[Any] =0
while j < len(lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if i >= n:
return 0, i
if k > len(lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__magic_name__ : Tuple =i
__magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0
for j in range(len(lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__magic_name__ : Optional[Any] =ds_c + ds_b
diff += addend
__magic_name__ : str =0
for j in range(lowerCamelCase ):
__magic_name__ : int =a_i[j] + addend
__magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return diff, i - start_i
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for j in range(lowerCamelCase , len(lowerCamelCase ) ):
__magic_name__ : Tuple =digits[j] + addend
if s >= 10:
__magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 )
__magic_name__ : int =addend // 10 + quotient
else:
__magic_name__ : Dict =s
__magic_name__ : Any =addend // 10
if addend == 0:
break
while addend > 0:
__magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 )
digits.append(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase = 10**15 ):
__magic_name__ : List[str] =[1]
__magic_name__ : str =1
__magic_name__ : str =0
while True:
__magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
__magic_name__ : int =0
for j in range(len(lowerCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 21 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple =[False] * len(lowerCamelCase )
__magic_name__ : Any =[-1] * len(lowerCamelCase )
def dfs(lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =True
__magic_name__ : Optional[int] =c
for u in graph[v]:
if not visited[u]:
dfs(lowerCamelCase , 1 - c )
for i in range(len(lowerCamelCase ) ):
if not visited[i]:
dfs(lowerCamelCase , 0 )
for i in range(len(lowerCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
UpperCAmelCase_ : List[str] = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase_ : List[str] = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
UpperCAmelCase_ : int = {"allegro/herbert-base-cased": 514}
UpperCAmelCase_ : int = {}
class __A ( UpperCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = HerbertTokenizer
def __init__( self :int , __snake_case :Any=None , __snake_case :str=None , __snake_case :Any=None , __snake_case :Any="<s>" , __snake_case :List[Any]="<unk>" , __snake_case :str="<pad>" , __snake_case :List[str]="<mask>" , __snake_case :Tuple="</s>" , **__snake_case :Union[str, Any] , ):
'''simple docstring'''
super().__init__(
__snake_case , __snake_case , tokenizer_file=__snake_case , cls_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sep_token=__snake_case , **__snake_case , )
def A__ ( self :str , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =[self.cls_token_id]
__magic_name__ : Optional[Any] =[self.sep_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 A__ ( self :Optional[int] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None , __snake_case :bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is None:
return [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case )) + [1]
def A__ ( self :List[str] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =[self.sep_token_id]
__magic_name__ : Tuple =[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 A__ ( self :Union[str, Any] , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
__magic_name__ : List[Any] =self._tokenizer.model.save(__snake_case , name=__snake_case )
return tuple(__snake_case )
| 21 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase_ : List[str] = 500000
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = os.path.split(__file__)
UpperCAmelCase_ : Any = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def lowerCAmelCase_ ( lowerCamelCase , **lowerCamelCase ):
__magic_name__ : Tuple =dataset.map(**lowerCamelCase )
@get_duration
def lowerCAmelCase_ ( lowerCamelCase , **lowerCamelCase ):
__magic_name__ : List[str] =dataset.filter(**lowerCamelCase )
def lowerCAmelCase_ ( ):
__magic_name__ : Optional[int] ={"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
__magic_name__ : str =datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
__magic_name__ : str =generate_example_dataset(
os.path.join(lowerCamelCase , """dataset.arrow""" ) , lowerCamelCase , num_examples=lowerCamelCase )
__magic_name__ : Dict =transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=lowerCamelCase )
def tokenize(lowerCamelCase ):
return tokenizer(examples["""text"""] )
__magic_name__ : Union[str, Any] =map(lowerCamelCase )
__magic_name__ : List[Any] =map(lowerCamelCase , batched=lowerCamelCase )
__magic_name__ : Optional[int] =map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type="""numpy""" ):
__magic_name__ : Optional[Any] =map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type="""pandas""" ):
__magic_name__ : int =map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
__magic_name__ : List[Any] =map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
__magic_name__ : Optional[Any] =map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
__magic_name__ : Optional[Any] =map(lowerCamelCase , function=lowerCamelCase , batched=lowerCamelCase )
__magic_name__ : Optional[int] =filter(lowerCamelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowerCamelCase , """wb""" ) as f:
f.write(json.dumps(lowerCamelCase ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __A ( unittest.TestCase ):
def __init__( self :Optional[int] , __snake_case :Dict , __snake_case :Union[str, Any]=13 , __snake_case :Dict=7 , __snake_case :Dict=True , __snake_case :Dict=True , __snake_case :Union[str, Any]=True , __snake_case :Any=True , __snake_case :List[Any]=99 , __snake_case :Dict=32 , __snake_case :Union[str, Any]=5 , __snake_case :List[Any]=4 , __snake_case :Optional[Any]=37 , __snake_case :List[str]="gelu" , __snake_case :str=0.1 , __snake_case :Tuple=0.1 , __snake_case :Optional[Any]=5_12 , __snake_case :Union[str, Any]=16 , __snake_case :Optional[Any]=2 , __snake_case :Optional[int]=0.02 , __snake_case :Optional[int]=4 , ):
'''simple docstring'''
__magic_name__ : str =parent
__magic_name__ : Optional[int] =batch_size
__magic_name__ : List[Any] =seq_length
__magic_name__ : List[str] =is_training
__magic_name__ : List[Any] =use_attention_mask
__magic_name__ : List[str] =use_token_type_ids
__magic_name__ : Any =use_labels
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Tuple =num_hidden_layers
__magic_name__ : Optional[Any] =num_attention_heads
__magic_name__ : Tuple =intermediate_size
__magic_name__ : Optional[Any] =hidden_act
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : List[str] =attention_probs_dropout_prob
__magic_name__ : List[str] =max_position_embeddings
__magic_name__ : Tuple =type_vocab_size
__magic_name__ : Optional[int] =type_sequence_label_size
__magic_name__ : Any =initializer_range
__magic_name__ : Any =num_choices
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict =None
if self.use_attention_mask:
__magic_name__ : Optional[int] =random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str =None
if self.use_token_type_ids:
__magic_name__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : Dict =RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : int =self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict =config_and_inputs
__magic_name__ : Optional[int] ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict =config_and_inputs
__magic_name__ : int =True
__magic_name__ : int =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : int =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[int] =FlaxRobertaPreLayerNormModelTester(self )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__magic_name__ : List[str] =model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=__snake_case )
__magic_name__ : Any =model(np.ones((1, 1) ) )
self.assertIsNotNone(__snake_case )
@require_flax
class __A ( unittest.TestCase ):
@slow
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=__snake_case )
__magic_name__ : int =np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] , dtype=jnp.intaa )
__magic_name__ : Optional[int] =model(__snake_case )[0]
__magic_name__ : str =[1, 11, 5_02_65]
self.assertEqual(list(output.shape ) , __snake_case )
# compare the actual values for a slice.
__magic_name__ : List[Any] =np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
@slow
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[int] =FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=__snake_case )
__magic_name__ : int =np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] , dtype=jnp.intaa )
__magic_name__ : List[Any] =model(__snake_case )[0]
# compare the actual values for a slice.
__magic_name__ : List[Any] =np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
| 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__magic_name__ : Dict =F"{src_lang}-{tgt_lang}"
print(F"Converting {dataset}-{pair}" )
__magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__magic_name__ : Optional[int] =F"{dataset}-{pair}"
__magic_name__ : int =Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
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
__magic_name__ : Dict ="""val""" if split == """validation""" else split
__magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" )
__magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" )
__magic_name__ : Optional[Any] =src_path.open("""w+""" )
__magic_name__ : List[Any] =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] ):
__magic_name__ : str =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)
| 21 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = """swin"""
UpperCamelCase = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self :List[Any] , __snake_case :Optional[int]=2_24 , __snake_case :str=4 , __snake_case :Tuple=3 , __snake_case :Any=96 , __snake_case :Any=[2, 2, 6, 2] , __snake_case :Union[str, Any]=[3, 6, 12, 24] , __snake_case :Optional[int]=7 , __snake_case :str=4.0 , __snake_case :Optional[Any]=True , __snake_case :Any=0.0 , __snake_case :int=0.0 , __snake_case :int=0.1 , __snake_case :Optional[int]="gelu" , __snake_case :Any=False , __snake_case :Union[str, Any]=0.02 , __snake_case :Dict=1E-5 , __snake_case :Dict=32 , __snake_case :Any=None , __snake_case :List[str]=None , **__snake_case :Union[str, Any] , ):
'''simple docstring'''
super().__init__(**__snake_case )
__magic_name__ : List[Any] =image_size
__magic_name__ : Dict =patch_size
__magic_name__ : Tuple =num_channels
__magic_name__ : Union[str, Any] =embed_dim
__magic_name__ : Union[str, Any] =depths
__magic_name__ : Optional[int] =len(__snake_case )
__magic_name__ : Any =num_heads
__magic_name__ : List[str] =window_size
__magic_name__ : List[Any] =mlp_ratio
__magic_name__ : Optional[int] =qkv_bias
__magic_name__ : Dict =hidden_dropout_prob
__magic_name__ : str =attention_probs_dropout_prob
__magic_name__ : List[Any] =drop_path_rate
__magic_name__ : Tuple =hidden_act
__magic_name__ : Tuple =use_absolute_embeddings
__magic_name__ : int =layer_norm_eps
__magic_name__ : Optional[int] =initializer_range
__magic_name__ : List[Any] =encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__magic_name__ : Union[str, Any] =int(embed_dim * 2 ** (len(__snake_case ) - 1) )
__magic_name__ : Tuple =["""stem"""] + [f"stage{idx}" for idx in range(1 , len(__snake_case ) + 1 )]
__magic_name__ , __magic_name__ : Tuple =get_aligned_output_features_output_indices(
out_features=__snake_case , out_indices=__snake_case , stage_names=self.stage_names )
class __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return 1E-4
| 21 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase = 10 ):
if not isinstance(lowerCamelCase , lowerCamelCase ) or n < 0:
raise ValueError("""Invalid input""" )
__magic_name__ : List[Any] =10**n
__magic_name__ : List[str] =28433 * (pow(2 , 7830457 , lowerCamelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 21 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( UpperCamelCase__ ):
UpperCamelCase = (PNDMScheduler,)
UpperCamelCase = (("""num_inference_steps""", 50),)
def A__ ( self :str , **__snake_case :Tuple ):
'''simple docstring'''
__magic_name__ : int ={
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**__snake_case )
return config
def A__ ( self :List[Any] , __snake_case :Union[str, Any]=0 , **__snake_case :Optional[int] ):
'''simple docstring'''
__magic_name__ : Any =dict(self.forward_default_kwargs )
__magic_name__ : str =kwargs.pop("""num_inference_steps""" , __snake_case )
__magic_name__ : int =self.dummy_sample
__magic_name__ : Dict =0.1 * sample
__magic_name__ : Tuple =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__magic_name__ : Optional[int] =self.get_scheduler_config(**__snake_case )
__magic_name__ : Dict =scheduler_class(**__snake_case )
scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals
__magic_name__ : Tuple =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__snake_case )
__magic_name__ : int =scheduler_class.from_pretrained(__snake_case )
new_scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals
__magic_name__ : Union[str, Any] =dummy_past_residuals[:]
__magic_name__ : int =scheduler.step_prk(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
__magic_name__ : Tuple =new_scheduler.step_prk(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__magic_name__ : Tuple =scheduler.step_plms(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
__magic_name__ : str =new_scheduler.step_plms(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A__ ( self :Tuple ):
'''simple docstring'''
pass
def A__ ( self :Dict , __snake_case :Optional[int]=0 , **__snake_case :List[Any] ):
'''simple docstring'''
__magic_name__ : List[str] =dict(self.forward_default_kwargs )
__magic_name__ : Optional[int] =kwargs.pop("""num_inference_steps""" , __snake_case )
__magic_name__ : List[Any] =self.dummy_sample
__magic_name__ : Optional[Any] =0.1 * sample
__magic_name__ : Optional[int] =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__magic_name__ : List[str] =self.get_scheduler_config()
__magic_name__ : int =scheduler_class(**__snake_case )
scheduler.set_timesteps(__snake_case )
# copy over dummy past residuals (must be after setting timesteps)
__magic_name__ : Union[str, Any] =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__snake_case )
__magic_name__ : Optional[int] =scheduler_class.from_pretrained(__snake_case )
# copy over dummy past residuals
new_scheduler.set_timesteps(__snake_case )
# copy over dummy past residual (must be after setting timesteps)
__magic_name__ : Any =dummy_past_residuals[:]
__magic_name__ : List[str] =scheduler.step_prk(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
__magic_name__ : Tuple =new_scheduler.step_prk(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__magic_name__ : Any =scheduler.step_plms(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
__magic_name__ : List[Any] =new_scheduler.step_plms(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A__ ( self :Any , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.scheduler_classes[0]
__magic_name__ : Dict =self.get_scheduler_config(**__snake_case )
__magic_name__ : Tuple =scheduler_class(**__snake_case )
__magic_name__ : int =10
__magic_name__ : Tuple =self.dummy_model()
__magic_name__ : Dict =self.dummy_sample_deter
scheduler.set_timesteps(__snake_case )
for i, t in enumerate(scheduler.prk_timesteps ):
__magic_name__ : Optional[int] =model(__snake_case , __snake_case )
__magic_name__ : List[str] =scheduler.step_prk(__snake_case , __snake_case , __snake_case ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
__magic_name__ : Optional[Any] =model(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =scheduler.step_plms(__snake_case , __snake_case , __snake_case ).prev_sample
return sample
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Optional[Any] =dict(self.forward_default_kwargs )
__magic_name__ : Tuple =kwargs.pop("""num_inference_steps""" , __snake_case )
for scheduler_class in self.scheduler_classes:
__magic_name__ : str =self.get_scheduler_config()
__magic_name__ : Dict =scheduler_class(**__snake_case )
__magic_name__ : Optional[int] =self.dummy_sample
__magic_name__ : int =0.1 * sample
if num_inference_steps is not None and hasattr(__snake_case , """set_timesteps""" ):
scheduler.set_timesteps(__snake_case )
elif num_inference_steps is not None and not hasattr(__snake_case , """set_timesteps""" ):
__magic_name__ : List[str] =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__magic_name__ : List[str] =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
__magic_name__ : int =dummy_past_residuals[:]
__magic_name__ : Union[str, Any] =scheduler.step_prk(__snake_case , 0 , __snake_case , **__snake_case ).prev_sample
__magic_name__ : int =scheduler.step_prk(__snake_case , 1 , __snake_case , **__snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__magic_name__ : List[Any] =scheduler.step_plms(__snake_case , 0 , __snake_case , **__snake_case ).prev_sample
__magic_name__ : str =scheduler.step_plms(__snake_case , 1 , __snake_case , **__snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A__ ( self :Tuple ):
'''simple docstring'''
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__snake_case )
def A__ ( self :int ):
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__snake_case )
__magic_name__ : str =self.scheduler_classes[0]
__magic_name__ : Tuple =self.get_scheduler_config(steps_offset=1 )
__magic_name__ : Tuple =scheduler_class(**__snake_case )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , )
def A__ ( self :Optional[int] ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=__snake_case , beta_end=__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__snake_case )
def A__ ( self :str ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=__snake_case )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=__snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : str =27
for scheduler_class in self.scheduler_classes:
__magic_name__ : Union[str, Any] =self.dummy_sample
__magic_name__ : str =0.1 * sample
__magic_name__ : List[str] =self.get_scheduler_config()
__magic_name__ : Optional[int] =scheduler_class(**__snake_case )
scheduler.set_timesteps(__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] ):
__magic_name__ : Dict =scheduler.step_prk(__snake_case , __snake_case , __snake_case ).prev_sample
def A__ ( self :List[Any] ):
'''simple docstring'''
with self.assertRaises(__snake_case ):
__magic_name__ : List[Any] =self.scheduler_classes[0]
__magic_name__ : Optional[Any] =self.get_scheduler_config()
__magic_name__ : List[str] =scheduler_class(**__snake_case )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Any =self.full_loop()
__magic_name__ : int =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : Optional[Any] =torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 198.1318 ) < 1E-2
assert abs(result_mean.item() - 0.2580 ) < 1E-3
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : List[Any] =self.full_loop(prediction_type="""v_prediction""" )
__magic_name__ : List[str] =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : List[str] =torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 67.3986 ) < 1E-2
assert abs(result_mean.item() - 0.0878 ) < 1E-3
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple =self.full_loop(set_alpha_to_one=__snake_case , beta_start=0.01 )
__magic_name__ : Tuple =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : List[Any] =torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 230.0399 ) < 1E-2
assert abs(result_mean.item() - 0.2995 ) < 1E-3
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Any =self.full_loop(set_alpha_to_one=__snake_case , beta_start=0.01 )
__magic_name__ : Union[str, Any] =torch.sum(torch.abs(__snake_case ) )
__magic_name__ : Any =torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 186.9482 ) < 1E-2
assert abs(result_mean.item() - 0.2434 ) < 1E-3
| 21 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : List[Any] =max_length
__magic_name__ : Dict =vocab
__magic_name__ : int =merges
__magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__magic_name__ : str =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
return cls(**__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case )
__magic_name__ : Tuple =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__magic_name__ : Tuple =max_length if max_length is not None else self.max_length
if max_length is not None:
__magic_name__ , __magic_name__ : Tuple =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 21 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCAmelCase_ : List[Any] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=DataRow("", "|", "|"),
padding=1,
with_header_hide=None,
)
UpperCAmelCase_ : Dict = []
UpperCAmelCase_ : Dict = []
UpperCAmelCase_ : str = {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}
UpperCAmelCase_ : Tuple = [
{
"type": "header",
"text": {
"type": "plain_text",
"text": F"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
"emoji": True,
},
}
]
UpperCAmelCase_ : Optional[Any] = 0
for log in Path().glob("*.log"):
UpperCAmelCase_ : Tuple = 0
with open(log, "r") as f:
for line in f:
UpperCAmelCase_ : Optional[Any] = json.loads(line)
if line.get("nodeid", "") != "":
UpperCAmelCase_ : List[Any] = line["nodeid"]
if line.get("duration", None) is not None:
UpperCAmelCase_ : Any = F"""{line["duration"]:.4f}"""
if line.get("outcome", "") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("_")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
UpperCAmelCase_ : Optional[Any] = []
log.unlink()
UpperCAmelCase_ : List[str] = ""
UpperCAmelCase_ : Any = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : List[str] = {}
for test in failed_tests:
UpperCAmelCase_ : int = test[0].split("::")
UpperCAmelCase_ : str = data[0].split("/")[-1]
if data[0] not in filesafailed:
UpperCAmelCase_ : Tuple = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
UpperCAmelCase_ : List[str] = [test[0] for test in failed_table]
UpperCAmelCase_ : int = list(set(files))
# Count number of instances in failed_tests
UpperCAmelCase_ : int = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
UpperCAmelCase_ : List[Any] = tabulate(
table,
headers=["Test Location", "Num Failed"],
tablefmt=hf_table_format,
stralign="right",
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
UpperCAmelCase_ : str = "Too many failed tests, please see the full report in the Action results."
UpperCAmelCase_ : Dict = len(err) + 10
UpperCAmelCase_ : List[Any] = message[: 3000 - offset] + F"""\n...\n```\n{err}"""
print(F"""### {message}""")
else:
UpperCAmelCase_ : str = "No failed tests! 🤗"
print(F"""## {message}""")
payload.append(no_error_payload)
if os.environ.get("TEST_TYPE", "") != "":
from slack_sdk import WebClient
UpperCAmelCase_ : str = WebClient(token=os.environ["SLACK_API_TOKEN"])
if message != "No failed tests! 🤗":
UpperCAmelCase_ : Tuple = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": message,
},
}
payload.append(md_report)
UpperCAmelCase_ : Any = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": "*For more details:*",
},
"accessory": {
"type": "button",
"text": {
"type": "plain_text",
"text": "Check Action results",
"emoji": True,
},
"url": F"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
payload.append(action_button)
UpperCAmelCase_ : Optional[int] = {
"type": "context",
"elements": [
{
"type": "plain_text",
"text": F"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
}
],
}
payload.append(date_report)
UpperCAmelCase_ : Tuple = client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
UpperCAmelCase_ : List[Any] = response.data["ts"]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
UpperCAmelCase_ : List[str] = ""
for i, row in enumerate(test_failures):
if row[0] != test_class:
UpperCAmelCase_ : str = row[0]
else:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : Dict = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
},
}
client.chat_postMessage(
channel="#accelerate-ci-daily",
thread_ts=ts,
blocks=[payload],
)
| 21 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 | 1 |
import requests
UpperCAmelCase_ : List[str] = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCAmelCase_ ( lowerCamelCase ):
# fetching a list of articles in json format
__magic_name__ : List[Any] =requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["""articles"""] , 1 ):
print(F"{i}.) {article['title']}" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 21 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ):
__magic_name__ : Any =nums[i]
__magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip())
UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 21 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def __call__( self :Union[str, Any] ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None
__magic_name__ : Optional[int] =len(self.languages ) if self.languages else None
def __call__( self :List[str] ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[int] =set(self.languages )
if self.languages and set(__snake_case ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__magic_name__ : Any =[]
for lang, text in translation_dict.items():
if isinstance(__snake_case , __snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) )
return {"language": languages, "translation": translations}
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 21 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase__ )
class __A ( UpperCamelCase__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
UpperCamelCase = field(default="""text-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
UpperCamelCase = Features({"""text""": Value("""string""" )} )
UpperCamelCase = Features({"""labels""": ClassLabel} )
UpperCamelCase = "text"
UpperCamelCase = "labels"
def A__ ( self :Any , __snake_case :Tuple ):
'''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] , __snake_case ):
raise ValueError(f"Column {self.label_column} is not a ClassLabel." )
__magic_name__ : Union[str, Any] =copy.deepcopy(self )
__magic_name__ : str =self.label_schema.copy()
__magic_name__ : str =features[self.label_column]
__magic_name__ : str =label_schema
return task_template
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 21 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
__magic_name__ : Optional[Any] =AutoTokenizer.from_pretrained("""google/mt5-small""" )
__magic_name__ : List[str] =tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
__magic_name__ : Tuple =tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
__magic_name__ : List[str] =shift_tokens_right(__snake_case , model.config.pad_token_id , model.config.decoder_start_token_id )
__magic_name__ : Tuple =model(__snake_case , decoder_input_ids=__snake_case ).logits
__magic_name__ : Union[str, Any] =optax.softmax_cross_entropy(__snake_case , onehot(__snake_case , logits.shape[-1] ) ).mean()
__magic_name__ : Union[str, Any] =-(labels.shape[-1] * loss.item())
__magic_name__ : List[Any] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
return config
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ):
if conf_path is None:
__magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml"""
__magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase )
__magic_name__ : Tuple =VQModel(**config.model.params )
if ckpt_path is None:
__magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt"""
__magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase )
if ".ckpt" in ckpt_path:
__magic_name__ : Any =sd["""state_dict"""]
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
model.to(lowerCamelCase )
del sd
return model
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__magic_name__ : List[Any] =model.decode(lowerCamelCase )
return xrec
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 )
if reload:
__magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase )
importlib.reload(lowerCamelCase )
return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls )
def lowerCAmelCase_ ( lowerCamelCase ):
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ):
__magic_name__ : str =instantiate_from_config(lowerCamelCase )
if sd is not None:
model.load_state_dict(lowerCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load the specified checkpoint
if ckpt:
__magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Any =pl_sd["""global_step"""]
print(F"loaded model from global step {global_step}." )
else:
__magic_name__ : List[Any] ={"""state_dict""": None}
__magic_name__ : Optional[Any] =None
__magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""]
return model, global_step
| 21 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : int =[
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[Any] =list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
__magic_name__ : List[Any] =s_dict.pop(lowerCamelCase )
elif "subsample" in key:
__magic_name__ : Any =s_dict.pop(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ , __magic_name__ : Union[str, Any] =emb.weight.shape
__magic_name__ : int =nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
__magic_name__ : int =emb.weight.data
return lin_layer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : int =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Optional[Any] =mam_aaa["""args"""]
__magic_name__ : List[str] =mam_aaa["""model"""]
__magic_name__ : str =state_dict["""decoder.output_projection.weight"""]
remove_ignore_keys_(lowerCamelCase )
rename_keys(lowerCamelCase )
__magic_name__ : Tuple =state_dict["""decoder.embed_tokens.weight"""].shape[0]
__magic_name__ : int =args.share_decoder_input_output_embed
__magic_name__ : List[Any] =[int(lowerCamelCase ) for i in args.conv_kernel_sizes.split(""",""" )]
__magic_name__ : Union[str, Any] =SpeechaTextConfig(
vocab_size=lowerCamelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , num_conv_layers=len(lowerCamelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=lowerCamelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=lowerCamelCase , num_beams=5 , max_length=200 , use_cache=lowerCamelCase , decoder_start_token_id=2 , early_stopping=lowerCamelCase , )
__magic_name__ : List[Any] =SpeechaTextForConditionalGeneration(lowerCamelCase )
__magic_name__ , __magic_name__ : Dict =model.model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
if len(lowerCamelCase ) > 0 and not set(lowerCamelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"""Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"""
F" but all the following weights are missing {missing}" )
if tie_embeds:
__magic_name__ : Optional[int] =make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__magic_name__ : Tuple =lm_head_weights
model.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase_ : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 21 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_script.main )
def A__ ( self :Dict ):
'''simple docstring'''
debug_launcher(test_ops.main )
| 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : int = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = ["BloomTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
"BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
"BloomForCausalLM",
"BloomModel",
"BloomPreTrainedModel",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmelCase_ ( lowerCamelCase ):
if not sequence:
return []
if len(lowerCamelCase ) == 1:
return list(lowerCamelCase )
__magic_name__ : int =0
__magic_name__ : str =len(lowerCamelCase ) - 1
__magic_name__ : Optional[Any] =0
while mid <= high:
if sequence[mid] == colors[0]:
__magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
__magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid]
high -= 1
else:
__magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values"
raise ValueError(lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip()
UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
print(F"""{dutch_national_flag_sort(unsorted)}""")
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase ):
return "".join([hex(lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase )] )
def lowerCAmelCase_ ( lowerCamelCase ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(lowerCamelCase ) % 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(lowerCamelCase ) <= 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] , 16 ) for i in range(0 , len(lowerCamelCase ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = 1
@register_to_config
def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ):
'''simple docstring'''
__magic_name__ : Dict =None
__magic_name__ : List[str] =None
__magic_name__ : str =None
def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case )
def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__magic_name__ : int =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__magic_name__ : str =std.flatten()
while len(std.shape ) < len(score.shape ):
__magic_name__ : List[str] =std.unsqueeze(-1 )
__magic_name__ : Union[str, Any] =-score / std
# compute
__magic_name__ : Tuple =-1.0 / len(self.timesteps )
__magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__magic_name__ : Dict =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__magic_name__ : Any =beta_t.unsqueeze(-1 )
__magic_name__ : Dict =-0.5 * beta_t * x
__magic_name__ : Optional[int] =torch.sqrt(__snake_case )
__magic_name__ : int =drift - diffusion**2 * score
__magic_name__ : List[str] =x + drift * dt
# add noise
__magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype )
__magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self :List[Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 21 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class __A ( unittest.TestCase ):
def A__ ( self :List[str] , __snake_case :List[str] ):
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
__magic_name__ : str =model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[str] =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""sgugger/tiny-distilbert-classification"""
__magic_name__ : List[str] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , only_pretrain_model=__snake_case , )
__magic_name__ : Dict =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : str ="""sshleifer/tiny-gpt2"""
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : int =TensorFlowBenchmark(__snake_case )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : str =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Tuple =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : Dict =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Union[str, Any] =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : str =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Any =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : Tuple =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : str =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case , [config] )
__magic_name__ : int =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : List[str] ="""patrickvonplaten/t5-tiny-random"""
__magic_name__ : Optional[Any] =AutoConfig.from_pretrained(__snake_case )
__magic_name__ : str =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__snake_case , )
__magic_name__ : List[Any] =TensorFlowBenchmark(__snake_case , configs=[config] )
__magic_name__ : Union[str, Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[Any] ="""sshleifer/tiny-gpt2"""
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__snake_case , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , use_xla=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[str] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Optional[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
__magic_name__ : Tuple =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__snake_case , save_to_csv=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__snake_case , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(__snake_case , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(__snake_case , """env.csv""" ) , multi_process=__snake_case , )
__magic_name__ : Optional[Any] =TensorFlowBenchmark(__snake_case )
benchmark.run()
self.assertTrue(Path(os.path.join(__snake_case , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case , """env.csv""" ) ).exists() )
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : int ="""sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(__snake_case :Dict ):
self.assertTrue(hasattr(__snake_case , """sequential""" ) )
self.assertTrue(hasattr(__snake_case , """cumulative""" ) )
self.assertTrue(hasattr(__snake_case , """current""" ) )
self.assertTrue(hasattr(__snake_case , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__magic_name__ : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__snake_case , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__snake_case , """log.txt""" ) , log_print=__snake_case , trace_memory_line_by_line=__snake_case , eager_mode=__snake_case , multi_process=__snake_case , )
__magic_name__ : List[Any] =TensorFlowBenchmark(__snake_case )
__magic_name__ : Optional[int] =benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(__snake_case , """log.txt""" ) ).exists() )
| 21 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = ["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
def __init__( self :List[Any] ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =nn.Linear(3 , 4 )
__magic_name__ : Union[str, Any] =nn.BatchNormad(4 )
__magic_name__ : List[str] =nn.Linear(4 , 5 )
def A__ ( self :Dict , __snake_case :Tuple ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) )
class __A ( UpperCamelCase__ ):
def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ):
'''simple docstring'''
return output + 1
class __A ( unittest.TestCase ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : Tuple =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
self.assertEqual(test_model._hf_hook , __snake_case )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : List[str] =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
add_hook_to_module(__snake_case , __snake_case , append=__snake_case )
self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
__magic_name__ : Any =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(x + 1 )
__magic_name__ : Optional[Any] =test_model(x + 2 )
__magic_name__ : int =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : int =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : str =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : List[str] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
assert torch.allclose(__snake_case , __snake_case , atol=1E-5 )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
__magic_name__ : Dict =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(__snake_case )
__magic_name__ : Dict =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Optional[int] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
assert torch.allclose(__snake_case , output + 2 , atol=1E-5 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =ModelForTest()
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Dict =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__magic_name__ : Any =True
__magic_name__ : Any =test_model(__snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[Any] =model(__snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) )
__magic_name__ : int =torch.randn(2 , 3 ).to(0 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : Union[str, Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
__magic_name__ : Tuple ={
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Tuple =torch.randn(2 , 3 )
__magic_name__ : int =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : str =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Optional[int] =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Any =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 21 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A ( UpperCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A ( unittest.TestCase ):
@property
def A__ ( self :int ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : int =ort.SessionOptions()
__magic_name__ : int =False
return options
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Optional[Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__magic_name__ : Any =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__magic_name__ : Union[str, Any] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=__snake_case , feature_extractor=__snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple ="""A red cat sitting on a park bench"""
__magic_name__ : int =np.random.RandomState(0 )
__magic_name__ : List[str] =pipe(
prompt=__snake_case , image=__snake_case , mask_image=__snake_case , guidance_scale=7.5 , num_inference_steps=10 , generator=__snake_case , output_type="""np""" , )
__magic_name__ : Union[str, Any] =output.images
__magic_name__ : str =images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__magic_name__ : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__magic_name__ : Tuple =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__magic_name__ : Optional[int] =LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
__magic_name__ : Optional[Any] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=__snake_case , safety_checker=__snake_case , feature_extractor=__snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : str ="""A red cat sitting on a park bench"""
__magic_name__ : Optional[int] =np.random.RandomState(0 )
__magic_name__ : Optional[int] =pipe(
prompt=__snake_case , image=__snake_case , mask_image=__snake_case , guidance_scale=7.5 , num_inference_steps=20 , generator=__snake_case , output_type="""np""" , )
__magic_name__ : Union[str, Any] =output.images
__magic_name__ : Union[str, Any] =images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__magic_name__ : Any =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = KandinskyInpaintPipeline
UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase = False
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def A__ ( self :Dict ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return 1_00
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def A__ ( self :str ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : str =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__magic_name__ : Tuple =MultilingualCLIP(__snake_case )
__magic_name__ : Optional[int] =text_encoder.eval()
return text_encoder
@property
def A__ ( self :Dict ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[Any] ={
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case )
return model
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A__ ( self :Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs )
return model
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[str] =self.dummy_text_encoder
__magic_name__ : Optional[Any] =self.dummy_tokenizer
__magic_name__ : Optional[Any] =self.dummy_unet
__magic_name__ : Tuple =self.dummy_movq
__magic_name__ : List[str] =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , )
__magic_name__ : str ={
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case )
# create init_image
__magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa )
__magic_name__ : Any =0
if str(__snake_case ).startswith("""mps""" ):
__magic_name__ : Dict =torch.manual_seed(__snake_case )
else:
__magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__magic_name__ : List[Any] ={
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple ="""cpu"""
__magic_name__ : List[Any] =self.get_dummy_components()
__magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case )
__magic_name__ : Tuple =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) )
__magic_name__ : List[Any] =output.images
__magic_name__ : Any =pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
__magic_name__ : int =image[0, -3:, -3:, -1]
__magic_name__ : str =image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Optional[Any] =np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def A__ ( self :Dict ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[str] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__magic_name__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa )
__magic_name__ : Any =0
__magic_name__ : int ="""a hat"""
__magic_name__ : int =KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
__magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__magic_name__ : int =pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 )
__magic_name__ , __magic_name__ : Dict =pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__magic_name__ : Optional[Any] =pipeline(
__snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , )
__magic_name__ : Optional[int] =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Dict = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __A :
def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ):
'''simple docstring'''
__magic_name__ : Optional[int] =parent
__magic_name__ : List[Any] =batch_size
__magic_name__ : List[str] =is_training
__magic_name__ : List[str] =use_auxiliary_loss
__magic_name__ : Union[str, Any] =num_queries
__magic_name__ : str =num_channels
__magic_name__ : Union[str, Any] =min_size
__magic_name__ : Union[str, Any] =max_size
__magic_name__ : Optional[int] =num_labels
__magic_name__ : Tuple =hidden_dim
__magic_name__ : Any =hidden_dim
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
__magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
__magic_name__ : List[str] =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
__magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
__magic_name__ : str =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__magic_name__ : str =self.num_queries
__magic_name__ : Dict =self.num_labels
__magic_name__ : int =[1, 1, 1, 1]
__magic_name__ : List[str] =self.num_channels
__magic_name__ : str =64
__magic_name__ : List[str] =1_28
__magic_name__ : Optional[Any] =self.hidden_dim
__magic_name__ : Tuple =self.hidden_dim
__magic_name__ : Optional[int] =self.hidden_dim
return config
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =output.encoder_hidden_states
__magic_name__ : List[str] =output.pixel_decoder_hidden_states
__magic_name__ : int =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_layers )
def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ):
'''simple docstring'''
with torch.no_grad():
__magic_name__ : List[str] =MaskaFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__snake_case , __snake_case )
def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case :List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : List[str] =model(__snake_case )
comm_check_on_output(__snake_case )
__magic_name__ : Any =model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Any =MaskaFormerModelTester(self )
__magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def A__ ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def A__ ( self :int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple =model_class(__snake_case )
__magic_name__ : Optional[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple =[*signature.parameters.keys()]
__magic_name__ : Optional[Any] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =(self.model_tester.min_size,) * 2
__magic_name__ : Union[str, Any] ={
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
__magic_name__ : Optional[Any] =self.model_tester.get_config()
__magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case )
__magic_name__ : Any =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case )
__magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def A__ ( self :int ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ : List[Any] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Dict =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Tuple =True
__magic_name__ : Optional[int] =True
__magic_name__ : int =model_class(__snake_case ).to(__snake_case )
model.train()
__magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
__magic_name__ : Optional[int] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__magic_name__ : Optional[int] =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase_ : Dict = 1e-4
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case )
__magic_name__ : int =self.default_image_processor
__magic_name__ : List[Any] =prepare_img()
__magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Dict =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : List[str] =model(**__snake_case )
__magic_name__ : Any =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Dict =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Any =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Optional[int] =self.default_image_processor
__magic_name__ : Tuple =prepare_img()
__magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
# masks_queries_logits
__magic_name__ : List[Any] =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__magic_name__ : List[Any] =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
__magic_name__ : Any =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__magic_name__ : int =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Any =self.default_image_processor
__magic_name__ : Union[str, Any] =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
__magic_name__ : str =inputs["""pixel_values"""].to(__snake_case )
__magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]]
__magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__magic_name__ : Dict =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 21 | 1 |
UpperCAmelCase_ : int = "Input must be a string of 8 numbers plus letter"
UpperCAmelCase_ : Tuple = "TRWAGMYFPDXBNJZSQVHLCKE"
def lowerCAmelCase_ ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[Any] =F"Expected string as input, found {type(lowerCamelCase ).__name__}"
raise TypeError(lowerCamelCase )
__magic_name__ : str =spanish_id.replace("""-""" , """""" ).upper()
if len(lowerCamelCase ) != 9:
raise ValueError(lowerCamelCase )
try:
__magic_name__ : Union[str, Any] =int(spanish_id_clean[0:8] )
__magic_name__ : int =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(lowerCamelCase ) from ex
if letter.isdigit():
raise ValueError(lowerCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """segformer"""
def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(**__snake_case )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , )
__magic_name__ : Dict =num_channels
__magic_name__ : str =num_encoder_blocks
__magic_name__ : List[Any] =depths
__magic_name__ : Optional[Any] =sr_ratios
__magic_name__ : List[str] =hidden_sizes
__magic_name__ : List[str] =patch_sizes
__magic_name__ : Any =strides
__magic_name__ : Optional[Any] =mlp_ratios
__magic_name__ : str =num_attention_heads
__magic_name__ : int =hidden_act
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : Optional[Any] =attention_probs_dropout_prob
__magic_name__ : Optional[Any] =classifier_dropout_prob
__magic_name__ : List[str] =initializer_range
__magic_name__ : List[str] =drop_path_rate
__magic_name__ : List[Any] =layer_norm_eps
__magic_name__ : List[str] =decoder_hidden_size
__magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case )
__magic_name__ : Dict =semantic_loss_ignore_index
class __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :Any ):
'''simple docstring'''
return 1E-4
@property
def A__ ( self :int ):
'''simple docstring'''
return 12
| 21 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class __A :
UpperCamelCase = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys() )} )
UpperCamelCase = field(
metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
UpperCamelCase = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : List[Any] =self.task_name.lower()
class __A ( UpperCamelCase__ ):
UpperCamelCase = """train"""
UpperCamelCase = """dev"""
UpperCamelCase = """test"""
class __A ( UpperCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
def __init__( self :Optional[Any] , __snake_case :GlueDataTrainingArguments , __snake_case :PreTrainedTokenizerBase , __snake_case :Optional[int] = None , __snake_case :Union[str, Split] = Split.train , __snake_case :Optional[str] = None , ):
'''simple docstring'''
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , __snake_case , )
__magic_name__ : Tuple =args
__magic_name__ : List[str] =glue_processors[args.task_name]()
__magic_name__ : Optional[int] =glue_output_modes[args.task_name]
if isinstance(__snake_case , __snake_case ):
try:
__magic_name__ : List[Any] =Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__magic_name__ : Optional[Any] =os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}" , )
__magic_name__ : List[str] =self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__magic_name__ , __magic_name__ : int =label_list[2], label_list[1]
__magic_name__ : str =label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__magic_name__ : Tuple =cached_features_file + """.lock"""
with FileLock(__snake_case ):
if os.path.exists(__snake_case ) and not args.overwrite_cache:
__magic_name__ : str =time.time()
__magic_name__ : Optional[int] =torch.load(__snake_case )
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
else:
logger.info(f"Creating features from dataset file at {args.data_dir}" )
if mode == Split.dev:
__magic_name__ : Optional[Any] =self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__magic_name__ : Any =self.processor.get_test_examples(args.data_dir )
else:
__magic_name__ : int =self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__magic_name__ : Optional[int] =examples[:limit_length]
__magic_name__ : Any =glue_convert_examples_to_features(
__snake_case , __snake_case , max_length=args.max_seq_length , label_list=__snake_case , output_mode=self.output_mode , )
__magic_name__ : str =time.time()
torch.save(self.features , __snake_case )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__( self :Tuple ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :List[Any] , __snake_case :Tuple ):
'''simple docstring'''
return self.features[i]
def A__ ( self :int ):
'''simple docstring'''
return self.label_list
| 21 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
__magic_name__ : Tuple =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0]
chosen_vertices.add(lowerCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__magic_name__ : Tuple =elem[1][1].index(lowerCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 21 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = RoFormerTokenizer
UpperCamelCase = RoFormerTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def A__ ( self :List[str] ):
'''simple docstring'''
super().setUp()
def A__ ( self :List[str] , **__snake_case :str ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **__snake_case )
def A__ ( self :str , **__snake_case :Dict ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **__snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ : List[str] ="""永和服装饰品有限公司,今天天气非常好"""
__magic_name__ : Any ="""永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.get_tokenizer()
__magic_name__ , __magic_name__ : Optional[int] =self.get_chinese_input_output_texts()
__magic_name__ : Any =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , output_text.split() )
__magic_name__ : Any =tokens + [tokenizer.unk_token]
__magic_name__ : Union[str, Any] =[2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : str =self.get_rust_tokenizer()
__magic_name__ , __magic_name__ : Any =self.get_chinese_input_output_texts()
__magic_name__ : Any =tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , output_text.split() )
__magic_name__ : Optional[int] =tokens + [tokenizer.unk_token]
__magic_name__ : Any =[2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , __snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
def A__ ( self :Dict ):
'''simple docstring'''
pass
def A__ ( self :Tuple ):
'''simple docstring'''
pass
| 21 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) )
__magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) )
__magic_name__ , __magic_name__ : Tuple =0, 0
__magic_name__ : Optional[Any] =n - i
__magic_name__ : Union[str, Any] =memo.get(lowerCamelCase )
if sub_memo is not None:
__magic_name__ : int =sub_memo.get(lowerCamelCase )
if jumps is not None and len(lowerCamelCase ) > 0:
# find and make the largest jump without going over
__magic_name__ : Dict =-1
for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__magic_name__ : Optional[Any] =_k
break
if max_jump >= 0:
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump]
# since the difference between jumps is cached, add c
__magic_name__ : Tuple =diff + c
for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ):
__magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 )
if new_c > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__magic_name__ : str =[]
else:
__magic_name__ : List[str] ={c: []}
__magic_name__ : List[str] =sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
__magic_name__ : Tuple =sub_memo[c]
# keep jumps sorted by # of terms skipped
__magic_name__ : List[Any] =0
while j < len(lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if i >= n:
return 0, i
if k > len(lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__magic_name__ : Tuple =i
__magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0
for j in range(len(lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__magic_name__ : Optional[Any] =ds_c + ds_b
diff += addend
__magic_name__ : str =0
for j in range(lowerCamelCase ):
__magic_name__ : int =a_i[j] + addend
__magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return diff, i - start_i
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for j in range(lowerCamelCase , len(lowerCamelCase ) ):
__magic_name__ : Tuple =digits[j] + addend
if s >= 10:
__magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 )
__magic_name__ : int =addend // 10 + quotient
else:
__magic_name__ : Dict =s
__magic_name__ : Any =addend // 10
if addend == 0:
break
while addend > 0:
__magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 )
digits.append(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase = 10**15 ):
__magic_name__ : List[str] =[1]
__magic_name__ : str =1
__magic_name__ : str =0
while True:
__magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
__magic_name__ : int =0
for j in range(len(lowerCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 21 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
UpperCAmelCase_ : int = parser.parse_args()
UpperCAmelCase_ : Dict = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCAmelCase_ : Optional[Any] = CLIPImageProcessor()
UpperCAmelCase_ : Tuple = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
UpperCAmelCase_ : Optional[int] = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """longformer"""
def __init__( self :Optional[int] , __snake_case :Union[List[int], int] = 5_12 , __snake_case :int = 2 , __snake_case :int = 1 , __snake_case :int = 0 , __snake_case :int = 2 , __snake_case :int = 3_05_22 , __snake_case :int = 7_68 , __snake_case :int = 12 , __snake_case :int = 12 , __snake_case :int = 30_72 , __snake_case :str = "gelu" , __snake_case :float = 0.1 , __snake_case :float = 0.1 , __snake_case :int = 5_12 , __snake_case :int = 2 , __snake_case :float = 0.02 , __snake_case :float = 1E-12 , __snake_case :bool = False , **__snake_case :List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , **__snake_case )
__magic_name__ : int =attention_window
__magic_name__ : Dict =sep_token_id
__magic_name__ : Dict =bos_token_id
__magic_name__ : Tuple =eos_token_id
__magic_name__ : List[str] =vocab_size
__magic_name__ : Dict =hidden_size
__magic_name__ : Any =num_hidden_layers
__magic_name__ : Union[str, Any] =num_attention_heads
__magic_name__ : int =hidden_act
__magic_name__ : int =intermediate_size
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : List[str] =attention_probs_dropout_prob
__magic_name__ : Optional[int] =max_position_embeddings
__magic_name__ : Dict =type_vocab_size
__magic_name__ : int =initializer_range
__magic_name__ : Optional[Any] =layer_norm_eps
__magic_name__ : int =onnx_export
class __A ( UpperCamelCase__ ):
def __init__( self :Optional[Any] , __snake_case :"PretrainedConfig" , __snake_case :str = "default" , __snake_case :"List[PatchingSpec]" = None ):
'''simple docstring'''
super().__init__(__snake_case , __snake_case , __snake_case )
__magic_name__ : Optional[Any] =True
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : Dict ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : List[str] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =super().outputs
if self.task == "default":
__magic_name__ : List[str] ={0: """batch"""}
return outputs
@property
def A__ ( self :Dict ):
'''simple docstring'''
return 1E-4
@property
def A__ ( self :Optional[int] ):
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def A__ ( self :int , __snake_case :"PreTrainedTokenizerBase" , __snake_case :int = -1 , __snake_case :int = -1 , __snake_case :bool = False , __snake_case :Optional[TensorType] = None , ):
'''simple docstring'''
__magic_name__ : Dict =super().generate_dummy_inputs(
preprocessor=__snake_case , batch_size=__snake_case , seq_length=__snake_case , is_pair=__snake_case , framework=__snake_case )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
__magic_name__ : List[Any] =torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
__magic_name__ : Optional[int] =1
return inputs
| 21 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase="pt" ):
__magic_name__ : int ={"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase ) and not line.startswith(""" """ ) else {}
__magic_name__ : Dict =padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None , ):
__magic_name__ : Union[str, Any] =input_ids.ne(lowerCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __A ( UpperCamelCase__ ):
def __init__( self :Tuple , __snake_case :Tuple , __snake_case :Optional[Any] , __snake_case :Optional[int] , __snake_case :Union[str, Any] , __snake_case :Dict="train" , __snake_case :str=None , __snake_case :Union[str, Any]=None , __snake_case :Tuple=None , __snake_case :List[str]="" , ):
'''simple docstring'''
super().__init__()
__magic_name__ : List[str] =Path(__snake_case ).joinpath(type_path + """.source""" )
__magic_name__ : Tuple =Path(__snake_case ).joinpath(type_path + """.target""" )
__magic_name__ : int =self.get_char_lens(self.src_file )
__magic_name__ : Optional[Any] =max_source_length
__magic_name__ : int =max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
__magic_name__ : Any =tokenizer
__magic_name__ : str =prefix
if n_obs is not None:
__magic_name__ : Union[str, Any] =self.src_lens[:n_obs]
__magic_name__ : Dict =src_lang
__magic_name__ : List[str] =tgt_lang
def __len__( self :Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self :int , __snake_case :List[Any] ):
'''simple docstring'''
__magic_name__ : str =index + 1 # linecache starts at 1
__magic_name__ : List[Any] =self.prefix + linecache.getline(str(self.src_file ) , __snake_case ).rstrip("""\n""" )
__magic_name__ : Tuple =linecache.getline(str(self.tgt_file ) , __snake_case ).rstrip("""\n""" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer , __snake_case ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__magic_name__ : Dict =(
self.tokenizer.question_encoder if isinstance(self.tokenizer , __snake_case ) else self.tokenizer
)
__magic_name__ : Optional[Any] =self.tokenizer.generator if isinstance(self.tokenizer , __snake_case ) else self.tokenizer
__magic_name__ : Union[str, Any] =encode_line(__snake_case , __snake_case , self.max_source_length , """right""" )
__magic_name__ : str =encode_line(__snake_case , __snake_case , self.max_target_length , """right""" )
__magic_name__ : Tuple =source_inputs["""input_ids"""].squeeze()
__magic_name__ : Any =target_inputs["""input_ids"""].squeeze()
__magic_name__ : Any =source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def A__ ( __snake_case :List[str] ):
'''simple docstring'''
return [len(__snake_case ) for x in Path(__snake_case ).open().readlines()]
def A__ ( self :Dict , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : Any =torch.stack([x["""input_ids"""] for x in batch] )
__magic_name__ : Any =torch.stack([x["""attention_mask"""] for x in batch] )
__magic_name__ : Optional[Any] =torch.stack([x["""decoder_input_ids"""] for x in batch] )
__magic_name__ : Dict =(
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __snake_case )
else self.tokenizer.pad_token_id
)
__magic_name__ : Any =(
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __snake_case )
else self.tokenizer.pad_token_id
)
__magic_name__ : Tuple =trim_batch(__snake_case , __snake_case )
__magic_name__ , __magic_name__ : Dict =trim_batch(__snake_case , __snake_case , attention_mask=__snake_case )
__magic_name__ : Union[str, Any] ={
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
UpperCAmelCase_ : int = getLogger(__name__)
def lowerCAmelCase_ ( lowerCamelCase ):
return list(itertools.chain.from_iterable(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json""" ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=4 , **lowerCamelCase ):
with open(lowerCamelCase , """w""" ) as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
with open(lowerCamelCase ) as f:
return json.load(lowerCamelCase )
def lowerCAmelCase_ ( ):
__magic_name__ : str =git.Repo(search_parent_directories=lowerCamelCase )
__magic_name__ : Optional[Any] ={
"""repo_id""": str(lowerCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return list(map(lowerCamelCase , lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase , """wb""" ) as f:
return pickle.dump(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
def remove_articles(lowerCamelCase ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase )
def white_space_fix(lowerCamelCase ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase ):
__magic_name__ : Optional[int] =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase ) ) ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =normalize_answer(lowerCamelCase ).split()
__magic_name__ : Tuple =normalize_answer(lowerCamelCase ).split()
__magic_name__ : Any =Counter(lowerCamelCase ) & Counter(lowerCamelCase )
__magic_name__ : Any =sum(common.values() )
if num_same == 0:
return 0
__magic_name__ : List[Any] =1.0 * num_same / len(lowerCamelCase )
__magic_name__ : Union[str, Any] =1.0 * num_same / len(lowerCamelCase )
__magic_name__ : List[str] =(2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return normalize_answer(lowerCamelCase ) == normalize_answer(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
assert len(lowerCamelCase ) == len(lowerCamelCase )
__magic_name__ : int =0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase ):
em += exact_match_score(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) > 0:
em /= len(lowerCamelCase )
return {"em": em}
def lowerCAmelCase_ ( lowerCamelCase ):
return model_prefix.startswith("""rag""" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[Any] ={p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__magic_name__ : Any ="""dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if not hasattr(lowerCamelCase , lowerCamelCase ) and not hasattr(lowerCamelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase ) )
delattr(lowerCamelCase , lowerCamelCase )
continue
__magic_name__ : str =p if hasattr(lowerCamelCase , lowerCamelCase ) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase ) )
delattr(lowerCamelCase , lowerCamelCase )
return hparams, config
| 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
UpperCAmelCase_ : Union[str, Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for attribute in key.split(""".""" ):
__magic_name__ : Tuple =getattr(lowerCamelCase , lowerCamelCase )
if weight_type is not None:
__magic_name__ : Optional[int] =getattr(lowerCamelCase , lowerCamelCase ).shape
else:
__magic_name__ : Union[str, Any] =hf_pointer.shape
assert hf_shape == value.shape, (
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}"
)
if weight_type == "weight":
__magic_name__ : int =value
elif weight_type == "weight_g":
__magic_name__ : int =value
elif weight_type == "weight_v":
__magic_name__ : Tuple =value
elif weight_type == "bias":
__magic_name__ : Tuple =value
else:
__magic_name__ : Any =value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[Any] =[]
__magic_name__ : Tuple =fairseq_model.state_dict()
__magic_name__ : Union[str, Any] =hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
__magic_name__ : List[Any] =None
for name, value in fairseq_dict.items():
__magic_name__ : Dict =False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
__magic_name__ : str =True
elif name.split(""".""" )[0] == "proj":
__magic_name__ : List[str] =fairseq_model.proj
__magic_name__ : Tuple =True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__magic_name__ : int =True
if "*" in mapped_key:
__magic_name__ : List[str] =name.split(lowerCamelCase )[0].split(""".""" )[-2]
__magic_name__ : str =mapped_key.replace("""*""" , lowerCamelCase )
if "weight_g" in name:
__magic_name__ : Optional[Any] ="""weight_g"""
elif "weight_v" in name:
__magic_name__ : Optional[Any] ="""weight_v"""
elif "bias" in name:
__magic_name__ : Tuple ="""bias"""
elif "weight" in name:
__magic_name__ : int ="""weight"""
else:
__magic_name__ : Union[str, Any] =None
set_recursively(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
continue
if not is_used:
unused_weights.append(lowerCamelCase )
logger.warning(F"Unused weights: {unused_weights}" )
return proj_weight
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =full_name.split("""conv_layers.""" )[-1]
__magic_name__ : Tuple =name.split(""".""" )
__magic_name__ : int =int(items[0] )
__magic_name__ : Union[str, Any] =int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
__magic_name__ : Tuple =value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
__magic_name__ : List[str] =value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
__magic_name__ : List[str] =value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
__magic_name__ : List[Any] =value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ , __magic_name__ : Tuple =emb.weight.shape
__magic_name__ : Dict =nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
__magic_name__ : int =emb.weight.data
return lin_layer
def lowerCAmelCase_ ( lowerCamelCase ):
with open(lowerCamelCase , """r""" , encoding="""utf-8""" ) as f:
__magic_name__ : Optional[Any] =f.readlines()
__magic_name__ : Tuple =[line.split(""" """ )[0] for line in lines]
__magic_name__ : List[Any] =len(lowerCamelCase )
__magic_name__ : List[str] ={
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(lowerCamelCase , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
__magic_name__ : Tuple =WavaVecaConfig.from_pretrained(lowerCamelCase )
__magic_name__ : List[Any] =SpeechaTextaConfig.from_pretrained(
lowerCamelCase , vocab_size=lowerCamelCase , decoder_layers=lowerCamelCase , do_stable_layer_norm=lowerCamelCase )
__magic_name__ : List[str] =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCamelCase , return_attention_mask=lowerCamelCase , )
__magic_name__ , __magic_name__ , __magic_name__ : List[Any] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__magic_name__ : Dict =model[0].eval()
# set weights for wav2vec2 encoder
__magic_name__ : Optional[Any] =WavaVecaModel(lowerCamelCase )
__magic_name__ : Tuple =recursively_load_weights_wavaveca(model.encoder , lowerCamelCase )
__magic_name__ : Tuple =SpeechaTextaForCausalLM(lowerCamelCase )
__magic_name__ , __magic_name__ : Dict =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=lowerCamelCase )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
__magic_name__ : List[str] =nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}" )
logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )
__magic_name__ : Optional[int] =SpeechEncoderDecoderModel(encoder=lowerCamelCase , decoder=lowerCamelCase )
__magic_name__ : Union[str, Any] =False
# add projection layer
__magic_name__ : List[str] =nn.Parameter(projection_layer.weight )
__magic_name__ : Dict =nn.Parameter(projection_layer.bias )
__magic_name__ : str =create_vocab_dict(lowerCamelCase )
with open(os.path.join(lowerCamelCase , """vocab.json""" ) , """w""" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
__magic_name__ : Any =SpeechaTextaTokenizer(os.path.join(lowerCamelCase , """vocab.json""" ) )
tokenizer.save_pretrained(lowerCamelCase )
__magic_name__ : str =hf_wavavec.config.to_dict()
__magic_name__ : int =tokenizer.pad_token_id
__magic_name__ : Optional[Any] =tokenizer.bos_token_id
__magic_name__ : Optional[Any] =tokenizer.eos_token_id
__magic_name__ : Tuple ="""speech_to_text_2"""
__magic_name__ : Union[str, Any] ="""wav2vec2"""
__magic_name__ : Union[str, Any] =SpeechEncoderDecoderConfig.from_dict(lowerCamelCase )
hf_wavavec.save_pretrained(lowerCamelCase )
feature_extractor.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
UpperCAmelCase_ : Any = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__magic_name__ : Dict =F"{src_lang}-{tgt_lang}"
print(F"Converting {dataset}-{pair}" )
__magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__magic_name__ : Optional[int] =F"{dataset}-{pair}"
__magic_name__ : int =Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
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
__magic_name__ : Dict ="""val""" if split == """validation""" else split
__magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" )
__magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" )
__magic_name__ : Optional[Any] =src_path.open("""w+""" )
__magic_name__ : List[Any] =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] ):
__magic_name__ : str =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)
| 21 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase ):
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__magic_name__ : int =nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if dist[i][j] != float("""inf""" ):
print(int(dist[i][j] ) , end="""\t""" )
else:
print("""INF""" , end="""\t""" )
print()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =[[float("""inf""" ) for _ in range(lowerCamelCase )] for _ in range(lowerCamelCase )]
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
__magic_name__ : Dict =graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(lowerCamelCase ):
# looping through rows of graph array
for i in range(lowerCamelCase ):
# looping through columns of graph array
for j in range(lowerCamelCase ):
if (
dist[i][k] != float("""inf""" )
and dist[k][j] != float("""inf""" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
__magic_name__ : List[str] =dist[i][k] + dist[k][j]
_print_dist(lowerCamelCase , lowerCamelCase )
return dist, v
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = int(input("Enter number of vertices: "))
UpperCAmelCase_ : str = int(input("Enter number of edges: "))
UpperCAmelCase_ : Optional[Any] = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
UpperCAmelCase_ : str = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
UpperCAmelCase_ : Optional[Any] = int(input("Enter source:"))
UpperCAmelCase_ : Union[str, Any] = int(input("Enter destination:"))
UpperCAmelCase_ : Dict = float(input("Enter weight:"))
UpperCAmelCase_ : Union[str, Any] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 21 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
import json
import sys
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase , encoding="""utf-8""" ) as f:
__magic_name__ : Tuple =json.load(lowerCamelCase )
__magic_name__ : Optional[Any] =["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]
for benchmark_name in sorted(lowerCamelCase ):
__magic_name__ : Union[str, Any] =results[benchmark_name]
__magic_name__ : Union[str, Any] =benchmark_name.split("""/""" )[-1]
output_md.append(F"### Benchmark: {benchmark_file_name}" )
__magic_name__ : Tuple ="""| metric |"""
__magic_name__ : Optional[Any] ="""|--------|"""
__magic_name__ : List[Any] ="""| new / old (diff) |"""
for metric_name in sorted(lowerCamelCase ):
__magic_name__ : Optional[int] =benchmark_res[metric_name]
__magic_name__ : str =metric_vals["""new"""]
__magic_name__ : Tuple =metric_vals.get("""old""" , lowerCamelCase )
__magic_name__ : Tuple =metric_vals.get("""diff""" , lowerCamelCase )
__magic_name__ : List[Any] =F" {new_val:f}" if isinstance(lowerCamelCase , (int, float) ) else """None"""
if old_val is not None:
val_str += F" / {old_val:f}" if isinstance(lowerCamelCase , (int, float) ) else "None"
if dif_val is not None:
val_str += F" ({dif_val:f})" if isinstance(lowerCamelCase , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("""</details>""" )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.writelines("""\n""".join(lowerCamelCase ) )
if __name__ == "__main__":
UpperCAmelCase_ : Tuple = sys.argv[1]
UpperCAmelCase_ : List[Any] = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 21 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : List[Any] =max_length
__magic_name__ : Dict =vocab
__magic_name__ : int =merges
__magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__magic_name__ : str =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
return cls(**__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case )
__magic_name__ : Tuple =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__magic_name__ : Tuple =max_length if max_length is not None else self.max_length
if max_length is not None:
__magic_name__ , __magic_name__ : Tuple =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 21 | 1 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
@register_to_config
def __init__( self :List[str] , __snake_case :bool , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : int =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
__magic_name__ : Dict =torch.zeros(__snake_case , __snake_case )
else:
__magic_name__ : Tuple =None
__magic_name__ : int =torch.nn.Parameter(__snake_case )
class __A ( UpperCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
def __init__( self :Dict , __snake_case :VQModel , __snake_case :CLIPTextModel , __snake_case :CLIPTokenizer , __snake_case :TransformeraDModel , __snake_case :VQDiffusionScheduler , __snake_case :LearnedClassifierFreeSamplingEmbeddings , ):
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=__snake_case , transformer=__snake_case , text_encoder=__snake_case , tokenizer=__snake_case , scheduler=__snake_case , learned_classifier_free_sampling_embeddings=__snake_case , )
def A__ ( self :Optional[int] , __snake_case :Optional[int] , __snake_case :Optional[Any] , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : List[str] =len(__snake_case ) if isinstance(__snake_case , __snake_case ) else 1
# get prompt text embeddings
__magic_name__ : str =self.tokenizer(
__snake_case , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
__magic_name__ : str =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__magic_name__ : int =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f" {self.tokenizer.model_max_length} tokens: {removed_text}" )
__magic_name__ : List[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
__magic_name__ : Union[str, Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
__magic_name__ : Optional[Any] =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__snake_case )
# duplicate text embeddings for each generation per prompt
__magic_name__ : Any =prompt_embeds.repeat_interleave(__snake_case , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
__magic_name__ : Union[str, Any] =self.learned_classifier_free_sampling_embeddings.embeddings
__magic_name__ : Optional[Any] =negative_prompt_embeds.unsqueeze(0 ).repeat(__snake_case , 1 , 1 )
else:
__magic_name__ : Any =[""""""] * batch_size
__magic_name__ : List[str] =text_input_ids.shape[-1]
__magic_name__ : Tuple =self.tokenizer(
__snake_case , padding="""max_length""" , max_length=__snake_case , truncation=__snake_case , return_tensors="""pt""" , )
__magic_name__ : List[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
__magic_name__ : Any =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__snake_case )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__magic_name__ : Any =negative_prompt_embeds.shape[1]
__magic_name__ : str =negative_prompt_embeds.repeat(1 , __snake_case , 1 )
__magic_name__ : Any =negative_prompt_embeds.view(batch_size * num_images_per_prompt , __snake_case , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__magic_name__ : Optional[int] =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self :Optional[Any] , __snake_case :Union[str, List[str]] , __snake_case :int = 1_00 , __snake_case :float = 5.0 , __snake_case :float = 1.0 , __snake_case :int = 1 , __snake_case :Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case :Optional[torch.FloatTensor] = None , __snake_case :Optional[str] = "pil" , __snake_case :bool = True , __snake_case :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __snake_case :int = 1 , ):
'''simple docstring'''
if isinstance(__snake_case , __snake_case ):
__magic_name__ : str =1
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : List[str] =len(__snake_case )
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__snake_case )}" )
__magic_name__ : List[str] =batch_size * num_images_per_prompt
__magic_name__ : Dict =guidance_scale > 1.0
__magic_name__ : Union[str, Any] =self._encode_prompt(__snake_case , __snake_case , __snake_case )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__snake_case , __snake_case ) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(__snake_case )}." )
# get the initial completely masked latents unless the user supplied it
__magic_name__ : List[Any] =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
__magic_name__ : List[Any] =self.transformer.num_vector_embeds - 1
__magic_name__ : Union[str, Any] =torch.full(__snake_case , __snake_case ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"""Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
f" {self.transformer.num_vector_embeds - 1} (inclusive)." )
__magic_name__ : Dict =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__snake_case , device=self.device )
__magic_name__ : Optional[Any] =self.scheduler.timesteps.to(self.device )
__magic_name__ : List[Any] =latents
for i, t in enumerate(self.progress_bar(__snake_case ) ):
# expand the sample if we are doing classifier free guidance
__magic_name__ : Dict =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
__magic_name__ : List[str] =self.transformer(__snake_case , encoder_hidden_states=__snake_case , timestep=__snake_case ).sample
if do_classifier_free_guidance:
__magic_name__ , __magic_name__ : List[str] =model_output.chunk(2 )
__magic_name__ : List[Any] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__snake_case , dim=1 , keepdim=__snake_case )
__magic_name__ : Any =self.truncate(__snake_case , __snake_case )
# remove `log(0)`'s (`-inf`s)
__magic_name__ : Union[str, Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
__magic_name__ : Union[str, Any] =self.scheduler.step(__snake_case , timestep=__snake_case , sample=__snake_case , generator=__snake_case ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__snake_case , __snake_case , __snake_case )
__magic_name__ : Tuple =self.vqvae.config.vq_embed_dim
__magic_name__ : str =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
__magic_name__ : Optional[int] =self.vqvae.quantize.get_codebook_entry(__snake_case , shape=__snake_case )
__magic_name__ : Any =self.vqvae.decode(__snake_case , force_not_quantize=__snake_case ).sample
__magic_name__ : Any =(image / 2 + 0.5).clamp(0 , 1 )
__magic_name__ : Optional[Any] =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__magic_name__ : Union[str, Any] =self.numpy_to_pil(__snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__snake_case )
def A__ ( self :List[str] , __snake_case :torch.FloatTensor , __snake_case :float ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =torch.sort(__snake_case , 1 , descending=__snake_case )
__magic_name__ : Any =torch.exp(__snake_case )
__magic_name__ : Dict =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
__magic_name__ : Dict =torch.full_like(keep_mask[:, 0:1, :] , __snake_case )
__magic_name__ : str =torch.cat((all_true, keep_mask) , dim=1 )
__magic_name__ : Tuple =keep_mask[:, :-1, :]
__magic_name__ : Optional[int] =keep_mask.gather(1 , indices.argsort(1 ) )
__magic_name__ : Optional[int] =log_p_x_0.clone()
__magic_name__ : Union[str, Any] =-torch.inf # -inf = log(0)
return rv
| 21 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Union[str, Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = ["ConditionalDetrFeatureExtractor"]
UpperCAmelCase_ : str = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ):
__magic_name__ : Any =nums[i]
__magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip())
UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 21 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = ["model.decoder.embed_positions.weights"]
def lowerCAmelCase_ ( lowerCamelCase ):
if "emb" in name:
__magic_name__ : List[str] =name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__magic_name__ : Optional[int] =name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__magic_name__ : List[str] =name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__magic_name__ : Tuple =name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__magic_name__ : Tuple =name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__magic_name__ : Optional[int] =name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__magic_name__ : Optional[int] =name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__magic_name__ : str =name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__magic_name__ : Any =name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__magic_name__ : Dict =name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__magic_name__ : List[str] =name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[Any] =list(state_dict.keys() )
__magic_name__ : List[Any] ={}
for key in keys:
__magic_name__ : Union[str, Any] =state_dict.pop(lowerCamelCase )
__magic_name__ : List[Any] =rename_keys(lowerCamelCase )
if "in_proj_weight" in key:
# split fused qkv proj
__magic_name__ : Optional[Any] =val[:hidden_size, :]
__magic_name__ : Optional[int] =val[hidden_size : 2 * hidden_size, :]
__magic_name__ : Dict =val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__magic_name__ : List[Any] =val
else:
__magic_name__ : Dict =val
return state_dict, enc_dec_proj_state_dict
def lowerCAmelCase_ ( lowerCamelCase ):
if checkpoint == "small":
# default config values
__magic_name__ : int =1024
__magic_name__ : str =24
__magic_name__ : int =16
elif checkpoint == "medium":
__magic_name__ : int =1536
__magic_name__ : List[Any] =48
__magic_name__ : List[str] =24
elif checkpoint == "large":
__magic_name__ : List[Any] =2048
__magic_name__ : Any =48
__magic_name__ : Union[str, Any] =32
else:
raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
__magic_name__ : Tuple =MusicgenDecoderConfig(
hidden_size=lowerCamelCase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowerCamelCase , num_attention_heads=lowerCamelCase , )
return config
@torch.no_grad()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase="cpu" ):
__magic_name__ : str =MusicGen.get_pretrained(lowerCamelCase , device=lowerCamelCase )
__magic_name__ : List[Any] =decoder_config_from_checkpoint(lowerCamelCase )
__magic_name__ : int =fairseq_model.lm.state_dict()
__magic_name__ , __magic_name__ : Optional[int] =rename_state_dict(
lowerCamelCase , hidden_size=decoder_config.hidden_size )
__magic_name__ : List[Any] =TaEncoderModel.from_pretrained("""t5-base""" )
__magic_name__ : List[Any] =EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__magic_name__ : Any =MusicgenForCausalLM(lowerCamelCase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__magic_name__ , __magic_name__ : List[str] =decoder.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowerCamelCase )
if len(lowerCamelCase ) > 0:
raise ValueError(F"Missing key(s) in state_dict: {missing_keys}" )
if len(lowerCamelCase ) > 0:
raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}" )
# init the composite model
__magic_name__ : List[Any] =MusicgenForConditionalGeneration(text_encoder=lowerCamelCase , audio_encoder=lowerCamelCase , decoder=lowerCamelCase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowerCamelCase )
# check we can do a forward pass
__magic_name__ : Any =torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__magic_name__ : Dict =input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__magic_name__ : int =model(input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__magic_name__ : Dict =AutoTokenizer.from_pretrained("""t5-base""" )
__magic_name__ : List[Any] =AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__magic_name__ : Optional[int] =MusicgenProcessor(feature_extractor=lowerCamelCase , tokenizer=lowerCamelCase )
# set the appropriate bos/pad token ids
__magic_name__ : List[str] =2048
__magic_name__ : Optional[int] =2048
# set other default generation config params
__magic_name__ : str =int(30 * audio_encoder.config.frame_rate )
__magic_name__ : Union[str, Any] =True
__magic_name__ : Any =3.0
if pytorch_dump_folder is not None:
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}" )
model.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
if repo_id:
logger.info(F"Pushing model {checkpoint} to {repo_id}" )
model.push_to_hub(lowerCamelCase )
processor.push_to_hub(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 21 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def __call__( self :Union[str, Any] ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None
__magic_name__ : Optional[int] =len(self.languages ) if self.languages else None
def __call__( self :List[str] ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[int] =set(self.languages )
if self.languages and set(__snake_case ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__magic_name__ : Any =[]
for lang, text in translation_dict.items():
if isinstance(__snake_case , __snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) )
return {"language": languages, "translation": translations}
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 21 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __A ( unittest.TestCase ):
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : str =tempfile.mkdtemp()
# fmt: off
__magic_name__ : str =["""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
__magic_name__ : Any =dict(zip(__snake_case , range(len(__snake_case ) ) ) )
__magic_name__ : Union[str, Any] =["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
__magic_name__ : Any ={"""unk_token""": """<unk>"""}
__magic_name__ : List[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__magic_name__ : Dict =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(__snake_case ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__snake_case ) )
__magic_name__ : List[str] ={
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
__magic_name__ : str =os.path.join(self.tmpdirname , __snake_case )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(__snake_case , __snake_case )
def A__ ( self :Dict , **__snake_case :Any ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def A__ ( self :List[Any] , **__snake_case :List[Any] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__snake_case )
def A__ ( self :Tuple , **__snake_case :List[Any] ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **__snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =[np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__magic_name__ : Tuple =[Image.fromarray(np.moveaxis(__snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.get_tokenizer()
__magic_name__ : List[Any] =self.get_rust_tokenizer()
__magic_name__ : Union[str, Any] =self.get_image_processor()
__magic_name__ : Optional[Any] =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
processor_slow.save_pretrained(self.tmpdirname )
__magic_name__ : int =CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=__snake_case )
__magic_name__ : Optional[Any] =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
processor_fast.save_pretrained(self.tmpdirname )
__magic_name__ : List[Any] =CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __snake_case )
self.assertIsInstance(processor_fast.tokenizer , __snake_case )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __snake_case )
self.assertIsInstance(processor_fast.image_processor , __snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Any =self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
__magic_name__ : Dict =self.get_image_processor(do_normalize=__snake_case , padding_value=1.0 )
__magic_name__ : Tuple =CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__snake_case , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __snake_case )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __snake_case )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =self.get_image_processor()
__magic_name__ : int =self.get_tokenizer()
__magic_name__ : Dict =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
__magic_name__ : int =self.prepare_image_inputs()
__magic_name__ : Union[str, Any] =image_processor(__snake_case , return_tensors="""np""" )
__magic_name__ : Any =processor(images=__snake_case , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : str =self.get_image_processor()
__magic_name__ : Tuple =self.get_tokenizer()
__magic_name__ : List[str] =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
__magic_name__ : Any ="""lower newer"""
__magic_name__ : Tuple =processor(text=__snake_case )
__magic_name__ : Dict =tokenizer(__snake_case )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : str =self.get_image_processor()
__magic_name__ : List[str] =self.get_tokenizer()
__magic_name__ : Optional[Any] =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
__magic_name__ : List[Any] ="""lower newer"""
__magic_name__ : Optional[Any] =self.prepare_image_inputs()
__magic_name__ : Optional[Any] =processor(text=__snake_case , images=__snake_case )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(__snake_case ):
processor()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Dict =self.get_image_processor()
__magic_name__ : str =self.get_tokenizer()
__magic_name__ : int =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
__magic_name__ : Optional[Any] =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__magic_name__ : Optional[Any] =processor.batch_decode(__snake_case )
__magic_name__ : Dict =tokenizer.batch_decode(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Tuple =self.get_image_processor()
__magic_name__ : Any =self.get_tokenizer()
__magic_name__ : List[str] =CLIPProcessor(tokenizer=__snake_case , image_processor=__snake_case )
__magic_name__ : Optional[int] ="""lower newer"""
__magic_name__ : int =self.prepare_image_inputs()
__magic_name__ : int =processor(text=__snake_case , images=__snake_case )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 21 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """mra"""
def __init__( self :Tuple , __snake_case :int=5_02_65 , __snake_case :Optional[int]=7_68 , __snake_case :Union[str, Any]=12 , __snake_case :List[Any]=12 , __snake_case :int=30_72 , __snake_case :str="gelu" , __snake_case :str=0.1 , __snake_case :Optional[int]=0.1 , __snake_case :Union[str, Any]=5_12 , __snake_case :Any=1 , __snake_case :Tuple=0.02 , __snake_case :Dict=1E-5 , __snake_case :int="absolute" , __snake_case :Any=4 , __snake_case :Tuple="full" , __snake_case :int=0 , __snake_case :str=0 , __snake_case :str=1 , __snake_case :str=0 , __snake_case :Tuple=2 , **__snake_case :List[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : Optional[Any] =vocab_size
__magic_name__ : List[Any] =max_position_embeddings
__magic_name__ : str =hidden_size
__magic_name__ : List[str] =num_hidden_layers
__magic_name__ : int =num_attention_heads
__magic_name__ : str =intermediate_size
__magic_name__ : str =hidden_act
__magic_name__ : Dict =hidden_dropout_prob
__magic_name__ : List[Any] =attention_probs_dropout_prob
__magic_name__ : Any =initializer_range
__magic_name__ : Optional[int] =type_vocab_size
__magic_name__ : Tuple =layer_norm_eps
__magic_name__ : List[str] =position_embedding_type
__magic_name__ : List[str] =block_per_row
__magic_name__ : Tuple =approx_mode
__magic_name__ : Optional[int] =initial_prior_first_n_blocks
__magic_name__ : List[str] =initial_prior_diagonal_n_blocks
| 21 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
return config
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ):
if conf_path is None:
__magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml"""
__magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase )
__magic_name__ : Tuple =VQModel(**config.model.params )
if ckpt_path is None:
__magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt"""
__magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase )
if ".ckpt" in ckpt_path:
__magic_name__ : Any =sd["""state_dict"""]
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
model.to(lowerCamelCase )
del sd
return model
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__magic_name__ : List[Any] =model.decode(lowerCamelCase )
return xrec
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 )
if reload:
__magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase )
importlib.reload(lowerCamelCase )
return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls )
def lowerCAmelCase_ ( lowerCamelCase ):
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ):
__magic_name__ : str =instantiate_from_config(lowerCamelCase )
if sd is not None:
model.load_state_dict(lowerCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load the specified checkpoint
if ckpt:
__magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Any =pl_sd["""global_step"""]
print(F"loaded model from global step {global_step}." )
else:
__magic_name__ : List[Any] ={"""state_dict""": None}
__magic_name__ : Optional[Any] =None
__magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""]
return model, global_step
| 21 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
__magic_name__ : Dict ={
"""7z""": (seven_zip_file, SevenZipExtractor),
"""bz2""": (bza_file, BzipaExtractor),
"""gzip""": (gz_file, GzipExtractor),
"""lz4""": (lza_file, LzaExtractor),
"""tar""": (tar_file, TarExtractor),
"""xz""": (xz_file, XzExtractor),
"""zip""": (zip_file, ZipExtractor),
"""zstd""": (zstd_file, ZstdExtractor),
}
__magic_name__ , __magic_name__ : List[Any] =input_paths_and_base_extractors[compression_format]
if input_path is None:
__magic_name__ : Optional[Any] =F"for '{compression_format}' compression_format, "
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(lowerCamelCase )
assert base_extractor.is_extractable(lowerCamelCase )
__magic_name__ : List[str] =tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
base_extractor.extract(lowerCamelCase , lowerCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
__magic_name__ : Any =file_path.read_text(encoding="""utf-8""" )
else:
__magic_name__ : Any =output_path.read_text(encoding="""utf-8""" )
__magic_name__ : Tuple =text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
__magic_name__ : List[str] ={
"""7z""": seven_zip_file,
"""bz2""": bza_file,
"""gzip""": gz_file,
"""lz4""": lza_file,
"""tar""": tar_file,
"""xz""": xz_file,
"""zip""": zip_file,
"""zstd""": zstd_file,
}
__magic_name__ : int =input_paths[compression_format]
if input_path is None:
__magic_name__ : List[Any] =F"for '{compression_format}' compression_format, "
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(lowerCamelCase )
__magic_name__ : Optional[int] =Extractor.infer_extractor_format(lowerCamelCase )
assert extractor_format is not None
__magic_name__ : List[str] =tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
Extractor.extract(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
__magic_name__ : List[str] =file_path.read_text(encoding="""utf-8""" )
else:
__magic_name__ : Optional[int] =output_path.read_text(encoding="""utf-8""" )
__magic_name__ : str =text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
import tarfile
__magic_name__ : Union[str, Any] =tmp_path / """data_dot_dot"""
directory.mkdir()
__magic_name__ : List[str] =directory / """tar_file_with_dot_dot.tar"""
with tarfile.TarFile(lowerCamelCase , """w""" ) as f:
f.add(lowerCamelCase , arcname=os.path.join("""..""" , text_file.name ) )
return path
@pytest.fixture
def lowerCAmelCase_ ( lowerCamelCase ):
import tarfile
__magic_name__ : Any =tmp_path / """data_sym_link"""
directory.mkdir()
__magic_name__ : Dict =directory / """tar_file_with_sym_link.tar"""
os.symlink("""..""" , directory / """subdir""" , target_is_directory=lowerCamelCase )
with tarfile.TarFile(lowerCamelCase , """w""" ) as f:
f.add(str(directory / """subdir""" ) , arcname="""subdir""" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"""insecure_tar_file, error_log""" , [("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : int ={
"""tar_file_with_dot_dot""": tar_file_with_dot_dot,
"""tar_file_with_sym_link""": tar_file_with_sym_link,
}
__magic_name__ : Optional[int] =insecure_tar_files[insecure_tar_file]
__magic_name__ : str =tmp_path / """extracted"""
TarExtractor.extract(lowerCamelCase , lowerCamelCase )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCAmelCase_ ( lowerCamelCase ):
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
__magic_name__ : List[str] =tmpdir / """not_a_zip_file"""
# From: https://github.com/python/cpython/pull/5053
__magic_name__ : Any =(
B"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
B"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"""
B"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
B"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
)
with not_a_zip_file.open("""wb""" ) as f:
f.write(lowerCamelCase )
assert zipfile.is_zipfile(str(lowerCamelCase ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(lowerCamelCase ) # but we're right
| 21 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_script.main )
def A__ ( self :Dict ):
'''simple docstring'''
debug_launcher(test_ops.main )
| 21 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None ):
if start is None:
__magic_name__ : Tuple =0
if end is None:
__magic_name__ : Tuple =len(lowerCamelCase ) - 1
if start >= end:
return
__magic_name__ : str =(start + end) // 2
slowsort(lowerCamelCase , lowerCamelCase , lowerCamelCase )
slowsort(lowerCamelCase , mid + 1 , lowerCamelCase )
if sequence[end] < sequence[mid]:
__magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[end]
slowsort(lowerCamelCase , lowerCamelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 21 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmelCase_ ( lowerCamelCase ):
if not sequence:
return []
if len(lowerCamelCase ) == 1:
return list(lowerCamelCase )
__magic_name__ : int =0
__magic_name__ : str =len(lowerCamelCase ) - 1
__magic_name__ : Optional[Any] =0
while mid <= high:
if sequence[mid] == colors[0]:
__magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
__magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid]
high -= 1
else:
__magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values"
raise ValueError(lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip()
UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
print(F"""{dutch_national_flag_sort(unsorted)}""")
| 21 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __A ( UpperCamelCase__ ):
UpperCamelCase = 42
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
@register_to_config
def __init__( self :Optional[int] , __snake_case :int = 32 , __snake_case :int = 64 , __snake_case :int = 20 , __snake_case :int = 7_68 , __snake_case :List[Any]=77 , __snake_case :Union[str, Any]=4 , __snake_case :float = 0.0 , __snake_case :str = "silu" , __snake_case :Optional[str] = None , __snake_case :Optional[str] = None , __snake_case :Optional[str] = "linear" , __snake_case :Optional[str] = "prd" , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =num_attention_heads
__magic_name__ : Tuple =attention_head_dim
__magic_name__ : Optional[int] =num_attention_heads * attention_head_dim
__magic_name__ : Union[str, Any] =additional_embeddings
__magic_name__ : Tuple =time_embed_dim or inner_dim
__magic_name__ : str =embedding_proj_dim or embedding_dim
__magic_name__ : List[Any] =clip_embed_dim or embedding_dim
__magic_name__ : Union[str, Any] =Timesteps(__snake_case , __snake_case , 0 )
__magic_name__ : Dict =TimestepEmbedding(__snake_case , __snake_case , out_dim=__snake_case , act_fn=__snake_case )
__magic_name__ : Tuple =nn.Linear(__snake_case , __snake_case )
if embedding_proj_norm_type is None:
__magic_name__ : Tuple =None
elif embedding_proj_norm_type == "layer":
__magic_name__ : Tuple =nn.LayerNorm(__snake_case )
else:
raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}" )
__magic_name__ : Tuple =nn.Linear(__snake_case , __snake_case )
if encoder_hid_proj_type is None:
__magic_name__ : Any =None
elif encoder_hid_proj_type == "linear":
__magic_name__ : List[str] =nn.Linear(__snake_case , __snake_case )
else:
raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}" )
__magic_name__ : Dict =nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , __snake_case ) )
if added_emb_type == "prd":
__magic_name__ : Union[str, Any] =nn.Parameter(torch.zeros(1 , 1 , __snake_case ) )
elif added_emb_type is None:
__magic_name__ : Optional[Any] =None
else:
raise ValueError(
f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`." )
__magic_name__ : Optional[int] =nn.ModuleList(
[
BasicTransformerBlock(
__snake_case , __snake_case , __snake_case , dropout=__snake_case , activation_fn="""gelu""" , attention_bias=__snake_case , )
for d in range(__snake_case )
] )
if norm_in_type == "layer":
__magic_name__ : Optional[Any] =nn.LayerNorm(__snake_case )
elif norm_in_type is None:
__magic_name__ : Optional[Any] =None
else:
raise ValueError(f"Unsupported norm_in_type: {norm_in_type}." )
__magic_name__ : Tuple =nn.LayerNorm(__snake_case )
__magic_name__ : Union[str, Any] =nn.Linear(__snake_case , __snake_case )
__magic_name__ : Any =torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10000.0 )
causal_attention_mask.triu_(1 )
__magic_name__ : Union[str, Any] =causal_attention_mask[None, ...]
self.register_buffer("""causal_attention_mask""" , __snake_case , persistent=__snake_case )
__magic_name__ : Union[str, Any] =nn.Parameter(torch.zeros(1 , __snake_case ) )
__magic_name__ : Tuple =nn.Parameter(torch.zeros(1 , __snake_case ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict ={}
def fn_recursive_add_processors(__snake_case :str , __snake_case :torch.nn.Module , __snake_case :Dict[str, AttentionProcessor] ):
if hasattr(__snake_case , """set_processor""" ):
__magic_name__ : Dict =module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}" , __snake_case , __snake_case )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__snake_case , __snake_case , __snake_case )
return processors
def A__ ( self :Any , __snake_case :Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
'''simple docstring'''
__magic_name__ : Dict =len(self.attn_processors.keys() )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(__snake_case )} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes." )
def fn_recursive_attn_processor(__snake_case :str , __snake_case :torch.nn.Module , __snake_case :List[Any] ):
if hasattr(__snake_case , """set_processor""" ):
if not isinstance(__snake_case , __snake_case ):
module.set_processor(__snake_case )
else:
module.set_processor(processor.pop(f"{name}.processor" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}" , __snake_case , __snake_case )
for name, module in self.named_children():
fn_recursive_attn_processor(__snake_case , __snake_case , __snake_case )
def A__ ( self :str ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def A__ ( self :int , __snake_case :List[Any] , __snake_case :Union[torch.Tensor, float, int] , __snake_case :torch.FloatTensor , __snake_case :Optional[torch.FloatTensor] = None , __snake_case :Optional[torch.BoolTensor] = None , __snake_case :bool = True , ):
'''simple docstring'''
__magic_name__ : Optional[int] =hidden_states.shape[0]
__magic_name__ : str =timestep
if not torch.is_tensor(__snake_case ):
__magic_name__ : Dict =torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(__snake_case ) and len(timesteps.shape ) == 0:
__magic_name__ : Optional[int] =timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__magic_name__ : Optional[Any] =timesteps * torch.ones(__snake_case , dtype=timesteps.dtype , device=timesteps.device )
__magic_name__ : Any =self.time_proj(__snake_case )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__magic_name__ : Optional[Any] =timesteps_projected.to(dtype=self.dtype )
__magic_name__ : Union[str, Any] =self.time_embedding(__snake_case )
if self.embedding_proj_norm is not None:
__magic_name__ : int =self.embedding_proj_norm(__snake_case )
__magic_name__ : Optional[int] =self.embedding_proj(__snake_case )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__magic_name__ : Union[str, Any] =self.encoder_hidden_states_proj(__snake_case )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""" )
__magic_name__ : Any =self.proj_in(__snake_case )
__magic_name__ : Tuple =self.positional_embedding.to(hidden_states.dtype )
__magic_name__ : Dict =[]
__magic_name__ : List[str] =0
if encoder_hidden_states is not None:
additional_embeds.append(__snake_case )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__magic_name__ : Optional[int] =proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__magic_name__ : Union[str, Any] =hidden_states[:, None, :]
__magic_name__ : Optional[Any] =additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__magic_name__ : List[Any] =self.prd_embedding.to(hidden_states.dtype ).expand(__snake_case , -1 , -1 )
additional_embeds.append(__snake_case )
__magic_name__ : Tuple =torch.cat(
__snake_case , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__magic_name__ : List[Any] =additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__magic_name__ : List[str] =F.pad(
__snake_case , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__magic_name__ : str =hidden_states + positional_embeddings
if attention_mask is not None:
__magic_name__ : Tuple =(1 - attention_mask.to(hidden_states.dtype )) * -10000.0
__magic_name__ : Optional[int] =F.pad(__snake_case , (0, self.additional_embeddings) , value=0.0 )
__magic_name__ : Any =(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__magic_name__ : Tuple =attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__magic_name__ : List[str] =self.norm_in(__snake_case )
for block in self.transformer_blocks:
__magic_name__ : str =block(__snake_case , attention_mask=__snake_case )
__magic_name__ : Tuple =self.norm_out(__snake_case )
if self.prd_embedding is not None:
__magic_name__ : Optional[Any] =hidden_states[:, -1]
else:
__magic_name__ : Union[str, Any] =hidden_states[:, additional_embeddings_len:]
__magic_name__ : str =self.proj_to_clip_embeddings(__snake_case )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=__snake_case )
def A__ ( self :int , __snake_case :Optional[int] ):
'''simple docstring'''
__magic_name__ : Tuple =(prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 21 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = 1
@register_to_config
def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ):
'''simple docstring'''
__magic_name__ : Dict =None
__magic_name__ : List[str] =None
__magic_name__ : str =None
def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case )
def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__magic_name__ : int =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__magic_name__ : str =std.flatten()
while len(std.shape ) < len(score.shape ):
__magic_name__ : List[str] =std.unsqueeze(-1 )
__magic_name__ : Union[str, Any] =-score / std
# compute
__magic_name__ : Tuple =-1.0 / len(self.timesteps )
__magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__magic_name__ : Dict =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__magic_name__ : Any =beta_t.unsqueeze(-1 )
__magic_name__ : Dict =-0.5 * beta_t * x
__magic_name__ : Optional[int] =torch.sqrt(__snake_case )
__magic_name__ : int =drift - diffusion**2 * score
__magic_name__ : List[str] =x + drift * dt
# add noise
__magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype )
__magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self :List[Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 21 | 1 |
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 __A ( unittest.TestCase ):
@slow
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : List[str] =AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" , return_dict=__snake_case ).to(__snake_case )
__magic_name__ : Any =AutoTokenizer.from_pretrained("""google/mt5-small""" )
__magic_name__ : Dict =tokenizer("""Hello there""" , return_tensors="""pt""" ).input_ids
__magic_name__ : Tuple =tokenizer("""Hi I am""" , return_tensors="""pt""" ).input_ids
__magic_name__ : List[Any] =model(input_ids.to(__snake_case ) , labels=labels.to(__snake_case ) ).loss
__magic_name__ : List[Any] =-(labels.shape[-1] * loss.item())
__magic_name__ : Optional[int] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class __A :
def __init__( self :Optional[Any] , __snake_case :List[str] , __snake_case :Any=99 , __snake_case :Optional[int]=13 , __snake_case :Any=16 , __snake_case :Any=7 , __snake_case :Tuple=True , __snake_case :List[Any]=True , __snake_case :Union[str, Any]=True , __snake_case :Dict=False , __snake_case :str=True , __snake_case :Optional[Any]=2 , __snake_case :Optional[int]=32 , __snake_case :int=4 , __snake_case :str=4 , __snake_case :Optional[int]=30 , __snake_case :str=0 , __snake_case :Optional[Any]=1 , __snake_case :Optional[int]=2 , __snake_case :List[str]=None , ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =parent
__magic_name__ : str =batch_size
__magic_name__ : List[str] =decoder_seq_length
# For common tests
__magic_name__ : Optional[Any] =self.decoder_seq_length
__magic_name__ : Any =is_training
__magic_name__ : Any =use_attention_mask
__magic_name__ : List[str] =use_labels
__magic_name__ : List[str] =vocab_size
__magic_name__ : str =d_model
__magic_name__ : Optional[int] =d_model
__magic_name__ : str =decoder_layers
__magic_name__ : Optional[Any] =decoder_layers
__magic_name__ : Dict =decoder_ffn_dim
__magic_name__ : Tuple =decoder_attention_heads
__magic_name__ : Optional[Any] =decoder_attention_heads
__magic_name__ : Union[str, Any] =eos_token_id
__magic_name__ : Optional[Any] =bos_token_id
__magic_name__ : Optional[Any] =pad_token_id
__magic_name__ : Any =decoder_start_token_id
__magic_name__ : str =use_cache
__magic_name__ : Optional[int] =max_position_embeddings
__magic_name__ : Any =None
__magic_name__ : Dict =decoder_seq_length
__magic_name__ : Union[str, Any] =2
__magic_name__ : List[str] =1
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__magic_name__ : Any =None
if self.use_attention_mask:
__magic_name__ : int =ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
__magic_name__ : Optional[int] =None
if self.use_labels:
__magic_name__ : List[str] =ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__magic_name__ : Tuple =TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def A__ ( self :Union[str, Any] , __snake_case :Any , __snake_case :int , __snake_case :Dict , __snake_case :Dict , ):
'''simple docstring'''
__magic_name__ : Dict =True
__magic_name__ : List[Any] =TrOCRDecoder(config=__snake_case ).to(__snake_case ).eval()
__magic_name__ : List[Any] =input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
__magic_name__ : str =model(__snake_case , use_cache=__snake_case )
__magic_name__ : Optional[Any] =model(__snake_case )
__magic_name__ : str =model(__snake_case , use_cache=__snake_case )
self.parent.assertTrue(len(__snake_case ) == len(__snake_case ) )
self.parent.assertTrue(len(__snake_case ) == len(__snake_case ) + 1 )
__magic_name__ : str =outputs["""past_key_values"""]
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Dict =ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
__magic_name__ : List[Any] =torch.cat([input_ids, next_tokens] , dim=-1 )
__magic_name__ : Dict =model(__snake_case )["""last_hidden_state"""]
__magic_name__ : Union[str, Any] =model(__snake_case , past_key_values=__snake_case )["""last_hidden_state"""]
# select random slice
__magic_name__ : Dict =ids_tensor((1,) , output_from_past.shape[-1] ).item()
__magic_name__ : List[str] =output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
__magic_name__ : List[str] =output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(__snake_case , __snake_case , atol=1E-3 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : int =config_and_inputs
__magic_name__ : Optional[int] ={"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
UpperCamelCase = (TrOCRForCausalLM,) if is_torch_available() else ()
UpperCamelCase = {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
UpperCamelCase = True
UpperCamelCase = False
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =TrOCRStandaloneDecoderModelTester(self , is_training=__snake_case )
__magic_name__ : Optional[int] =ConfigTester(self , config_class=__snake_case )
def A__ ( self :Optional[int] ):
'''simple docstring'''
pass
def A__ ( self :Tuple ):
'''simple docstring'''
pass
def A__ ( self :int ):
'''simple docstring'''
pass
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return
@unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :)
def A__ ( self :Optional[int] ):
'''simple docstring'''
pass
| 21 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
def __init__( self :List[Any] ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =nn.Linear(3 , 4 )
__magic_name__ : Union[str, Any] =nn.BatchNormad(4 )
__magic_name__ : List[str] =nn.Linear(4 , 5 )
def A__ ( self :Dict , __snake_case :Tuple ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) )
class __A ( UpperCamelCase__ ):
def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ):
'''simple docstring'''
return output + 1
class __A ( unittest.TestCase ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : Tuple =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
self.assertEqual(test_model._hf_hook , __snake_case )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : List[str] =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
add_hook_to_module(__snake_case , __snake_case , append=__snake_case )
self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
__magic_name__ : Any =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(x + 1 )
__magic_name__ : Optional[Any] =test_model(x + 2 )
__magic_name__ : int =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : int =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : str =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : List[str] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
assert torch.allclose(__snake_case , __snake_case , atol=1E-5 )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
__magic_name__ : Dict =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(__snake_case )
__magic_name__ : Dict =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Optional[int] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
assert torch.allclose(__snake_case , output + 2 , atol=1E-5 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =ModelForTest()
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Dict =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__magic_name__ : Any =True
__magic_name__ : Any =test_model(__snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[Any] =model(__snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) )
__magic_name__ : int =torch.randn(2 , 3 ).to(0 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : Union[str, Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
__magic_name__ : Tuple ={
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Tuple =torch.randn(2 , 3 )
__magic_name__ : int =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : str =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Optional[int] =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Any =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 21 | 1 |
import colorsys
from PIL import Image # type: ignore
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Any =x
__magic_name__ : Dict =y
for step in range(lowerCamelCase ): # noqa: B007
__magic_name__ : str =a * a - b * b + x
__magic_name__ : int =2 * a * b + y
__magic_name__ : Union[str, Any] =a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCAmelCase_ ( lowerCamelCase ):
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCAmelCase_ ( lowerCamelCase ):
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(lowerCamelCase , 1 , 1 ) )
def lowerCAmelCase_ ( lowerCamelCase = 800 , lowerCamelCase = 600 , lowerCamelCase = -0.6 , lowerCamelCase = 0 , lowerCamelCase = 3.2 , lowerCamelCase = 50 , lowerCamelCase = True , ):
__magic_name__ : Tuple =Image.new("""RGB""" , (image_width, image_height) )
__magic_name__ : List[Any] =img.load()
# loop through the image-coordinates
for image_x in range(lowerCamelCase ):
for image_y in range(lowerCamelCase ):
# determine the figure-coordinates based on the image-coordinates
__magic_name__ : Optional[int] =figure_width / image_width * image_height
__magic_name__ : Optional[int] =figure_center_x + (image_x / image_width - 0.5) * figure_width
__magic_name__ : Tuple =figure_center_y + (image_y / image_height - 0.5) * figure_height
__magic_name__ : Optional[int] =get_distance(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
__magic_name__ : Optional[int] =get_color_coded_rgb(lowerCamelCase )
else:
__magic_name__ : int =get_black_and_white_rgb(lowerCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
UpperCAmelCase_ : Any = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = KandinskyInpaintPipeline
UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase = False
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return 32
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def A__ ( self :Dict ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A__ ( self :List[Any] ):
'''simple docstring'''
return 1_00
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def A__ ( self :str ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : str =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__magic_name__ : Tuple =MultilingualCLIP(__snake_case )
__magic_name__ : Optional[int] =text_encoder.eval()
return text_encoder
@property
def A__ ( self :Dict ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Optional[Any] ={
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case )
return model
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A__ ( self :Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs )
return model
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[str] =self.dummy_text_encoder
__magic_name__ : Optional[Any] =self.dummy_tokenizer
__magic_name__ : Optional[Any] =self.dummy_unet
__magic_name__ : Tuple =self.dummy_movq
__magic_name__ : List[str] =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , )
__magic_name__ : str ={
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case )
# create init_image
__magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
__magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa )
__magic_name__ : Any =0
if str(__snake_case ).startswith("""mps""" ):
__magic_name__ : Dict =torch.manual_seed(__snake_case )
else:
__magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case )
__magic_name__ : List[Any] ={
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Tuple ="""cpu"""
__magic_name__ : List[Any] =self.get_dummy_components()
__magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case )
__magic_name__ : Tuple =pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) )
__magic_name__ : List[Any] =output.images
__magic_name__ : Any =pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
__magic_name__ : int =image[0, -3:, -3:, -1]
__magic_name__ : str =image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Optional[Any] =np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def A__ ( self :Dict ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def A__ ( self :List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : List[str] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__magic_name__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa )
__magic_name__ : Any =0
__magic_name__ : int ="""a hat"""
__magic_name__ : int =KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
__magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__magic_name__ : int =pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 )
__magic_name__ , __magic_name__ : Dict =pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__magic_name__ : Optional[Any] =pipeline(
__snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , )
__magic_name__ : Optional[int] =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 21 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __A :
def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ):
'''simple docstring'''
__magic_name__ : Optional[int] =parent
__magic_name__ : List[Any] =batch_size
__magic_name__ : List[str] =is_training
__magic_name__ : List[str] =use_auxiliary_loss
__magic_name__ : Union[str, Any] =num_queries
__magic_name__ : str =num_channels
__magic_name__ : Union[str, Any] =min_size
__magic_name__ : Union[str, Any] =max_size
__magic_name__ : Optional[int] =num_labels
__magic_name__ : Tuple =hidden_dim
__magic_name__ : Any =hidden_dim
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
__magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
__magic_name__ : List[str] =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
__magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
__magic_name__ : str =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Dict =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__magic_name__ : str =self.num_queries
__magic_name__ : Dict =self.num_labels
__magic_name__ : int =[1, 1, 1, 1]
__magic_name__ : List[str] =self.num_channels
__magic_name__ : str =64
__magic_name__ : List[str] =1_28
__magic_name__ : Optional[Any] =self.hidden_dim
__magic_name__ : Tuple =self.hidden_dim
__magic_name__ : Optional[int] =self.hidden_dim
return config
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs()
__magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : int =output.encoder_hidden_states
__magic_name__ : List[str] =output.pixel_decoder_hidden_states
__magic_name__ : int =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_layers )
def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ):
'''simple docstring'''
with torch.no_grad():
__magic_name__ : List[str] =MaskaFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__snake_case , __snake_case )
def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case :List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case )
__magic_name__ : List[str] =model(__snake_case )
comm_check_on_output(__snake_case )
__magic_name__ : Any =model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : Any =MaskaFormerModelTester(self )
__magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def A__ ( self :Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def A__ ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def A__ ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def A__ ( self :int ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def A__ ( self :Tuple ):
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Tuple =model_class(__snake_case )
__magic_name__ : Optional[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Tuple =[*signature.parameters.keys()]
__magic_name__ : Optional[Any] =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def A__ ( self :Tuple ):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =(self.model_tester.min_size,) * 2
__magic_name__ : Union[str, Any] ={
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
__magic_name__ : Optional[Any] =self.model_tester.get_config()
__magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case )
__magic_name__ : Any =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case )
__magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def A__ ( self :int ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__magic_name__ : List[Any] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Dict =model_class(__snake_case )
model.to(__snake_case )
model.train()
__magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : List[str] =self.all_model_classes[1]
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs()
__magic_name__ : Tuple =True
__magic_name__ : Optional[int] =True
__magic_name__ : int =model_class(__snake_case ).to(__snake_case )
model.train()
__magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
__magic_name__ : Optional[int] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__magic_name__ : Optional[int] =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase_ : Dict = 1e-4
def lowerCAmelCase_ ( ):
__magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __A ( unittest.TestCase ):
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def A__ ( self :int ):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case )
__magic_name__ : int =self.default_image_processor
__magic_name__ : List[Any] =prepare_img()
__magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Dict =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : List[str] =model(**__snake_case )
__magic_name__ : Any =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Dict =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
__magic_name__ : Any =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Optional[int] =self.default_image_processor
__magic_name__ : Tuple =prepare_img()
__magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
__magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) )
with torch.no_grad():
__magic_name__ : str =model(**__snake_case )
# masks_queries_logits
__magic_name__ : List[Any] =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__magic_name__ : List[Any] =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
__magic_name__ : Any =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__magic_name__ : int =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval()
__magic_name__ : Any =self.default_image_processor
__magic_name__ : Union[str, Any] =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
__magic_name__ : str =inputs["""pixel_values"""].to(__snake_case )
__magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]]
__magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__magic_name__ : Dict =model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 21 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
def __init__( self :List[Any] ):
'''simple docstring'''
super().__init__()
__magic_name__ : Tuple =nn.Linear(3 , 4 )
__magic_name__ : Union[str, Any] =nn.BatchNormad(4 )
__magic_name__ : List[str] =nn.Linear(4 , 5 )
def A__ ( self :Dict , __snake_case :Tuple ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) )
class __A ( UpperCamelCase__ ):
def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ):
'''simple docstring'''
return output + 1
class __A ( unittest.TestCase ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : Tuple =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
self.assertEqual(test_model._hf_hook , __snake_case )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
__magic_name__ : List[str] =ModelHook()
add_hook_to_module(__snake_case , __snake_case )
add_hook_to_module(__snake_case , __snake_case , append=__snake_case )
self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(__snake_case , """_old_forward""" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , """forward""" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] )
remove_hook_from_module(__snake_case )
self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) )
self.assertFalse(hasattr(__snake_case , """_old_forward""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
__magic_name__ : Any =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(x + 1 )
__magic_name__ : Optional[Any] =test_model(x + 2 )
__magic_name__ : int =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : int =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : str =PreForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : List[str] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
assert torch.allclose(__snake_case , __snake_case , atol=1E-5 )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
__magic_name__ : Dict =torch.randn(2 , 3 )
__magic_name__ : Any =test_model(__snake_case )
__magic_name__ : Dict =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Any =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Optional[int] =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
__magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
assert torch.allclose(__snake_case , output + 2 , atol=1E-5 )
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =ModelForTest()
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =test_model(__snake_case )
__magic_name__ : Union[str, Any] =PostForwardHook()
add_hook_to_module(__snake_case , __snake_case )
__magic_name__ : Dict =test_model(__snake_case )
self.assertTrue(torch.allclose(__snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
__magic_name__ : Any =True
__magic_name__ : Any =test_model(__snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Optional[Any] =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[Any] =model(__snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) )
__magic_name__ : int =torch.randn(2 , 3 ).to(0 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : int =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : Union[str, Any] =torch.randn(2 , 3 )
__magic_name__ : Optional[int] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
__magic_name__ : Tuple ={
"""execution_device""": 0 if torch.cuda.is_available() else """cpu""",
"""offload""": True,
"""offload_buffers""": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Tuple =torch.randn(2 , 3 )
__magic_name__ : int =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[Any] ):
'''simple docstring'''
__magic_name__ : Any =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : str =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : Optional[int] =torch.randn(2 , 3 )
__magic_name__ : Union[str, Any] =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Dict =ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# This will move each submodule on different devices
__magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu"""
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
# Buffers are not included in the offload by default, so are on the execution device
__magic_name__ : Optional[Any] =torch.device(__snake_case )
self.assertEqual(model.batchnorm.running_mean.device , __snake_case )
__magic_name__ : int =torch.randn(2 , 3 )
__magic_name__ : Any =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
__snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) )
__magic_name__ : List[Any] =torch.randn(2 , 3 )
__magic_name__ : str =model(__snake_case )
self.assertEqual(output.device , __snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(__snake_case )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) )
self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
| 21 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """segformer"""
def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(**__snake_case )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , )
__magic_name__ : Dict =num_channels
__magic_name__ : str =num_encoder_blocks
__magic_name__ : List[Any] =depths
__magic_name__ : Optional[Any] =sr_ratios
__magic_name__ : List[str] =hidden_sizes
__magic_name__ : List[str] =patch_sizes
__magic_name__ : Any =strides
__magic_name__ : Optional[Any] =mlp_ratios
__magic_name__ : str =num_attention_heads
__magic_name__ : int =hidden_act
__magic_name__ : List[Any] =hidden_dropout_prob
__magic_name__ : Optional[Any] =attention_probs_dropout_prob
__magic_name__ : Optional[Any] =classifier_dropout_prob
__magic_name__ : List[str] =initializer_range
__magic_name__ : List[str] =drop_path_rate
__magic_name__ : List[Any] =layer_norm_eps
__magic_name__ : List[str] =decoder_hidden_size
__magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case )
__magic_name__ : Dict =semantic_loss_ignore_index
class __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :Any ):
'''simple docstring'''
return 1E-4
@property
def A__ ( self :int ):
'''simple docstring'''
return 12
| 21 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
assert isinstance(lowerCamelCase , lowerCamelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Dict =tmp_path / """cache"""
__magic_name__ : int ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__magic_name__ : Dict =ParquetDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase ).read()
_check_parquet_dataset(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =tmp_path / """cache"""
__magic_name__ : Dict ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__magic_name__ : List[str] =features.copy() if features else default_expected_features
__magic_name__ : Any =(
Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : Dict =ParquetDatasetReader(lowerCamelCase , features=lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_parquet_dataset(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =tmp_path / """cache"""
__magic_name__ : List[Any] ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__magic_name__ : Optional[Any] =ParquetDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase , split=lowerCamelCase ).read()
_check_parquet_dataset(lowerCamelCase , lowerCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if issubclass(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Dict =parquet_path
elif issubclass(lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =[parquet_path]
__magic_name__ : str =tmp_path / """cache"""
__magic_name__ : Tuple ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__magic_name__ : List[str] =ParquetDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_parquet_dataset(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=("train",) ):
assert isinstance(lowerCamelCase , lowerCamelCase )
for split in splits:
__magic_name__ : Tuple =dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[int] =tmp_path / """cache"""
__magic_name__ : Any ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__magic_name__ : str =ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase ).read()
_check_parquet_datasetdict(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[int] =tmp_path / """cache"""
__magic_name__ : List[Any] ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__magic_name__ : List[str] =features.copy() if features else default_expected_features
__magic_name__ : Optional[Any] =(
Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : Union[str, Any] =ParquetDatasetReader({"""train""": parquet_path} , features=lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_parquet_datasetdict(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if split:
__magic_name__ : Optional[int] ={split: parquet_path}
else:
__magic_name__ : int ="""train"""
__magic_name__ : Dict ={"""train""": parquet_path, """test""": parquet_path}
__magic_name__ : Dict =tmp_path / """cache"""
__magic_name__ : Optional[Any] ={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__magic_name__ : List[str] =ParquetDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_parquet_datasetdict(lowerCamelCase , lowerCamelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =ParquetDatasetWriter(lowerCamelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
__magic_name__ : Any =pq.ParquetFile(tmp_path / """foo.parquet""" )
__magic_name__ : Optional[int] =pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =str(shared_datadir / """test_image_rgb.jpg""" )
__magic_name__ : Tuple ={"""image""": [image_path]}
__magic_name__ : str =Features({"""image""": Image()} )
__magic_name__ : Any =Dataset.from_dict(lowerCamelCase , features=lowerCamelCase )
__magic_name__ : int =ParquetDatasetWriter(lowerCamelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
__magic_name__ : List[str] =Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
__magic_name__ : Any =ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=lowerCamelCase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
assert get_writer_batch_size(lowerCamelCase ) == expected
| 21 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
__magic_name__ : Tuple =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0]
chosen_vertices.add(lowerCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__magic_name__ : Tuple =elem[1][1].index(lowerCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 21 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Tuple =SwinConfig(
embed_dim=192 , depths=(2, 2, 18, 2) , num_heads=(6, 12, 24, 48) , window_size=12 , out_features=["""stage2""", """stage3""", """stage4"""] , )
__magic_name__ : Tuple =DetaConfig(
backbone_config=lowerCamelCase , num_queries=900 , encoder_ffn_dim=2048 , decoder_ffn_dim=2048 , num_feature_levels=5 , assign_first_stage=lowerCamelCase , with_box_refine=lowerCamelCase , two_stage=lowerCamelCase , )
# set labels
__magic_name__ : Optional[Any] ="""huggingface/label-files"""
if "o365" in model_name:
__magic_name__ : int =366
__magic_name__ : Any ="""object365-id2label.json"""
else:
__magic_name__ : Optional[int] =91
__magic_name__ : Any ="""coco-detection-id2label.json"""
__magic_name__ : Union[str, Any] =num_labels
__magic_name__ : Any =json.load(open(cached_download(hf_hub_url(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
__magic_name__ : Union[str, Any] ={int(lowerCamelCase ): v for k, v in idalabel.items()}
__magic_name__ : Union[str, Any] =idalabel
__magic_name__ : Tuple ={v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =[]
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.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.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )
if i < 3:
rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight") )
rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias") )
rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""") )
rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""") )
rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""") )
rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""") )
rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""") )
rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""") )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias") )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =dct.pop(lowerCamelCase )
__magic_name__ : List[str] =val
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__magic_name__ : Optional[int] =num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__magic_name__ : List[str] =state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight" )
__magic_name__ : Any =state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : List[Any] =in_proj_weight[:dim, :]
__magic_name__ : Tuple =in_proj_bias[: dim]
__magic_name__ : str =in_proj_weight[
dim : dim * 2, :
]
__magic_name__ : Optional[int] =in_proj_bias[
dim : dim * 2
]
__magic_name__ : Union[str, Any] =in_proj_weight[
-dim :, :
]
__magic_name__ : Tuple =in_proj_bias[-dim :]
# fmt: on
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
# transformer decoder self-attention layers
__magic_name__ : Union[str, Any] =config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
__magic_name__ : Dict =state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
__magic_name__ : int =state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : int =in_proj_weight[:hidden_size, :]
__magic_name__ : Tuple =in_proj_bias[:hidden_size]
__magic_name__ : int =in_proj_weight[
hidden_size : hidden_size * 2, :
]
__magic_name__ : List[Any] =in_proj_bias[hidden_size : hidden_size * 2]
__magic_name__ : str =in_proj_weight[-hidden_size:, :]
__magic_name__ : str =in_proj_bias[-hidden_size:]
def lowerCAmelCase_ ( ):
__magic_name__ : int ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
__magic_name__ : str =Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Dict =get_deta_config(lowerCamelCase )
# load original state dict
if model_name == "deta-swin-large":
__magic_name__ : List[str] =hf_hub_download(repo_id="""nielsr/deta-checkpoints""" , filename="""adet_swin_ft.pth""" )
elif model_name == "deta-swin-large-o365":
__magic_name__ : Dict =hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" , filename="""deta_swin_pt_o365.pth""" )
else:
raise ValueError(F"Model name {model_name} not supported" )
__magic_name__ : int =torch.load(lowerCamelCase , map_location="""cpu""" )["""model"""]
# original state dict
for name, param in state_dict.items():
print(lowerCamelCase , param.shape )
# rename keys
__magic_name__ : Any =create_rename_keys(lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_swin_q_k_v(lowerCamelCase , config.backbone_config )
read_in_decoder_q_k_v(lowerCamelCase , lowerCamelCase )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
__magic_name__ : Any =state_dict.pop(lowerCamelCase )
__magic_name__ : Any =val
if "input_proj" in key:
__magic_name__ : List[str] =state_dict.pop(lowerCamelCase )
__magic_name__ : Optional[Any] =val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
__magic_name__ : int =state_dict.pop(lowerCamelCase )
__magic_name__ : Any =val
# finally, create HuggingFace model and load state dict
__magic_name__ : str =DetaForObjectDetection(lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
__magic_name__ : Optional[Any] ="""cuda""" if torch.cuda.is_available() else """cpu"""
model.to(lowerCamelCase )
# load image processor
__magic_name__ : Any =DetaImageProcessor(format="""coco_detection""" )
# verify our conversion on image
__magic_name__ : Tuple =prepare_img()
__magic_name__ : Optional[Any] =processor(images=lowerCamelCase , return_tensors="""pt""" )
__magic_name__ : int =encoding["""pixel_values"""]
__magic_name__ : List[str] =model(pixel_values.to(lowerCamelCase ) )
# verify logits
print("""Logits:""" , outputs.logits[0, :3, :3] )
print("""Boxes:""" , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
__magic_name__ : str =torch.tensor(
[[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
__magic_name__ : Dict =torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
elif model_name == "deta-swin-large-o365":
__magic_name__ : List[Any] =torch.tensor(
[[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
__magic_name__ : Optional[Any] =torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(lowerCamelCase ) , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(lowerCamelCase ) , atol=1E-4 )
print("""Everything ok!""" )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..." )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
# Push to hub
if push_to_hub:
print("""Pushing model and processor to hub...""" )
model.push_to_hub(F"jozhang97/{model_name}" )
processor.push_to_hub(F"jozhang97/{model_name}" )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
type=str,
default="deta-swin-large",
choices=["deta-swin-large", "deta-swin-large-o365"],
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
help="Path to the folder to output PyTorch model.",
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCAmelCase_ : str = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 21 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) )
__magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) )
__magic_name__ , __magic_name__ : Tuple =0, 0
__magic_name__ : Optional[Any] =n - i
__magic_name__ : Union[str, Any] =memo.get(lowerCamelCase )
if sub_memo is not None:
__magic_name__ : int =sub_memo.get(lowerCamelCase )
if jumps is not None and len(lowerCamelCase ) > 0:
# find and make the largest jump without going over
__magic_name__ : Dict =-1
for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__magic_name__ : Optional[Any] =_k
break
if max_jump >= 0:
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump]
# since the difference between jumps is cached, add c
__magic_name__ : Tuple =diff + c
for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ):
__magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 )
if new_c > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__magic_name__ : str =[]
else:
__magic_name__ : List[str] ={c: []}
__magic_name__ : List[str] =sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
__magic_name__ : Tuple =sub_memo[c]
# keep jumps sorted by # of terms skipped
__magic_name__ : List[Any] =0
while j < len(lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if i >= n:
return 0, i
if k > len(lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__magic_name__ : Tuple =i
__magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0
for j in range(len(lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__magic_name__ : Optional[Any] =ds_c + ds_b
diff += addend
__magic_name__ : str =0
for j in range(lowerCamelCase ):
__magic_name__ : int =a_i[j] + addend
__magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return diff, i - start_i
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for j in range(lowerCamelCase , len(lowerCamelCase ) ):
__magic_name__ : Tuple =digits[j] + addend
if s >= 10:
__magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 )
__magic_name__ : int =addend // 10 + quotient
else:
__magic_name__ : Dict =s
__magic_name__ : Any =addend // 10
if addend == 0:
break
while addend > 0:
__magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 )
digits.append(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase = 10**15 ):
__magic_name__ : List[str] =[1]
__magic_name__ : str =1
__magic_name__ : str =0
while True:
__magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
__magic_name__ : int =0
for j in range(len(lowerCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 21 | 1 |
from numpy import exp, pi, sqrt
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
def lowerCAmelCase_ ( *lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCamelCase , """handle_key""" , lowerCamelCase )
return func
return decorator
class __A ( UpperCamelCase__ ):
def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ):
'''simple docstring'''
__magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case )
if not hasattr(__snake_case , """key_handler""" ):
setattr(__snake_case , """key_handler""" , {} )
setattr(__snake_case , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__magic_name__ : int =getattr(__snake_case , """handle_key""" , [] )
for key in handled_keys:
__magic_name__ : List[str] =value
return new_cls
@staticmethod
def A__ ( cls :Optional[int] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =get_character()
if char != KEYMAP["undefined"]:
__magic_name__ : Optional[int] =ord(__snake_case )
__magic_name__ : int =cls.key_handler.get(__snake_case )
if handler:
__magic_name__ : Dict =char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 21 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( lowerCamelCase ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
UpperCAmelCase_ : Tuple = "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"
class __A ( UpperCamelCase__ ):
@staticmethod
def A__ ( __snake_case :ArgumentParser ):
'''simple docstring'''
__magic_name__ : List[str] =parser.add_parser(
"""convert""" , help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" , )
train_parser.add_argument("""--model_type""" , type=__snake_case , required=__snake_case , help="""Model's type.""" )
train_parser.add_argument(
"""--tf_checkpoint""" , type=__snake_case , required=__snake_case , help="""TensorFlow checkpoint path or folder.""" )
train_parser.add_argument(
"""--pytorch_dump_output""" , type=__snake_case , required=__snake_case , help="""Path to the PyTorch saved model output.""" )
train_parser.add_argument("""--config""" , type=__snake_case , default="""""" , help="""Configuration file path or folder.""" )
train_parser.add_argument(
"""--finetuning_task_name""" , type=__snake_case , default=__snake_case , help="""Optional fine-tuning task name if the TF model was a finetuned model.""" , )
train_parser.set_defaults(func=__snake_case )
def __init__( self :Any , __snake_case :str , __snake_case :str , __snake_case :str , __snake_case :str , __snake_case :str , *__snake_case :Optional[Any] , ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =logging.get_logger("""transformers-cli/converting""" )
self._logger.info(f"Loading model {model_type}" )
__magic_name__ : List[Any] =model_type
__magic_name__ : List[str] =tf_checkpoint
__magic_name__ : Union[str, Any] =pytorch_dump_output
__magic_name__ : Tuple =config
__magic_name__ : Optional[int] =finetuning_task_name
def A__ ( self :int ):
'''simple docstring'''
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
if "ckpt" in self._tf_checkpoint.lower():
__magic_name__ : Tuple =self._tf_checkpoint
__magic_name__ : Tuple =""""""
else:
__magic_name__ : List[Any] =self._tf_checkpoint
__magic_name__ : List[str] =""""""
convert_transfo_xl_checkpoint_to_pytorch(
__snake_case , self._config , self._pytorch_dump_output , __snake_case )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__snake_case )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"""--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]""" )
| 21 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase_ ( lowerCamelCase ):
def choose_first(lowerCamelCase , lowerCamelCase=False ):
assert isinstance(lowerCamelCase , lowerCamelCase )
if len(lowerCamelCase ) == 1:
__magic_name__ : List[str] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__magic_name__ : Tuple ={k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
__magic_name__ : str ={"""id""": example["""id"""]}
__magic_name__ : List[Any] =example["""annotations"""]
__magic_name__ : List[str] =annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
__magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""]
__magic_name__ : List[str] =[]
__magic_name__ : Dict =[]
__magic_name__ : str =["""<cls>"""]
else:
__magic_name__ : Tuple =["""short"""]
__magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
__magic_name__ : Tuple =["""long"""]
__magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase )
__magic_name__ : List[Any] =[]
answer.update(lowerCamelCase )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
__magic_name__ : Any =True
else:
__magic_name__ : List[str] =False
__magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ):
raise ValueError("""Issue in ID""" , example["""id"""] )
return answer
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : Any =example["""document"""]["""tokens"""]
__magic_name__ : str =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__magic_name__ : Dict =["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__magic_name__ : Tuple =example["""document"""]["""tokens"""]
__magic_name__ : Optional[int] =answer["""start_token"""]
__magic_name__ : List[Any] =answer["""end_token"""]
__magic_name__ : Optional[Any] =[]
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] )
# checking above code
if assertion:
__magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
__magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] )
if new != old:
print("""ID:""" , example["""id"""] )
print("""New:""" , lowerCamelCase , end="""\n""" )
print("""Old:""" , lowerCamelCase , end="""\n\n""" )
return {
"context": " ".join(lowerCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ):
# overlap will be of doc_stride - q_len
__magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase )
__magic_name__ : Union[str, Any] =out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids
__magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__magic_name__ : List[str] =[]
__magic_name__ : int =[]
__magic_name__ : List[str] =input_ids[:q_len]
__magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Tuple =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowerCamelCase ),
"end_token": [-100] * len(lowerCamelCase ),
"category": category,
},
}
__magic_name__ : int =out["""context"""].split()
__magic_name__ : Any =splitted_context[answer["""end_token"""]]
__magic_name__ : str =len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids )
__magic_name__ : Optional[int] =len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
__magic_name__ : Dict =answer["""start_token"""]
__magic_name__ : int =answer["""end_token"""]
if assertion:
__magic_name__ : Any =tokenizer.decode(lowerCamelCase )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" , answer["""span"""] )
print("""NEW:""" , lowerCamelCase , end="""\n\n""" )
if len(lowerCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__magic_name__ : Any =input_ids[:q_len]
__magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride )
__magic_name__ : Any =[]
__magic_name__ : List[str] =[]
__magic_name__ : List[str] =[]
__magic_name__ : str =[] # null, yes, no, long, short
for i in doc_start_indices:
__magic_name__ : List[Any] =i + max_length - q_len
__magic_name__ : Dict =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__magic_name__ : List[Any] =start_token - i + q_len
__magic_name__ : Optional[Any] =end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
__magic_name__ : Optional[Any] =-100
__magic_name__ : Optional[Any] =-100
answers_category.append("""null""" )
__magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase )
answers_end_token.append(lowerCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" , example["""id"""] )
print("""New:""" , tokenizer.decode(lowerCamelCase ) )
print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ):
__magic_name__ : List[Any] =get_strided_contexts_and_ans(
lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , )
return example
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
with jsonlines.open(lowerCamelCase , """a""" ) as writer:
for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ):
__magic_name__ : int =example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions")
UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"]
UpperCAmelCase_ : Optional[int] = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 21 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : List[str] =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : Union[str, Any] =[(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=False ):
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : Optional[Any] =""""""
else:
__magic_name__ : List[Any] ="""vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Tuple =state_dict.pop(F"module.blocks.{i}.attn.qkv.weight" )
__magic_name__ : Optional[Any] =state_dict.pop(F"module.blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Union[str, Any] =in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : Any =in_proj_bias[: config.hidden_size]
__magic_name__ : Any =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Any =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Union[str, Any] =in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : Any =in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : int =["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
__magic_name__ : List[Any] =[
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[Any] =dct.pop(lowerCamelCase )
__magic_name__ : List[str] =val
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =ViTMSNConfig()
__magic_name__ : str =1000
__magic_name__ : Tuple ="""datasets/huggingface/label-files"""
__magic_name__ : Union[str, Any] ="""imagenet-1k-id2label.json"""
__magic_name__ : int =json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase ) , """r""" ) )
__magic_name__ : Dict ={int(lowerCamelCase ): v for k, v in idalabel.items()}
__magic_name__ : int =idalabel
__magic_name__ : List[Any] ={v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__magic_name__ : int =384
__magic_name__ : List[str] =1536
__magic_name__ : Optional[int] =6
elif "l16" in checkpoint_url:
__magic_name__ : Tuple =1024
__magic_name__ : Dict =4096
__magic_name__ : Tuple =24
__magic_name__ : Tuple =16
__magic_name__ : List[str] =0.1
elif "b4" in checkpoint_url:
__magic_name__ : str =4
elif "l7" in checkpoint_url:
__magic_name__ : List[Any] =7
__magic_name__ : Dict =1024
__magic_name__ : Any =4096
__magic_name__ : Union[str, Any] =24
__magic_name__ : Tuple =16
__magic_name__ : Any =0.1
__magic_name__ : List[str] =ViTMSNModel(lowerCamelCase )
__magic_name__ : str =torch.hub.load_state_dict_from_url(lowerCamelCase , map_location="""cpu""" )["""target_encoder"""]
__magic_name__ : Dict =ViTImageProcessor(size=config.image_size )
remove_projection_head(lowerCamelCase )
__magic_name__ : Dict =create_rename_keys(lowerCamelCase , base_model=lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_q_k_v(lowerCamelCase , lowerCamelCase , base_model=lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
__magic_name__ : List[Any] ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
__magic_name__ : Dict =Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
__magic_name__ : Tuple =ViTImageProcessor(
size=config.image_size , image_mean=lowerCamelCase , image_std=lowerCamelCase )
__magic_name__ : Tuple =image_processor(images=lowerCamelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
__magic_name__ : Dict =model(**lowerCamelCase )
__magic_name__ : List[Any] =outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__magic_name__ : Any =torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
__magic_name__ : Optional[int] =torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
__magic_name__ : Tuple =torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
__magic_name__ : Tuple =torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
__magic_name__ : List[Any] =torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , lowerCamelCase , atol=1E-4 )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCAmelCase_ : int = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __A ( UpperCamelCase__ ):
UpperCamelCase = """xlm-roberta-xl"""
def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
__magic_name__ : List[str] =vocab_size
__magic_name__ : List[str] =hidden_size
__magic_name__ : Union[str, Any] =num_hidden_layers
__magic_name__ : Any =num_attention_heads
__magic_name__ : Any =hidden_act
__magic_name__ : List[str] =intermediate_size
__magic_name__ : Any =hidden_dropout_prob
__magic_name__ : Union[str, Any] =attention_probs_dropout_prob
__magic_name__ : Any =max_position_embeddings
__magic_name__ : Any =type_vocab_size
__magic_name__ : List[str] =initializer_range
__magic_name__ : Optional[int] =layer_norm_eps
__magic_name__ : Dict =position_embedding_type
__magic_name__ : Any =use_cache
__magic_name__ : Dict =classifier_dropout
class __A ( UpperCamelCase__ ):
@property
def A__ ( self :Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class __A :
def __init__( self :Optional[Any] , __snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : int =str(id_ )
__magic_name__ : str =None
__magic_name__ : Any =None
__magic_name__ : List[Any] =[]
__magic_name__ : Optional[Any] ={} # {vertex:distance}
def __lt__( self :List[Any] , __snake_case :str ):
'''simple docstring'''
return self.key < other.key
def __repr__( self :List[str] ):
'''simple docstring'''
return self.id
def A__ ( self :List[Any] , __snake_case :List[Any] ):
'''simple docstring'''
self.neighbors.append(__snake_case )
def A__ ( self :Dict , __snake_case :List[Any] , __snake_case :Dict ):
'''simple docstring'''
__magic_name__ : Dict =weight
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowerCamelCase )
graph[b - 1].add_edge(graph[a - 1] , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[str] =[]
for u in graph:
__magic_name__ : Any =math.inf
__magic_name__ : Tuple =None
__magic_name__ : Any =0
__magic_name__ : Tuple =graph[:]
while q:
__magic_name__ : Tuple =min(lowerCamelCase )
q.remove(lowerCamelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
__magic_name__ : Optional[Any] =u
__magic_name__ : int =u.edges[v.id]
for i in range(1 , len(lowerCamelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
for u in graph:
__magic_name__ : Any =math.inf
__magic_name__ : str =None
__magic_name__ : Union[str, Any] =0
__magic_name__ : Union[str, Any] =list(lowerCamelCase )
hq.heapify(lowerCamelCase )
while h:
__magic_name__ : str =hq.heappop(lowerCamelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
__magic_name__ : List[str] =u
__magic_name__ : str =u.edges[v.id]
hq.heapify(lowerCamelCase )
for i in range(1 , len(lowerCamelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def lowerCAmelCase_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__magic_name__ : Dict =F"{src_lang}-{tgt_lang}"
print(F"Converting {dataset}-{pair}" )
__magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__magic_name__ : Optional[int] =F"{dataset}-{pair}"
__magic_name__ : int =Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
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
__magic_name__ : Dict ="""val""" if split == """validation""" else split
__magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" )
__magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" )
__magic_name__ : Optional[Any] =src_path.open("""w+""" )
__magic_name__ : List[Any] =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] ):
__magic_name__ : str =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)
| 21 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Optional[int] =1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__magic_name__ : Union[str, Any] =n - k
# Calculate C(n,k)
for i in range(lowerCamelCase ):
result *= n - i
result //= i + 1
return result
def lowerCAmelCase_ ( lowerCamelCase ):
return binomial_coefficient(2 * node_count , lowerCamelCase ) // (node_count + 1)
def lowerCAmelCase_ ( lowerCamelCase ):
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
__magic_name__ : List[str] =1
for i in range(1 , n + 1 ):
result *= i
return result
def lowerCAmelCase_ ( lowerCamelCase ):
return catalan_number(lowerCamelCase ) * factorial(lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
F"""Given {node_count} nodes, there are {binary_tree_count(node_count)} """
F"""binary trees and {catalan_number(node_count)} binary search trees."""
)
| 21 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =[]
__magic_name__ : List[Any] =11
__magic_name__ : Tuple =int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
__magic_name__ : List[str] =10
return solutions
def lowerCAmelCase_ ( lowerCamelCase = 2 ):
__magic_name__ : str =1.0
for fraction in fraction_list(lowerCamelCase ):
__magic_name__ : int =Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 21 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ : int = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
UpperCAmelCase_ : Any = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
UpperCAmelCase_ : Tuple = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return float((preds == labels).mean() )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase="binary" ):
__magic_name__ : Optional[int] =simple_accuracy(lowerCamelCase , lowerCamelCase )
__magic_name__ : Optional[int] =float(fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average=lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple ={}
for id_pred, label in zip(lowerCamelCase , lowerCamelCase ):
__magic_name__ : List[Any] =F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
__magic_name__ : Any =id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
__magic_name__ : Optional[int] =[(pred, label)]
__magic_name__ , __magic_name__ : Dict =[], []
for question, preds_labels in question_map.items():
__magic_name__ , __magic_name__ : Tuple =zip(*lowerCamelCase )
__magic_name__ : str =fa_score(y_true=lowerCamelCase , y_pred=lowerCamelCase , average="""macro""" )
fas.append(lowerCamelCase )
__magic_name__ : Optional[int] =int(sum(pred == label for pred, label in preds_labels ) == len(lowerCamelCase ) )
ems.append(lowerCamelCase )
__magic_name__ : Any =float(sum(lowerCamelCase ) / len(lowerCamelCase ) )
__magic_name__ : str =sum(lowerCamelCase ) / len(lowerCamelCase )
__magic_name__ : Any =float(fa_score(y_true=lowerCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :str ):
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def A__ ( self :int ):
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def A__ ( self :Union[str, Any] , __snake_case :List[str] , __snake_case :List[Any] ):
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(__snake_case , __snake_case )}
elif self.config_name == "cb":
return acc_and_fa(__snake_case , __snake_case , fa_avg="""macro""" )
elif self.config_name == "record":
__magic_name__ : List[str] =[
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
__magic_name__ : Optional[int] ={pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(__snake_case , __snake_case )[0]
elif self.config_name == "multirc":
return evaluate_multirc(__snake_case , __snake_case )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(__snake_case , __snake_case )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 21 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =sorted(numsa + numsa )
__magic_name__ , __magic_name__ : Optional[Any] =divmod(len(lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Union[str, Any] = [float(x) for x in input("Enter the elements of first array: ").split()]
UpperCAmelCase_ : Dict = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 21 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ):
'''simple docstring'''
super().__init__()
__magic_name__ : Optional[int] =pad_token_id
__magic_name__ : List[Any] =max_length
__magic_name__ : Dict =vocab
__magic_name__ : int =merges
__magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case )
@classmethod
def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()]
__magic_name__ : str =tokenizer.get_vocab()
return cls(__snake_case , __snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ):
'''simple docstring'''
__magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case )
return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case )
@classmethod
def A__ ( cls :Optional[Any] , __snake_case :List[Any] ):
'''simple docstring'''
return cls(**__snake_case )
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case )
__magic_name__ : Tuple =tf.ones_like(__snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
__magic_name__ : Tuple =max_length if max_length is not None else self.max_length
if max_length is not None:
__magic_name__ , __magic_name__ : Tuple =pad_model_inputs(
__snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 21 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
"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 __A ( UpperCamelCase__ ):
UpperCamelCase = """levit"""
def __init__( self :Optional[int] , __snake_case :str=2_24 , __snake_case :Any=3 , __snake_case :List[str]=3 , __snake_case :List[Any]=2 , __snake_case :Optional[Any]=1 , __snake_case :Optional[int]=16 , __snake_case :List[str]=[1_28, 2_56, 3_84] , __snake_case :Dict=[4, 8, 12] , __snake_case :Optional[Any]=[4, 4, 4] , __snake_case :Union[str, Any]=[16, 16, 16] , __snake_case :Any=0 , __snake_case :Dict=[2, 2, 2] , __snake_case :List[Any]=[2, 2, 2] , __snake_case :List[Any]=0.02 , **__snake_case :Optional[int] , ):
'''simple docstring'''
super().__init__(**__snake_case )
__magic_name__ : List[Any] =image_size
__magic_name__ : Optional[int] =num_channels
__magic_name__ : Any =kernel_size
__magic_name__ : Optional[Any] =stride
__magic_name__ : Union[str, Any] =padding
__magic_name__ : Tuple =hidden_sizes
__magic_name__ : str =num_attention_heads
__magic_name__ : Dict =depths
__magic_name__ : Union[str, Any] =key_dim
__magic_name__ : int =drop_path_rate
__magic_name__ : List[Any] =patch_size
__magic_name__ : Dict =attention_ratio
__magic_name__ : List[Any] =mlp_ratio
__magic_name__ : Tuple =initializer_range
__magic_name__ : Any =[
["""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 __A ( UpperCamelCase__ ):
UpperCamelCase = version.parse("""1.11""" )
@property
def A__ ( self :Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self :List[str] ):
'''simple docstring'''
return 1E-4
| 21 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype )
__magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase )
return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype )
__magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ):
__magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase )
return a * tf.math.sigmoid(lowerCamelCase )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCAmelCase_ ( lowerCamelCase ):
return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase )
UpperCAmelCase_ : List[str] = tf.keras.activations.gelu
UpperCAmelCase_ : Dict = approximate_gelu_wrap
else:
UpperCAmelCase_ : Dict = _gelu
UpperCAmelCase_ : str = _gelu_new
UpperCAmelCase_ : Any = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( lowerCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
| 21 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=True ):
model.train()
__magic_name__ : Dict =model(lowerCamelCase )
__magic_name__ : int =F.mse_loss(lowerCamelCase , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
set_seed(42 )
__magic_name__ : Optional[int] =RegressionModel()
__magic_name__ : Optional[Any] =deepcopy(lowerCamelCase )
__magic_name__ : List[str] =RegressionDataset(length=80 )
__magic_name__ : Dict =DataLoader(lowerCamelCase , batch_size=16 )
model.to(accelerator.device )
if sched:
__magic_name__ : Optional[Any] =AdamW(params=model.parameters() , lr=1E-3 )
__magic_name__ : Optional[Any] =AdamW(params=ddp_model.parameters() , lr=1E-3 )
__magic_name__ : Optional[int] =LambdaLR(lowerCamelCase , lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
__magic_name__ : Dict =LambdaLR(lowerCamelCase , lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict =accelerator.prepare(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__magic_name__ , __magic_name__ : int =accelerator.prepare(lowerCamelCase , lowerCamelCase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def lowerCAmelCase_ ( lowerCamelCase ):
# Test when on a single CPU or GPU that the context manager does nothing
__magic_name__ , __magic_name__ , __magic_name__ : Dict =get_training_setup(lowerCamelCase )
# Use a single batch
__magic_name__ , __magic_name__ : Optional[Any] =next(iter(lowerCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__magic_name__ , __magic_name__ : Any =accelerator.gather((ddp_input, ddp_target) )
__magic_name__ , __magic_name__ : str =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowerCamelCase ):
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
# Sync grads
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
__magic_name__ : Dict =ddp_input[torch.randperm(len(lowerCamelCase ) )]
def lowerCAmelCase_ ( lowerCamelCase ):
# Test on distributed setup that context manager behaves properly
__magic_name__ , __magic_name__ , __magic_name__ : Dict =get_training_setup(lowerCamelCase )
# Use a single batch
__magic_name__ , __magic_name__ : Any =next(iter(lowerCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__magic_name__ , __magic_name__ : Tuple =accelerator.gather((ddp_input, ddp_target) )
__magic_name__ , __magic_name__ : Any =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowerCamelCase ):
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
# Sync grads
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
__magic_name__ : Optional[Any] =ddp_input[torch.randperm(len(lowerCamelCase ) )]
def lowerCAmelCase_ ( lowerCamelCase=False , lowerCamelCase=False ):
__magic_name__ : List[Any] =Accelerator(
split_batches=lowerCamelCase , dispatch_batches=lowerCamelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__magic_name__ , __magic_name__ , __magic_name__ : str =get_training_setup(lowerCamelCase )
for iteration, batch in enumerate(lowerCamelCase ):
__magic_name__ , __magic_name__ : List[Any] =batch.values()
# Gather the distributed inputs and targs for the base model
__magic_name__ , __magic_name__ : Optional[Any] =accelerator.gather((ddp_input, ddp_target) )
__magic_name__ , __magic_name__ : str =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(lowerCamelCase ):
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
__magic_name__ : List[Any] =ddp_input[torch.randperm(len(lowerCamelCase ) )]
GradientState._reset_state()
def lowerCAmelCase_ ( lowerCamelCase=False , lowerCamelCase=False ):
__magic_name__ : Union[str, Any] =Accelerator(
split_batches=lowerCamelCase , dispatch_batches=lowerCamelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Any =get_training_setup(lowerCamelCase , lowerCamelCase )
for iteration, batch in enumerate(lowerCamelCase ):
__magic_name__ , __magic_name__ : str =batch.values()
# Gather the distributed inputs and targs for the base model
__magic_name__ , __magic_name__ : List[Any] =accelerator.gather((ddp_input, ddp_target) )
__magic_name__ , __magic_name__ : List[Any] =input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(lowerCamelCase ):
step_model(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
__magic_name__ : Union[str, Any] =(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase ))
if accelerator.num_processes > 1:
check_model_parameters(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
GradientState._reset_state()
def lowerCAmelCase_ ( ):
__magic_name__ : Optional[int] =Accelerator()
__magic_name__ : List[Any] =RegressionDataset(length=80 )
__magic_name__ : Union[str, Any] =DataLoader(lowerCamelCase , batch_size=16 )
__magic_name__ : Tuple =RegressionDataset(length=96 )
__magic_name__ : Dict =DataLoader(lowerCamelCase , batch_size=16 )
__magic_name__ , __magic_name__ : Union[str, Any] =accelerator.prepare(lowerCamelCase , lowerCamelCase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(lowerCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
if iteration < len(lowerCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(lowerCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
if batch_num < len(lowerCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def lowerCAmelCase_ ( ):
__magic_name__ : str =Accelerator()
__magic_name__ : str =accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(lowerCamelCase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(lowerCamelCase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**" , )
test_gradient_accumulation(lowerCamelCase , lowerCamelCase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**" , )
test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( lowerCamelCase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 21 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ):
__magic_name__ : Any =nums[i]
__magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip())
UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 21 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = ["""pixel_values"""]
def __init__( self :Optional[int] , __snake_case :bool = True , __snake_case :Union[int, float] = 1 / 2_55 , __snake_case :bool = True , __snake_case :int = 8 , **__snake_case :int , ):
'''simple docstring'''
super().__init__(**__snake_case )
__magic_name__ : Optional[Any] =do_rescale
__magic_name__ : List[Any] =rescale_factor
__magic_name__ : Dict =do_pad
__magic_name__ : Tuple =pad_size
def A__ ( self :List[str] , __snake_case :np.ndarray , __snake_case :float , __snake_case :Optional[Union[str, ChannelDimension]] = None , **__snake_case :Tuple ):
'''simple docstring'''
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def A__ ( self :List[Any] , __snake_case :np.ndarray , __snake_case :int , __snake_case :Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
__magic_name__ , __magic_name__ : Optional[int] =get_image_size(__snake_case )
__magic_name__ : List[Any] =(old_height // size + 1) * size - old_height
__magic_name__ : Union[str, Any] =(old_width // size + 1) * size - old_width
return pad(__snake_case , ((0, pad_height), (0, pad_width)) , mode="""symmetric""" , data_format=__snake_case )
def A__ ( self :Union[str, Any] , __snake_case :ImageInput , __snake_case :Optional[bool] = None , __snake_case :Optional[float] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[int] = None , __snake_case :Optional[Union[str, TensorType]] = None , __snake_case :Union[str, ChannelDimension] = ChannelDimension.FIRST , **__snake_case :Tuple , ):
'''simple docstring'''
__magic_name__ : List[str] =do_rescale if do_rescale is not None else self.do_rescale
__magic_name__ : str =rescale_factor if rescale_factor is not None else self.rescale_factor
__magic_name__ : Dict =do_pad if do_pad is not None else self.do_pad
__magic_name__ : Union[str, Any] =pad_size if pad_size is not None else self.pad_size
__magic_name__ : int =make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
__magic_name__ : Optional[int] =[to_numpy_array(__snake_case ) for image in images]
if do_rescale:
__magic_name__ : Any =[self.rescale(image=__snake_case , scale=__snake_case ) for image in images]
if do_pad:
__magic_name__ : Optional[Any] =[self.pad(__snake_case , size=__snake_case ) for image in images]
__magic_name__ : Any =[to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
__magic_name__ : Dict ={"""pixel_values""": images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 21 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def __call__( self :Union[str, Any] ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class __A :
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "dict"
UpperCamelCase = None
UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None
__magic_name__ : Optional[int] =len(self.languages ) if self.languages else None
def __call__( self :List[str] ):
'''simple docstring'''
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def A__ ( self :str , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[int] =set(self.languages )
if self.languages and set(__snake_case ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__magic_name__ : Any =[]
for lang, text in translation_dict.items():
if isinstance(__snake_case , __snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) )
return {"language": languages, "translation": translations}
def A__ ( self :List[Any] ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 21 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCAmelCase_ : Union[str, Any] = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
UpperCAmelCase_ : Optional[Any] = (
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode("utf-8").split()
)
UpperCAmelCase_ : int = "|".join(sys.argv[1:])
UpperCAmelCase_ : Union[str, Any] = re.compile(RF"""^({joined_dirs}).*?\.py$""")
UpperCAmelCase_ : List[Any] = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 21 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def A__ ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 21 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
return config
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ):
if conf_path is None:
__magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml"""
__magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase )
__magic_name__ : Tuple =VQModel(**config.model.params )
if ckpt_path is None:
__magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt"""
__magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase )
if ".ckpt" in ckpt_path:
__magic_name__ : Any =sd["""state_dict"""]
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
model.to(lowerCamelCase )
del sd
return model
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__magic_name__ : List[Any] =model.decode(lowerCamelCase )
return xrec
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 )
if reload:
__magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase )
importlib.reload(lowerCamelCase )
return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls )
def lowerCAmelCase_ ( lowerCamelCase ):
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ):
__magic_name__ : str =instantiate_from_config(lowerCamelCase )
if sd is not None:
model.load_state_dict(lowerCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# load the specified checkpoint
if ckpt:
__magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" )
__magic_name__ : Any =pl_sd["""global_step"""]
print(F"loaded model from global step {global_step}." )
else:
__magic_name__ : List[Any] ={"""state_dict""": None}
__magic_name__ : Optional[Any] =None
__magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""]
return model, global_step
| 21 | 1 |
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