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'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : Optional[Any] = abs(lowercase__ )
__a : int = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : List[str] = abs(lowercase__ )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return sum(int(lowercase__ ) for c in str(abs(lowercase__ ) ) )
def lowerCamelCase ():
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_SCREAMING_SNAKE_CASE : Callable , _SCREAMING_SNAKE_CASE : int ) -> None:
__a : Union[str, Any] = F"""{func.__name__}({value})"""
__a : Any = timeit(F"""__main__.{call}""" , setup='import __main__' )
print(F"""{call:56} = {func(lowercase__ )} -- {timing:.4f} seconds""" )
for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(lowercase__ , lowercase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 356
|
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split(',' )
names.sort()
__a : Union[str, Any] = 0
__a : Tuple = 0
for i, name in enumerate(_SCREAMING_SNAKE_CASE ):
for letter in name:
name_score += ord(_SCREAMING_SNAKE_CASE ) - 64
total_score += (i + 1) * name_score
__a : Any = 0
return total_score
if __name__ == "__main__":
print(solution())
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
def count_of_possible_combinations(_SCREAMING_SNAKE_CASE : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(A__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
def count_of_possible_combinations_with_dp_array(
_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__a : str = sum(
count_of_possible_combinations_with_dp_array(target - item , A__ )
for item in array )
__a : Optional[Any] = answer
return answer
__a : Tuple = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(A__ , A__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
__a : Tuple = [0] * (target + 1)
__a : Dict = 1
for i in range(1 , target + 1 ):
for j in range(A__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase : List[Any] = 3
__lowercase : Dict = 5
__lowercase : Union[str, Any] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 357
|
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
| 294
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : Union[str, Any] = jnp.ones((batch_size, length) ) / length
return scores
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = None
__a : List[Any] = 20
__a : List[Any] = self._get_uniform_logits(batch_size=2 , length=__lowerCAmelCase )
# tweak scores to not be uniform anymore
__a : List[Any] = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
__a : Tuple = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
__a : Optional[Any] = jax.nn.softmax(__lowerCAmelCase , axis=-1 )
__a : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
__a : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=1.3 )
__a : Optional[int] = jax.nn.softmax(temp_dist_warper_sharper(__lowerCAmelCase , scores.copy() , cur_len=__lowerCAmelCase ) , axis=-1 )
__a : Optional[int] = jax.nn.softmax(temp_dist_warper_smoother(__lowerCAmelCase , scores.copy() , cur_len=__lowerCAmelCase ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = None
__a : Union[str, Any] = 10
__a : int = 2
# create ramp distribution
__a : Optional[Any] = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, vocab_size) ).copy()
__a : str = ramp_logits[1:, : vocab_size // 2] + vocab_size
__a : Dict = FlaxTopKLogitsWarper(3 )
__a : int = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
__a : List[Any] = 5
__a : int = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
__a : Union[str, Any] = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, length) ).copy()
__a : Optional[int] = top_k_warp_safety_check(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = None
__a : Tuple = 10
__a : List[str] = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
__a : Optional[int] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
__a : List[Any] = FlaxTopPLogitsWarper(0.8 )
__a : Union[str, Any] = np.exp(top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
__a : Tuple = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# check edge cases with negative and extreme logits
__a : Optional[Any] = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
__a : Optional[Any] = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
__a : Tuple = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
__a : Optional[Any] = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = 20
__a : Union[str, Any] = 4
__a : Tuple = 0
__a : str = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
# check that min length is applied at length 5
__a : Dict = ids_tensor((batch_size, 20) , vocab_size=20 )
__a : Optional[Any] = 5
__a : List[str] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : Any = min_dist_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('inf' )] )
# check that min length is not applied anymore at length 15
__a : List[Any] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : int = 15
__a : Optional[Any] = min_dist_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = 20
__a : List[str] = 4
__a : Tuple = 0
__a : Tuple = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
__a : List[str] = ids_tensor((batch_size, 1) , vocab_size=20 )
__a : Dict = 1
__a : Optional[int] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : Tuple = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
__a : int = 3
__a : Dict = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : List[Any] = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = 20
__a : str = 4
__a : Any = 0
__a : Tuple = 5
__a : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
__a : Dict = ids_tensor((batch_size, 4) , vocab_size=20 )
__a : Dict = 4
__a : List[str] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : List[str] = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
__a : Tuple = 3
__a : Tuple = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : Dict = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = 4
__a : List[str] = 10
__a : List[str] = 15
__a : List[str] = 2
__a : Optional[int] = 1
__a : Tuple = 15
# dummy input_ids and scores
__a : List[Any] = ids_tensor((batch_size, sequence_length) , __lowerCAmelCase )
__a : List[Any] = input_ids.copy()
__a : str = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : int = scores.copy()
# instantiate all dist processors
__a : Optional[int] = FlaxTemperatureLogitsWarper(temperature=0.5 )
__a : List[str] = FlaxTopKLogitsWarper(3 )
__a : Optional[int] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
__a : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
__a : Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
__a : str = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
__a : int = 10
# no processor list
__a : Union[str, Any] = temp_dist_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Tuple = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Any = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Union[str, Any] = min_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Dict = bos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Any = eos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# with processor list
__a : Tuple = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
__a : int = processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = 4
__a : Dict = 10
__a : str = 15
__a : str = 2
__a : List[Any] = 1
__a : Union[str, Any] = 15
# dummy input_ids and scores
__a : Union[str, Any] = ids_tensor((batch_size, sequence_length) , __lowerCAmelCase )
__a : List[Any] = input_ids.copy()
__a : str = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
__a : Tuple = scores.copy()
# instantiate all dist processors
__a : Any = FlaxTemperatureLogitsWarper(temperature=0.5 )
__a : List[Any] = FlaxTopKLogitsWarper(3 )
__a : int = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
__a : List[str] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
__a : int = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
__a : Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
__a : Any = 10
# no processor list
def run_no_processor_list(__a , __a , __a ):
__a : Tuple = temp_dist_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : int = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : int = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : int = min_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : str = bos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
__a : Union[str, Any] = eos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
return scores
# with processor list
def run_processor_list(__a , __a , __a ):
__a : Union[str, Any] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
__a : int = processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
return scores
__a : Union[str, Any] = jax.jit(__lowerCAmelCase )
__a : Dict = jax.jit(__lowerCAmelCase )
__a : List[Any] = jitted_run_no_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__a : List[Any] = jitted_run_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 358
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool = False ):
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__a : Any = F"""Expected string as input, found {type(_UpperCamelCase )}"""
raise ValueError(_UpperCamelCase )
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__a : List[Any] = F"""Expected boolean as use_pascal parameter, found {type(_UpperCamelCase )}"""
raise ValueError(_UpperCamelCase )
__a : Any = input_str.split('_' )
__a : int = 0 if use_pascal else 1
__a : List[str] = words[start_index:]
__a : Dict = [word[0].upper() + word[1:] for word in words_to_capitalize]
__a : List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 359
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
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|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
__lowercase : Tuple = False
@skip_mps
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = StableDiffusionAttendAndExcitePipeline
A_ = False
A_ = TEXT_TO_IMAGE_PARAMS
A_ = TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"} )
A_ = TEXT_TO_IMAGE_IMAGE_PARAMS
A_ = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(__lowerCAmelCase )
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(__lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__lowerCAmelCase , )
__a : Dict = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , )
torch.manual_seed(0 )
__a : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__a : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
__a : List[str] = CLIPTextModel(__lowerCAmelCase )
__a : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__a : Dict = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __UpperCAmelCase ( self , __a , __a=0 ):
'''simple docstring'''
if str(__lowerCAmelCase ).startswith('mps' ):
__a : Union[str, Any] = torch.manual_seed(__lowerCAmelCase )
else:
__a : Optional[int] = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
__a : List[Any] = {
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'cpu'
__a : str = self.get_dummy_components()
__a : Optional[Any] = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__a : List[Any] = self.get_dummy_inputs(__lowerCAmelCase )
__a : List[str] = pipe(**__lowerCAmelCase ).images
__a : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
__a : int = np.array(
[0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496] )
__a : List[str] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase , 1E-3 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7E-4 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_save_load_local(expected_max_difference=5E-4 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=4E-4 )
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(__lowerCAmelCase )
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(__lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = torch.manual_seed(51 )
__a : List[Any] = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , safety_checker=__lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to('cuda' )
__a : int = 'a painting of an elephant with glasses'
__a : Optional[Any] = [5, 7]
__a : List[Any] = pipe(
prompt=__lowerCAmelCase , token_indices=__lowerCAmelCase , guidance_scale=7.5 , generator=__lowerCAmelCase , num_inference_steps=5 , max_iter_to_alter=5 , output_type='numpy' , ).images[0]
__a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5E-1
| 360
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = 'laion/clap-htsat-unfused'
__a : Optional[Any] = tempfile.mkdtemp()
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return RobertaTokenizer.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.get_tokenizer()
__a : List[str] = self.get_feature_extractor()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
processor.save_pretrained(self.tmpdirname )
__a : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__a : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__a : List[str] = self.get_feature_extractor(do_normalize=__a , padding_value=1.0 )
__a : Tuple = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : int = self.get_tokenizer()
__a : str = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : int = floats_list((3, 1000) )
__a : str = feature_extractor(__a , return_tensors='np' )
__a : int = processor(audios=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_feature_extractor()
__a : Any = self.get_tokenizer()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Union[str, Any] = 'This is a test string'
__a : Union[str, Any] = processor(text=__a )
__a : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : str = self.get_tokenizer()
__a : List[str] = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Optional[int] = processor.batch_decode(__a )
__a : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_feature_extractor()
__a : Optional[int] = self.get_tokenizer()
__a : int = ClapProcessor(tokenizer=__a , feature_extractor=__a )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 294
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|
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = CustomTokenizer
pass
| 361
|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : List[str] = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : str = hidden_size
__a : List[str] = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Tuple = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[int] = max_position_embeddings
__a : Dict = type_vocab_size
__a : Any = type_sequence_label_size
__a : Dict = initializer_range
__a : Optional[Any] = num_labels
__a : Optional[Any] = num_choices
__a : Union[str, Any] = relative_attention
__a : List[str] = position_biased_input
__a : List[Any] = pos_att_type
__a : Tuple = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : Any = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[int] = None
__a : int = None
__a : Dict = None
if self.use_labels:
__a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__a : str = model(__a , token_type_ids=__a )[0]
__a : Optional[int] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : int = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.num_labels
__a : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__a : Any = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : str = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : str = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__a : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Dict = config_and_inputs
__a : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = DebertaVaModelTester(self )
__a : List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : str = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__a : Optional[Any] = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : int = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__a : str = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : int = 1
__a : str = 2
while i * i <= n:
__a : List[str] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def lowerCamelCase ():
__a : Optional[Any] = 1
__a : Optional[int] = 1
while True:
i += 1
t_num += i
if count_divisors(lowercase_ ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution())
| 362
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294
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'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
__lowercase : Optional[int] = logging.get_logger(__name__)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bool , _SCREAMING_SNAKE_CASE : bool ):
def run_func(_SCREAMING_SNAKE_CASE : Optional[Any] ):
@wraps(UpperCamelCase__ )
def run_in_eager_mode(*_SCREAMING_SNAKE_CASE : int , **_SCREAMING_SNAKE_CASE : Dict ):
return func(*UpperCamelCase__ , **UpperCamelCase__ )
@wraps(UpperCamelCase__ )
@tf.function(experimental_compile=UpperCamelCase__ )
def run_in_graph_mode(*_SCREAMING_SNAKE_CASE : List[Any] , **_SCREAMING_SNAKE_CASE : Tuple ):
return func(*UpperCamelCase__ , **UpperCamelCase__ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
__a : Tuple = random.Random()
__a : Any = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(UpperCamelCase__ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = 42
A_ = 42
A_ = "TensorFlow"
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return tf.__version__
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : Tuple = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
__a : Union[str, Any] = self._prepare_inference_func(_a , _a , _a )
return self._measure_speed(_inference )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : int = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
__a : List[Any] = self._prepare_train_func(_a , _a , _a )
return self._measure_speed(_train )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _a )
__a : Any = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
__a : Optional[Any] = self._prepare_inference_func(_a , _a , _a )
return self._measure_memory(_inference )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _a )
__a : str = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
__a : Optional[int] = self._prepare_train_func(_a , _a , _a )
return self._measure_memory(_train )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : str = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('Mixed precision is currently not supported.' )
__a : Any = (
hasattr(_a , 'architectures' )
and isinstance(config.architectures , _a )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__a : str = 'TF' + config.architectures[0] # prepend 'TF' for tensorflow model
__a : int = __import__('transformers' , fromlist=[model_class] )
__a : Optional[int] = getattr(_a , _a )
__a : Dict = model_cls(_a )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
else:
__a : List[Any] = TF_MODEL_MAPPING[config.__class__](_a )
# encoder-decoder has vocab size saved differently
__a : str = config.vocab_size if hasattr(_a , 'vocab_size' ) else config.encoder.vocab_size
__a : Any = random_input_ids(_a , _a , _a )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(_a , decoder_input_ids=_a , training=_a )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(_a , training=_a )
__a : Any = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : int = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.' )
if self.args.fpaa:
raise NotImplementedError('Mixed precision is currently not supported.' )
__a : Any = (
hasattr(_a , 'architectures' )
and isinstance(config.architectures , _a )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__a : int = 'TF' + config.architectures[0] # prepend 'TF' for tensorflow model
__a : int = __import__('transformers' , fromlist=[model_class] )
__a : Union[str, Any] = getattr(_a , _a )
__a : List[str] = model_cls(_a )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
else:
__a : Optional[int] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_a )
# encoder-decoder has vocab size saved differently
__a : Any = config.vocab_size if hasattr(_a , 'vocab_size' ) else config.encoder.vocab_size
__a : Optional[Any] = random_input_ids(_a , _a , _a )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
__a : Tuple = model(_a , decoder_input_ids=_a , labels=_a , training=_a )[0]
__a : List[str] = tf.gradients(_a , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
__a : Optional[Any] = model(_a , labels=_a , training=_a )[0]
__a : int = tf.gradients(_a , model.trainable_variables )
return gradients
__a : Any = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('Do inference on TPU. Running model 5 times to stabilize compilation' )
timeit.repeat(_a , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
__a : Union[str, Any] = timeit.repeat(
_a , repeat=self.args.repeat , number=10 , )
return min(_a ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
logger.info(
'Note that TensorFlow allocates more memory than '
'it might need to speed up computation. '
'The memory reported here corresponds to the memory '
'reported by `nvidia-smi`, which can vary depending '
'on total available memory on the GPU that is used.' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
' consumption line by line.' )
__a : Dict = start_memory_tracing('transformers' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
' with `args.memory=False`' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'py3nvml not installed, we won\'t log GPU memory usage. '
'Install py3nvml (pip install py3nvml) to log information about GPU.' )
__a : List[str] = 'N/A'
else:
logger.info(
'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
' running on the same GPU.' )
# init nvml
nvml.nvmlInit()
func()
__a : List[str] = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
__a : Any = nvml.nvmlDeviceGetMemoryInfo(_a )
__a : Dict = meminfo.used
__a : Optional[Any] = Memory(_a )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
' TensorFlow.' )
__a : Optional[int] = None
else:
__a : int = measure_peak_memory_cpu(_a )
__a : Dict = Memory(_a ) if isinstance(_a , _a ) else memory_bytes
if self.args.trace_memory_line_by_line:
__a : Tuple = stop_memory_tracing(_a )
if memory is None:
__a : Tuple = summary.total
else:
__a : List[Any] = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 363
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294
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|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ):
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__a : str = update_area_of_max_square(_a , col + 1 )
__a : Tuple = update_area_of_max_square(row + 1 , col + 1 )
__a : Any = update_area_of_max_square(row + 1 , _a )
if mat[row][col]:
__a : Any = 1 + min([right, diagonal, down] )
__a : Optional[Any] = max(largest_square_area[0] , _a )
return sub_problem_sol
else:
return 0
__a : List[Any] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__a : Any = update_area_of_max_square_using_dp_array(_a , col + 1 , _a )
__a : Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , _a )
__a : Optional[int] = update_area_of_max_square_using_dp_array(row + 1 , _a , _a )
if mat[row][col]:
__a : int = 1 + min([right, diagonal, down] )
__a : Tuple = max(largest_square_area[0] , _a )
__a : List[str] = sub_problem_sol
return sub_problem_sol
else:
return 0
__a : Dict = [0]
__a : int = [[-1] * cols for _ in range(_a )]
update_area_of_max_square_using_dp_array(0 , 0 , _a )
return largest_square_area[0]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ):
__a : List[str] = [[0] * (cols + 1) for _ in range(rows + 1 )]
__a : List[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__a : List[str] = dp_array[row][col + 1]
__a : Union[str, Any] = dp_array[row + 1][col + 1]
__a : Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
__a : Dict = 1 + min(_a , _a , _a )
__a : Tuple = max(dp_array[row][col] , _a )
else:
__a : Tuple = 0
return largest_square_area
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ):
__a : Tuple = [0] * (cols + 1)
__a : Union[str, Any] = [0] * (cols + 1)
__a : Tuple = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__a : int = current_row[col + 1]
__a : Dict = next_row[col + 1]
__a : Optional[Any] = next_row[col]
if mat[row][col] == 1:
__a : str = 1 + min(_a , _a , _a )
__a : str = max(current_row[col] , _a )
else:
__a : Optional[int] = 0
__a : str = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 364
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
A_ = 42
A_ = None
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : TreeNode | None ):
# Validation
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__lowercase : List[Any] = 0
__lowercase : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__lowercase : Dict = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__lowercase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : Union[str, Any] = pos_x
__a : int = pos_y
__a : Any = (pos_y, pos_x)
__a : Tuple = goal_x
__a : Optional[int] = goal_y
__a : Dict = g_cost
__a : str = parent
__a : Dict = self.calculate_heuristic()
__a : Optional[int] = self.g_cost + self.h_cost
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.pos_x - self.goal_x
__a : int = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(UpperCAmelCase_ ) + abs(UpperCAmelCase_ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self , __a ):
'''simple docstring'''
return self.f_cost < other.f_cost
class __UpperCamelCase :
def __init__( self , __a , __a ):
'''simple docstring'''
__a : List[str] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , UpperCAmelCase_ )
__a : Any = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , UpperCAmelCase_ )
__a : str = [self.start]
__a : list[Node] = []
__a : List[str] = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
__a : List[Any] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(UpperCAmelCase_ )
self.closed_nodes.append(UpperCAmelCase_ )
__a : List[Any] = self.get_successors(UpperCAmelCase_ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(UpperCAmelCase_ )
else:
# retrieve the best current path
__a : List[str] = self.open_nodes.pop(self.open_nodes.index(UpperCAmelCase_ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(UpperCAmelCase_ )
else:
self.open_nodes.append(UpperCAmelCase_ )
return [self.start.pos]
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : int = []
for action in delta:
__a : Any = parent.pos_x + action[1]
__a : Dict = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
UpperCAmelCase_ , UpperCAmelCase_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , UpperCAmelCase_ , ) )
return successors
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : List[str] = node
__a : Dict = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__a : int = current_node.parent
path.reverse()
return path
class __UpperCamelCase :
def __init__( self , __a , __a ):
'''simple docstring'''
__a : Dict = AStar(UpperCAmelCase_ , UpperCAmelCase_ )
__a : str = AStar(UpperCAmelCase_ , UpperCAmelCase_ )
__a : int = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
__a : List[str] = self.fwd_astar.open_nodes.pop(0 )
__a : str = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
UpperCAmelCase_ , UpperCAmelCase_ )
self.fwd_astar.closed_nodes.append(UpperCAmelCase_ )
self.bwd_astar.closed_nodes.append(UpperCAmelCase_ )
__a : Any = current_bwd_node
__a : Tuple = current_fwd_node
__a : List[str] = {
self.fwd_astar: self.fwd_astar.get_successors(UpperCAmelCase_ ),
self.bwd_astar: self.bwd_astar.get_successors(UpperCAmelCase_ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(UpperCAmelCase_ )
else:
# retrieve the best current path
__a : Optional[Any] = astar.open_nodes.pop(
astar.open_nodes.index(UpperCAmelCase_ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(UpperCAmelCase_ )
else:
astar.open_nodes.append(UpperCAmelCase_ )
return [self.fwd_astar.start.pos]
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.fwd_astar.retrace_path(UpperCAmelCase_ )
__a : Tuple = self.bwd_astar.retrace_path(UpperCAmelCase_ )
bwd_path.pop()
bwd_path.reverse()
__a : Optional[Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__lowercase : Tuple = (0, 0)
__lowercase : Union[str, Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__lowercase : Tuple = time.time()
__lowercase : List[Any] = AStar(init, goal)
__lowercase : int = a_star.search()
__lowercase : List[Any] = time.time() - start_time
print(f'''AStar execution time = {end_time:f} seconds''')
__lowercase : List[str] = time.time()
__lowercase : Tuple = BidirectionalAStar(init, goal)
__lowercase : List[str] = time.time() - bd_start_time
print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 365
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : str = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = [
'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'GraphormerForGraphClassification',
'GraphormerModel',
'GraphormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__lowercase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 366
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
re.sub('<n>' , '' , _SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 294
| 0
|
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list ):
if not nums:
raise ValueError('List is empty' )
return sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__lowercase : Any = [0, 25, 50]
__lowercase : int = [25, 50, 75]
__lowercase : List[str] = fuzz.membership.trimf(X, abca)
__lowercase : Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowercase : List[Any] = np.ones(75)
__lowercase : Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowercase : str = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowercase : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowercase : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowercase : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowercase : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowercase : Union[str, Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
while second != 0:
__a : List[Any] = first & second
first ^= second
__a : Any = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase : Any = int(input('Enter the first number: ').strip())
__lowercase : int = int(input('Enter the second number: ').strip())
print(f'''{add(first, second) = }''')
| 368
|
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = 1
for digit in s:
product *= int(_SCREAMING_SNAKE_CASE )
return product
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = N ):
__a : Optional[int] = -sys.maxsize - 1
__a : Optional[Any] = n[:13]
__a : int = 13
while cur_index < len(_SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__a : List[Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__a : Dict = max(_SCREAMING_SNAKE_CASE , str_eval(_SCREAMING_SNAKE_CASE ) )
__a : Optional[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 294
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Dict = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = [
"FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FocalNetForImageClassification",
"FocalNetForMaskedImageModeling",
"FocalNetBackbone",
"FocalNetModel",
"FocalNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 369
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = CodeGenTokenizer
A_ = CodeGenTokenizerFast
A_ = True
A_ = {"add_prefix_space": True}
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__a : Tuple = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
__a : Union[str, Any] = dict(zip(__a , range(len(__a ) ) ) )
__a : Tuple = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__a : Dict = {'unk_token': '<unk>'}
__a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Tuple = 'lower newer'
__a : Tuple = 'lower newer'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a : str = 'lower newer'
__a : Tuple = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
__a : List[str] = tokens + [tokenizer.unk_token]
__a : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a : List[Any] = self.get_tokenizer()
__a : List[str] = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Any = 'lower newer'
# Testing tokenization
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
__a : Dict = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
__a : int = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__a : Tuple = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
__a : Tuple = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Union[str, Any] = tokenizer.encode(__a , add_prefix_space=__a )
__a : int = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
__a : Any = tokens + [rust_tokenizer.unk_token]
__a : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self , __a=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Optional[int] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
__a : List[Any] = 'This is a simple input'
__a : Tuple = ['This is a simple input 1', 'This is a simple input 2']
__a : Tuple = ('This is a simple input', 'This is a pair')
__a : str = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
__a : str = 'This is a simple input'
__a : Any = ['This is a simple input looooooooong', 'This is a simple input']
__a : Optional[int] = ('This is a simple input', 'This is a pair')
__a : Optional[Any] = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
__a : int = tokenizer.pad_token_id
__a : List[Any] = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
__a : Union[str, Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
__a : Optional[Any] = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
__a : List[Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = '$$$'
__a : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
__a : Union[str, Any] = 'This is a simple input'
__a : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
__a : List[Any] = tokenizer.bos_token_id
__a : List[str] = tokenizer(__a )
__a : Optional[Any] = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__a : Any = tokenizer.decode(out_s.input_ids )
__a : Union[str, Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
__a : Optional[int] = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
__a : Tuple = '\nif len_a > len_b: result = a\nelse: result = b'
__a : Optional[int] = tokenizer.encode(__a )
__a : Union[str, Any] = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
__a : Tuple = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 294
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|
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __UpperCamelCase ( _a ):
A_ = (DPMSolverSDEScheduler,)
A_ = 10
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
__a : List[str] = {
"num_train_timesteps": 1100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"noise_sampler_seed": 0,
}
config.update(**_a )
return config
def __UpperCAmelCase ( self ):
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=_a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.scheduler_classes[0]
__a : str = self.get_scheduler_config()
__a : Tuple = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
__a : List[Any] = self.dummy_model()
__a : str = self.dummy_sample_deter * scheduler.init_noise_sigma
__a : str = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
__a : List[Any] = scheduler.scale_model_input(_a , _a )
__a : Dict = model(_a , _a )
__a : Optional[Any] = scheduler.step(_a , _a , _a )
__a : Optional[int] = output.prev_sample
__a : Optional[Any] = torch.sum(torch.abs(_a ) )
__a : Optional[Any] = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.scheduler_classes[0]
__a : Dict = self.get_scheduler_config(prediction_type='v_prediction' )
__a : Any = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
__a : Union[str, Any] = self.dummy_model()
__a : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
__a : Dict = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
__a : Dict = scheduler.scale_model_input(_a , _a )
__a : str = model(_a , _a )
__a : int = scheduler.step(_a , _a , _a )
__a : List[Any] = output.prev_sample
__a : Optional[Any] = torch.sum(torch.abs(_a ) )
__a : List[Any] = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.scheduler_classes[0]
__a : Union[str, Any] = self.get_scheduler_config()
__a : Dict = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
__a : Any = self.dummy_model()
__a : List[str] = self.dummy_sample_deter.to(_a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__a : Optional[Any] = scheduler.scale_model_input(_a , _a )
__a : List[str] = model(_a , _a )
__a : str = scheduler.step(_a , _a , _a )
__a : Optional[Any] = output.prev_sample
__a : List[str] = torch.sum(torch.abs(_a ) )
__a : Dict = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.scheduler_classes[0]
__a : Optional[Any] = self.get_scheduler_config()
__a : List[Any] = scheduler_class(**_a , use_karras_sigmas=_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
__a : List[str] = self.dummy_model()
__a : List[str] = self.dummy_sample_deter.to(_a ) * scheduler.init_noise_sigma
__a : Optional[int] = sample.to(_a )
for t in scheduler.timesteps:
__a : Tuple = scheduler.scale_model_input(_a , _a )
__a : Any = model(_a , _a )
__a : int = scheduler.step(_a , _a , _a )
__a : int = output.prev_sample
__a : List[Any] = torch.sum(torch.abs(_a ) )
__a : List[str] = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 370
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
__a : Dict = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(_SCREAMING_SNAKE_CASE )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class __UpperCamelCase :
def __init__( self ):
'''simple docstring'''
__a : List[str] = {}
def __UpperCAmelCase ( self , __a , __a , __a=1 ):
'''simple docstring'''
if self.graph.get(snake_case_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
__a : Tuple = [[w, v]]
if not self.graph.get(snake_case_ ):
__a : Optional[Any] = []
def __UpperCAmelCase ( self ):
'''simple docstring'''
return list(self.graph )
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
if self.graph.get(snake_case_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(snake_case_ )
def __UpperCAmelCase ( self , __a=-2 , __a=-1 ):
'''simple docstring'''
if s == d:
return []
__a : Optional[Any] = []
__a : List[Any] = []
if s == -2:
__a : Union[str, Any] = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : Optional[int] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : str = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(snake_case_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__a : Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(snake_case_ ) != 0:
__a : Tuple = stack[len(snake_case_ ) - 1]
else:
__a : Tuple = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return visited
def __UpperCAmelCase ( self , __a=-1 ):
'''simple docstring'''
if c == -1:
__a : Union[str, Any] = floor(random() * 1_0000 ) + 10
for i in range(snake_case_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__a : str = floor(random() * c ) + 1
if n != i:
self.add_pair(snake_case_ , snake_case_ , 1 )
def __UpperCAmelCase ( self , __a=-2 ):
'''simple docstring'''
__a : Tuple = deque()
__a : str = []
if s == -2:
__a : str = list(self.graph )[0]
d.append(snake_case_ )
visited.append(snake_case_ )
while d:
__a : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Optional[int] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
return len(self.graph[u] )
def __UpperCAmelCase ( self , __a=-2 ):
'''simple docstring'''
__a : str = []
__a : Any = []
if s == -2:
__a : Any = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : Dict = s
__a : List[Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : List[str] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__a : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(snake_case_ ) != 0:
__a : Optional[int] = stack[len(snake_case_ ) - 1]
else:
__a : Union[str, Any] = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return sorted_nodes
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = []
__a : Union[str, Any] = []
__a : Optional[int] = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : List[Any] = -2
__a : Union[str, Any] = []
__a : Optional[Any] = s
__a : Optional[Any] = False
__a : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : Tuple = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__a : str = len(snake_case_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__a : Dict = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__a : List[str] = True
if len(snake_case_ ) != 0:
__a : Any = stack[len(snake_case_ ) - 1]
else:
__a : Optional[Any] = False
indirect_parents.append(snake_case_ )
__a : Union[str, Any] = s
__a : str = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return list(snake_case_ )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = []
__a : str = []
__a : Tuple = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : Optional[int] = -2
__a : List[str] = []
__a : Optional[int] = s
__a : str = False
__a : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__a : Optional[Any] = len(snake_case_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__a : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__a : List[str] = True
if len(snake_case_ ) != 0:
__a : List[str] = stack[len(snake_case_ ) - 1]
else:
__a : int = False
indirect_parents.append(snake_case_ )
__a : Any = s
__a : Tuple = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return False
def __UpperCAmelCase ( self , __a=-2 , __a=-1 ):
'''simple docstring'''
__a : List[Any] = time()
self.dfs(snake_case_ , snake_case_ )
__a : Optional[Any] = time()
return end - begin
def __UpperCAmelCase ( self , __a=-2 ):
'''simple docstring'''
__a : Any = time()
self.bfs(snake_case_ )
__a : List[str] = time()
return end - begin
class __UpperCamelCase :
def __init__( self ):
'''simple docstring'''
__a : List[str] = {}
def __UpperCAmelCase ( self , __a , __a , __a=1 ):
'''simple docstring'''
if self.graph.get(snake_case_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
__a : Dict = [[w, v]]
# add the other way
if self.graph.get(snake_case_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
__a : Any = [[w, u]]
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
if self.graph.get(snake_case_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(snake_case_ )
# the other way round
if self.graph.get(snake_case_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(snake_case_ )
def __UpperCAmelCase ( self , __a=-2 , __a=-1 ):
'''simple docstring'''
if s == d:
return []
__a : Dict = []
__a : Optional[int] = []
if s == -2:
__a : Any = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : Optional[int] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : Optional[int] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(snake_case_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__a : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(snake_case_ ) != 0:
__a : str = stack[len(snake_case_ ) - 1]
else:
__a : Union[str, Any] = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return visited
def __UpperCAmelCase ( self , __a=-1 ):
'''simple docstring'''
if c == -1:
__a : Union[str, Any] = floor(random() * 1_0000 ) + 10
for i in range(snake_case_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__a : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(snake_case_ , snake_case_ , 1 )
def __UpperCAmelCase ( self , __a=-2 ):
'''simple docstring'''
__a : Union[str, Any] = deque()
__a : Optional[int] = []
if s == -2:
__a : Tuple = list(self.graph )[0]
d.append(snake_case_ )
visited.append(snake_case_ )
while d:
__a : str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
return len(self.graph[u] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = []
__a : List[str] = []
__a : str = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : Tuple = -2
__a : Optional[int] = []
__a : str = s
__a : int = False
__a : Dict = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__a : Tuple = len(snake_case_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__a : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__a : Optional[Any] = True
if len(snake_case_ ) != 0:
__a : Dict = stack[len(snake_case_ ) - 1]
else:
__a : int = False
indirect_parents.append(snake_case_ )
__a : int = s
__a : Tuple = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return list(snake_case_ )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = []
__a : Tuple = []
__a : Any = list(self.graph )[0]
stack.append(snake_case_ )
visited.append(snake_case_ )
__a : List[Any] = -2
__a : Dict = []
__a : str = s
__a : Optional[Any] = False
__a : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__a : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__a : Optional[int] = len(snake_case_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__a : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__a : Any = True
if len(snake_case_ ) != 0:
__a : Any = stack[len(snake_case_ ) - 1]
else:
__a : Tuple = False
indirect_parents.append(snake_case_ )
__a : Optional[int] = s
__a : List[Any] = ss
# check if se have reached the starting point
if len(snake_case_ ) == 0:
return False
def __UpperCAmelCase ( self ):
'''simple docstring'''
return list(self.graph )
def __UpperCAmelCase ( self , __a=-2 , __a=-1 ):
'''simple docstring'''
__a : int = time()
self.dfs(snake_case_ , snake_case_ )
__a : List[str] = time()
return end - begin
def __UpperCAmelCase ( self , __a=-2 ):
'''simple docstring'''
__a : Optional[int] = time()
self.bfs(snake_case_ )
__a : str = time()
return end - begin
| 371
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , ):
'''simple docstring'''
__a : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__a : int = parent
__a : Dict = batch_size
__a : Optional[int] = num_channels
__a : List[Any] = image_size
__a : Tuple = min_resolution
__a : str = max_resolution
__a : str = do_resize
__a : Optional[Any] = size
__a : str = apply_ocr
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LayoutLMvaImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'apply_ocr' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
__a : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
__a : Any = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__a : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : Tuple = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__a : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : List[str] = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
__a : str = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
__a : Tuple = Image.open(ds[0]['file'] ).convert('RGB' )
__a : Optional[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__a : Optional[Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__a : Union[str, Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
__a : List[Any] = LayoutLMvaImageProcessor(apply_ocr=__a )
__a : List[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(__lowerCAmelCase ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(__lowerCAmelCase ) == 1:
return True
__a : Any = series[1] - series[0]
for index in range(len(__lowerCAmelCase ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(__lowerCAmelCase ) == 0:
raise ValueError('Input list must be a non empty list' )
__a : Any = 0
for val in series:
answer += val
return answer / len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350
|
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "ernie_m"
A_ = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __a = 25_0002 , __a = 768 , __a = 12 , __a = 12 , __a = 3072 , __a = "gelu" , __a = 0.1 , __a = 0.1 , __a = 514 , __a = 0.02 , __a = 1 , __a = 1E-0_5 , __a=None , __a=False , __a=0.0 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
__a : int = vocab_size
__a : Dict = hidden_size
__a : str = num_hidden_layers
__a : Dict = num_attention_heads
__a : List[str] = intermediate_size
__a : Union[str, Any] = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : int = initializer_range
__a : Dict = layer_norm_eps
__a : int = classifier_dropout
__a : Dict = is_decoder
__a : int = act_dropout
| 294
| 0
|
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Optional[int] = botoa.client('iam' )
__a : Optional[Any] = {
'Version': '2012-10-17',
'Statement': [
{'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=_UpperCAmelCase , AssumeRolePolicyDocument=json.dumps(_UpperCAmelCase , indent=2 ) )
__a : Optional[Any] = {
'Version': '2012-10-17',
'Statement': [
{
'Effect': 'Allow',
'Action': [
'sagemaker:*',
'ecr:GetDownloadUrlForLayer',
'ecr:BatchGetImage',
'ecr:BatchCheckLayerAvailability',
'ecr:GetAuthorizationToken',
'cloudwatch:PutMetricData',
'cloudwatch:GetMetricData',
'cloudwatch:GetMetricStatistics',
'cloudwatch:ListMetrics',
'logs:CreateLogGroup',
'logs:CreateLogStream',
'logs:DescribeLogStreams',
'logs:PutLogEvents',
'logs:GetLogEvents',
's3:CreateBucket',
's3:ListBucket',
's3:GetBucketLocation',
's3:GetObject',
's3:PutObject',
],
'Resource': '*',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=_UpperCAmelCase , PolicyName=F"""{role_name}_policy_permission""" , PolicyDocument=json.dumps(_UpperCAmelCase , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F"""role {role_name} already exists. Using existing one""" )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : int = botoa.client('iam' )
return iam_client.get_role(RoleName=_UpperCAmelCase )["Role"]["Arn"]
def lowerCamelCase ():
__a : Optional[Any] = _ask_options(
'How do you want to authorize?' , ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '] , _UpperCAmelCase , )
__a : Optional[Any] = None
if credentials_configuration == 0:
__a : Dict = _ask_field('Enter your AWS Profile name: [default] ' , default='default' )
__a : Union[str, Any] = aws_profile
else:
print(
'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
'`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`' )
__a : str = _ask_field('AWS Access Key ID: ' )
__a : Optional[int] = aws_access_key_id
__a : List[str] = _ask_field('AWS Secret Access Key: ' )
__a : str = aws_secret_access_key
__a : Tuple = _ask_field('Enter your AWS Region: [us-east-1]' , default='us-east-1' )
__a : Any = aws_region
__a : Optional[Any] = _ask_options(
'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?' , ['Provide IAM Role name', 'Create new IAM role using credentials'] , _UpperCAmelCase , )
if role_management == 0:
__a : int = _ask_field('Enter your IAM role name: ' )
else:
__a : List[Any] = 'accelerate_sagemaker_execution_role'
print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials""" )
_create_iam_role_for_sagemaker(_UpperCAmelCase )
__a : Optional[Any] = _ask_field(
'Do you want to use custom Docker image? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
__a : Dict = None
if is_custom_docker_image:
__a : Tuple = _ask_field('Enter your Docker image: ' , lambda _SCREAMING_SNAKE_CASE : str(_UpperCAmelCase ).lower() )
__a : Optional[Any] = _ask_field(
'Do you want to provide SageMaker input channels with data locations? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
__a : int = None
if is_sagemaker_inputs_enabled:
__a : Union[str, Any] = _ask_field(
'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ' , lambda _SCREAMING_SNAKE_CASE : str(_UpperCAmelCase ).lower() , )
__a : str = _ask_field(
'Do you want to enable SageMaker metrics? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
__a : Any = None
if is_sagemaker_metrics_enabled:
__a : List[Any] = _ask_field(
'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ' , lambda _SCREAMING_SNAKE_CASE : str(_UpperCAmelCase ).lower() , )
__a : Tuple = _ask_options(
'What is the distributed mode?' , ['No distributed training', 'Data parallelism'] , _convert_sagemaker_distributed_mode , )
__a : Tuple = {}
__a : Optional[Any] = _ask_field(
'Do you wish to optimize your script with torch dynamo?[yes/NO]:' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
if use_dynamo:
__a : Optional[int] = 'dynamo_'
__a : List[str] = _ask_options(
'Which dynamo backend would you like to use?' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
__a : str = _ask_field(
'Do you want to customize the defaults sent to torch.compile? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
if use_custom_options:
__a : Union[str, Any] = _ask_options(
'Which mode do you want to use?' , _UpperCAmelCase , lambda _SCREAMING_SNAKE_CASE : TORCH_DYNAMO_MODES[int(_UpperCAmelCase )] , default='default' , )
__a : Optional[int] = _ask_field(
'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
__a : List[str] = _ask_field(
'Do you want to enable dynamic shape tracing? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCAmelCase , error_message='Please enter yes or no.' , )
__a : str = 'Which EC2 instance type you want to use for your training?'
if distributed_type != SageMakerDistributedType.NO:
__a : Optional[Any] = _ask_options(
_UpperCAmelCase , _UpperCAmelCase , lambda _SCREAMING_SNAKE_CASE : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_UpperCAmelCase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
__a : List[Any] = _ask_field(_UpperCAmelCase , lambda _SCREAMING_SNAKE_CASE : str(_UpperCAmelCase ).lower() , default='ml.p3.2xlarge' )
__a : Optional[Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
__a : str = _ask_field(
'How many machines do you want use? [1]: ' , _UpperCAmelCase , default=1 , )
__a : str = _ask_options(
'Do you wish to use FP16 or BF16 (mixed precision)?' , ['no', 'fp16', 'bf16', 'fp8'] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.' )
return SageMakerConfig(
image_uri=_UpperCAmelCase , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=_UpperCAmelCase , use_cpu=_UpperCAmelCase , dynamo_config=_UpperCAmelCase , eca_instance_type=_UpperCAmelCase , profile=_UpperCAmelCase , region=_UpperCAmelCase , iam_role_name=_UpperCAmelCase , mixed_precision=_UpperCAmelCase , num_machines=_UpperCAmelCase , sagemaker_inputs_file=_UpperCAmelCase , sagemaker_metrics_file=_UpperCAmelCase , )
| 351
|
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a ):
'''simple docstring'''
super().__init__()
__a : int = module
__a : List[Any] = nn.Sequential(
nn.Linear(module.in_features , __a , bias=__a ) , nn.Linear(__a , module.out_features , bias=__a ) , )
__a : int = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__a )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __UpperCAmelCase ( self , __a , *__a , **__a ):
'''simple docstring'''
return self.module(__a , *__a , **__a ) + self.adapter(__a )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
A_ = "bigscience/bloom-1b7"
# Constant values
A_ = 2.109659552692574
A_ = "Hello my name is"
A_ = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
A_ = 10
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = AutoTokenizer.from_pretrained(self.model_name )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_abit.config
self.assertTrue(hasattr(__a , 'quantization_config' ) )
__a : Union[str, Any] = config.to_dict()
__a : Tuple = config.to_diff_dict()
__a : Tuple = config.to_json_string()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__a : List[Any] = self.model_fpaa.get_memory_footprint()
__a : List[Any] = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__a : Tuple = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__a , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Union[str, Any] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BitsAndBytesConfig()
__a : Tuple = True
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' )
__a : List[Any] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = BitsAndBytesConfig()
with self.assertRaises(__a ):
__a : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , load_in_abit=__a , device_map='auto' , bnb_abit_quant_type='nf4' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(__a ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__a : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Optional[int] = self.model_fpaa.to(torch.floataa )
__a : Tuple = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__a : List[Any] = self.model_fpaa.to('cpu' )
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.half()
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.float()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=__a , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
__a : Any = 't5-small'
__a : Tuple = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
__a : int = AutoTokenizer.from_pretrained(cls.model_name )
__a : Union[str, Any] = 'Translate in German: Hello, my dog is cute'
def __UpperCAmelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__a : Optional[int] = TaForConditionalGeneration._keep_in_fpaa_modules
__a : List[str] = None
# test with `t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : Any = model.generate(**__a )
# test with `flan-t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[Any] = model.generate(**__a )
__a : Optional[int] = modules
def __UpperCAmelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[str] = model.generate(**__a )
# test with `flan-t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : int = model.generate(**__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__a : List[Any] = 'bigscience/bloom-560m'
__a : Union[str, Any] = 't5-small'
# Different types of model
__a : Optional[Any] = AutoModel.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Sequence classification model
__a : Dict = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__a , device_map='auto' )
# CausalLM model
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Seq2seq model
__a : Any = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__a : str = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__a , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__a : List[Any] = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
__a : str = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'facebook/opt-350m'
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__a : Tuple = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__a : Tuple = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__a ) ):
__a : str = LoRALayer(module.q_proj , rank=16 )
__a : str = LoRALayer(module.k_proj , rank=16 )
__a : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__a : List[str] = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__a : int = model.forward(**__a )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__a , __a ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__a , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "gpt2-xl"
A_ = 3.3191854854152187
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|
'''simple docstring'''
class __UpperCamelCase :
"""simple docstring"""
def __init__( self , __a ):
'''simple docstring'''
__a : str = val
__a : Dict = None
__a : str = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.val:
if val < self.val:
if self.left is None:
__a : Optional[Any] = Node(__SCREAMING_SNAKE_CASE )
else:
self.left.insert(__SCREAMING_SNAKE_CASE )
elif val > self.val:
if self.right is None:
__a : List[str] = Node(__SCREAMING_SNAKE_CASE )
else:
self.right.insert(__SCREAMING_SNAKE_CASE )
else:
__a : Any = val
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict ):
# Recursive traversal
if root:
inorder(root.left , _SCREAMING_SNAKE_CASE )
res.append(root.val )
inorder(root.right , _SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
# Build BST
if len(_SCREAMING_SNAKE_CASE ) == 0:
return arr
__a : List[str] = Node(arr[0] )
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
root.insert(arr[i] )
# Traverse BST in order.
__a : List[str] = []
inorder(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 352
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294
| 0
|
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__lowercase = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
__lowercase = '\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'
__lowercase = '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'
__lowercase = '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results["meteor"], 4))\n 0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
import nltk
nltk.download('wordnet' )
if NLTK_VERSION >= version.Version('3.6.5' ):
nltk.download('punkt' )
if NLTK_VERSION >= version.Version('3.6.6' ):
nltk.download('omw-1.4' )
def __UpperCAmelCase ( self , __a , __a , __a=0.9 , __a=3 , __a=0.5 ):
'''simple docstring'''
if NLTK_VERSION >= version.Version('3.6.5' ):
__a : str = [
meteor_score.single_meteor_score(
word_tokenize(__a ) , word_tokenize(__a ) , alpha=__a , beta=__a , gamma=__a )
for ref, pred in zip(__a , __a )
]
else:
__a : Tuple = [
meteor_score.single_meteor_score(__a , __a , alpha=__a , beta=__a , gamma=__a )
for ref, pred in zip(__a , __a )
]
return {"meteor": np.mean(__a )}
| 353
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __UpperCamelCase :
def __init__( self , __a , __a=13 , __a=10 , __a=3 , __a=2 , __a=2 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a="divided_space_time" , __a=None , ):
'''simple docstring'''
__a : Union[str, Any] = parent
__a : Optional[Any] = batch_size
__a : str = image_size
__a : List[Any] = num_channels
__a : Dict = patch_size
__a : List[str] = num_frames
__a : Optional[Any] = is_training
__a : Optional[Any] = use_labels
__a : Optional[int] = hidden_size
__a : List[Any] = num_hidden_layers
__a : Optional[Any] = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Optional[Any] = hidden_act
__a : Optional[int] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Tuple = attention_type
__a : Optional[Any] = initializer_range
__a : Optional[int] = scope
__a : str = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__a : Optional[int] = (image_size // patch_size) ** 2
__a : str = (num_frames) * self.num_patches_per_frame + 1
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__a : Union[str, Any] = None
if self.use_labels:
__a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
__a : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__a : Dict = self.num_labels
return config
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : Union[str, Any] = TimesformerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__a : Any = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = TimesformerForVideoClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__a : Dict = model(_lowerCAmelCase )
# verify the logits shape
__a : Union[str, Any] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , _lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
__a , __a , __a : str = config_and_inputs
__a : Optional[int] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
A_ = (
{"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = TimesformerModelTester(self )
__a : Optional[Any] = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def __UpperCAmelCase ( self , __a , __a , __a=False ):
'''simple docstring'''
__a : Any = copy.deepcopy(_lowerCAmelCase )
if return_labels:
if model_class in get_values(_lowerCAmelCase ):
__a : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowerCAmelCase )
return inputs_dict
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='TimeSformer does not use inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a , __a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[Any] = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a , __a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : int = model_class(_lowerCAmelCase )
__a : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : List[Any] = [*signature.parameters.keys()]
__a : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*_lowerCAmelCase )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Any = TimesformerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__a , __a : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__a : str = True
for model_class in self.all_model_classes:
__a : List[str] = self.model_tester.seq_length
__a : Union[str, Any] = self.model_tester.num_frames
__a : str = True
__a : int = False
__a : Optional[int] = True
__a : int = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__a : Any = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__a : List[str] = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a : str = True
__a : List[str] = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__a : List[Any] = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__a : Any = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__a : List[str] = len(_lowerCAmelCase )
# Check attention is always last and order is fine
__a : Tuple = True
__a : List[str] = True
__a : Optional[int] = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__a : str = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(out_len + 1 , len(_lowerCAmelCase ) )
__a : List[Any] = outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a ):
__a : Tuple = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__a : Union[str, Any] = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__a : Union[str, Any] = outputs.hidden_states
__a : Optional[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__a : List[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__a , __a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[Any] = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : Optional[int] = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def lowerCamelCase ():
__a : Tuple = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
__a : Union[str, Any] = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400' ).to(
_lowerCAmelCase )
__a : Dict = self.default_image_processor
__a : List[str] = prepare_video()
__a : List[Any] = image_processor(video[:8] , return_tensors='pt' ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__a : str = model(**_lowerCAmelCase )
# verify the logits
__a : Dict = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__a : Optional[int] = torch.tensor([-0.3016, -0.7713, -0.4205] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 354
|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowercase : str = logging.get_logger(__name__)
# General docstring
__lowercase : List[str] = 'MobileNetV1Config'
# Base docstring
__lowercase : Tuple = 'google/mobilenet_v1_1.0_224'
__lowercase : List[Any] = [1, 10_24, 7, 7]
# Image classification docstring
__lowercase : int = 'google/mobilenet_v1_1.0_224'
__lowercase : Any = 'tabby, tabby cat'
__lowercase : Dict = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any]=None ):
__a : Dict = {}
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = model.mobilenet_va
else:
__a : List[Any] = model
__a : Dict = 'MobilenetV1/Conv2d_0/'
__a : Dict = backbone.conv_stem.convolution.weight
__a : Optional[Any] = backbone.conv_stem.normalization.bias
__a : int = backbone.conv_stem.normalization.weight
__a : int = backbone.conv_stem.normalization.running_mean
__a : Tuple = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__a : int = i + 1
__a : Dict = i * 2
__a : Dict = backbone.layer[pt_index]
__a : Dict = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__a : Union[str, Any] = pointer.convolution.weight
__a : Optional[Any] = pointer.normalization.bias
__a : Union[str, Any] = pointer.normalization.weight
__a : List[Any] = pointer.normalization.running_mean
__a : Tuple = pointer.normalization.running_var
__a : List[str] = backbone.layer[pt_index + 1]
__a : Optional[Any] = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__a : Optional[int] = pointer.convolution.weight
__a : List[str] = pointer.normalization.bias
__a : Dict = pointer.normalization.weight
__a : Dict = pointer.normalization.running_mean
__a : Optional[int] = pointer.normalization.running_var
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Any = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__a : Optional[int] = model.classifier.weight
__a : List[Any] = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__a : Union[str, Any] = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
__a : List[str] = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Optional[Any] = array
# Build TF to PyTorch weights loading map
__a : Optional[int] = _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
__a : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__a : Optional[Any] = np.transpose(_SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__a : Union[str, Any] = array.squeeze().transpose()
else:
__a : Dict = np.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
__a : List[str] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
tf_weights.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp_1' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/ExponentialMovingAverage' , _SCREAMING_SNAKE_CASE )
logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}""" )
return model
def lowerCamelCase (_SCREAMING_SNAKE_CASE : torch.Tensor , _SCREAMING_SNAKE_CASE : nn.Convad ):
__a , __a : Any = features.shape[-2:]
__a , __a : int = conv_layer.stride
__a , __a : Any = conv_layer.kernel_size
if in_height % stride_height == 0:
__a : int = max(kernel_height - stride_height , 0 )
else:
__a : int = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__a : Any = max(kernel_width - stride_width , 0 )
else:
__a : str = max(kernel_width - (in_width % stride_width) , 0 )
__a : int = pad_along_width // 2
__a : Dict = pad_along_width - pad_left
__a : List[str] = pad_along_height // 2
__a : Union[str, Any] = pad_along_height - pad_top
__a : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'constant' , 0.0 )
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a , __a , __a , __a = 1 , __a = 1 , __a = False , __a = True , __a = True , ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
__a : Dict = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
__a : Union[str, Any] = nn.Convad(
in_channels=__a , out_channels=__a , kernel_size=__a , stride=__a , padding=__a , groups=__a , bias=__a , padding_mode='zeros' , )
if use_normalization:
__a : List[str] = nn.BatchNormad(
num_features=__a , eps=config.layer_norm_eps , momentum=0.9997 , affine=__a , track_running_stats=__a , )
else:
__a : Tuple = None
if use_activation:
if isinstance(__a , __a ):
__a : Tuple = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __a ):
__a : Union[str, Any] = ACTaFN[config.hidden_act]
else:
__a : Dict = config.hidden_act
else:
__a : List[Any] = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.config.tf_padding:
__a : Union[str, Any] = apply_tf_padding(__a , self.convolution )
__a : Union[str, Any] = self.convolution(__a )
if self.normalization is not None:
__a : str = self.normalization(__a )
if self.activation is not None:
__a : Optional[int] = self.activation(__a )
return features
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = MobileNetVaConfig
A_ = load_tf_weights_in_mobilenet_va
A_ = "mobilenet_v1"
A_ = "pixel_values"
A_ = False
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__lowercase : Any = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowercase : Optional[int] = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a = True ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[int] = config
__a : str = 32
__a : Dict = max(int(depth * config.depth_multiplier ) , config.min_depth )
__a : Union[str, Any] = MobileNetVaConvLayer(
__a , in_channels=config.num_channels , out_channels=__a , kernel_size=3 , stride=2 , )
__a : Tuple = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__a : Any = nn.ModuleList()
for i in range(13 ):
__a : Union[str, Any] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__a : List[Any] = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=3 , stride=strides[i] , groups=__a , ) )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=1 , ) )
__a : Optional[int] = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__a : int = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__a : Union[str, Any] = self.conv_stem(__a )
__a : Any = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
__a : List[str] = layer_module(__a )
if output_hidden_states:
__a : List[Any] = all_hidden_states + (hidden_states,)
__a : str = hidden_states
if self.pooler is not None:
__a : Union[str, Any] = torch.flatten(self.pooler(__a ) , start_dim=1 )
else:
__a : int = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__a , pooler_output=__a , hidden_states=__a , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Tuple = config.num_labels
__a : Tuple = MobileNetVaModel(__a )
__a : Optional[int] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__a : Any = nn.Dropout(config.classifier_dropout_prob , inplace=__a )
__a : Any = nn.Linear(__a , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Dict = self.mobilenet_va(__a , output_hidden_states=__a , return_dict=__a )
__a : List[str] = outputs.pooler_output if return_dict else outputs[1]
__a : int = self.classifier(self.dropout(__a ) )
__a : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__a : str = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__a : int = 'single_label_classification'
else:
__a : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__a : Optional[Any] = MSELoss()
if self.num_labels == 1:
__a : List[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__a : Any = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__a : List[str] = CrossEntropyLoss()
__a : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__a : Tuple = BCEWithLogitsLoss()
__a : Optional[int] = loss_fct(__a , __a )
if not return_dict:
__a : List[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__a , logits=__a , hidden_states=outputs.hidden_states , )
| 294
| 0
|
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : Any = '▁'
__lowercase : Union[str, Any] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class __UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
A_ = BertGenerationTokenizer
A_ = False
A_ = True
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
__a : List[str] = BertGenerationTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = '<s>'
__a : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '<pad>' )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 1002 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = BertGenerationTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
__a : Any = tokenizer.tokenize('This is a test' )
self.assertListEqual(_SCREAMING_SNAKE_CASE , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE ) , [285, 46, 10, 170, 382] , )
__a : List[str] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a : Optional[Any] = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__a : List[Any] = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = 'Hello World!'
__a : List[str] = [1_8536, 2260, 101]
self.assertListEqual(_SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE ) )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
__a : Optional[Any] = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE ) )
@require_torch
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
__a : int = list(self.big_tokenizer.get_vocab().keys() )[:10]
__a : Union[str, Any] = ' '.join(_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = self.big_tokenizer.encode_plus(_SCREAMING_SNAKE_CASE , return_tensors='pt' , return_token_type_ids=_SCREAMING_SNAKE_CASE )
__a : Tuple = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] , return_tensors='pt' , return_token_type_ids=_SCREAMING_SNAKE_CASE )
__a : int = BertGenerationConfig()
__a : Dict = BertGenerationEncoder(_SCREAMING_SNAKE_CASE )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_SCREAMING_SNAKE_CASE )
model(**_SCREAMING_SNAKE_CASE )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_SCREAMING_SNAKE_CASE , model_name='google/bert_for_seq_generation_L-24_bbc_encoder' , revision='c817d1fd1be2ffa69431227a1fe320544943d4db' , )
| 355
|
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowercase : str = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "dhaka" , _SCREAMING_SNAKE_CASE : int = 5 ):
__a : Optional[Any] = min(_SCREAMING_SNAKE_CASE , 50 ) # Prevent abuse!
__a : Optional[Any] = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
__a : Tuple = requests.get('https://www.google.com/search' , params=_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE )
__a : Dict = BeautifulSoup(html.text , 'html.parser' )
__a : List[str] = ''.join(
re.findall(r'AF_initDataCallback\(([^<]+)\);' , str(soup.select('script' ) ) ) )
__a : Optional[Any] = json.dumps(_SCREAMING_SNAKE_CASE )
__a : List[str] = json.loads(_SCREAMING_SNAKE_CASE )
__a : List[Any] = re.findall(
r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",' , _SCREAMING_SNAKE_CASE , )
if not matched_google_image_data:
return 0
__a : Tuple = re.sub(
r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]' , '' , str(_SCREAMING_SNAKE_CASE ) , )
__a : Optional[Any] = re.findall(
r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]' , _SCREAMING_SNAKE_CASE , )
for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
__a : List[str] = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Tuple = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Dict = urllib.request.build_opener()
__a : Union[str, Any] = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_SCREAMING_SNAKE_CASE )
__a : List[Any] = F"""query_{query.replace(" " , "_" )}"""
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
_SCREAMING_SNAKE_CASE , F"""{path_name}/original_size_img_{index}.jpg""" )
return index
if __name__ == "__main__":
try:
__lowercase : Optional[int] = download_images_from_google_query(sys.argv[1])
print(f'''{image_count} images were downloaded to disk.''')
except IndexError:
print('Please provide a search term.')
raise
| 294
| 0
|
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __UpperCamelCase ( snake_case_ ):
A_ = "M-CLIP"
def __init__( self , __a=1024 , __a=768 , **__a ):
'''simple docstring'''
__a : Tuple = transformerDimSize
__a : Optional[Any] = imageDimSize
super().__init__(**__a )
class __UpperCamelCase ( snake_case_ ):
A_ = MCLIPConfig
def __init__( self , __a , *__a , **__a ):
'''simple docstring'''
super().__init__(__a , *__a , **__a )
__a : Optional[int] = XLMRobertaModel(__a )
__a : List[str] = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : List[Any] = self.transformer(input_ids=__a , attention_mask=__a )[0]
__a : Optional[Any] = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(__a ), embs
| 356
|
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split(',' )
names.sort()
__a : Union[str, Any] = 0
__a : Tuple = 0
for i, name in enumerate(_SCREAMING_SNAKE_CASE ):
for letter in name:
name_score += ord(_SCREAMING_SNAKE_CASE ) - 64
total_score += (i + 1) * name_score
__a : Any = 0
return total_score
if __name__ == "__main__":
print(solution())
| 294
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Any = {
"configuration_xlm_roberta": [
"XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaConfig",
"XLMRobertaOnnxConfig",
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Optional[int] = ["XLMRobertaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ["XLMRobertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
"XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaForCausalLM",
"XLMRobertaForMaskedLM",
"XLMRobertaForMultipleChoice",
"XLMRobertaForQuestionAnswering",
"XLMRobertaForSequenceClassification",
"XLMRobertaForTokenClassification",
"XLMRobertaModel",
"XLMRobertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = [
"TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMRobertaPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Dict = [
"FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FlaxXLMRobertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
__lowercase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 357
|
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : str = len(A__ )
for i in range(length - 1 ):
__a : Dict = i
for k in range(i + 1 , A__ ):
if collection[k] < collection[least]:
__a : Any = k
if least != i:
__a , __a : Optional[Any] = (collection[i], collection[least])
return collection
if __name__ == "__main__":
__lowercase : Any = input('Enter numbers separated by a comma:\n').strip()
__lowercase : Optional[int] = [int(item) for item in user_input.split(',')]
print(selection_sort(unsorted))
| 358
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = pipeline(
task='zero-shot-audio-classification' , model='hf-internal-testing/tiny-clap-htsat-unfused' )
__a : List[str] = load_dataset('ashraq/esc50' )
__a : str = dataset['train']['audio'][-1]['array']
__a : int = audio_classifier(__a , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__a ) , [{'score': 0.501, 'label': 'Sound of a dog'}, {'score': 0.499, 'label': 'Sound of vaccum cleaner'}] , )
@unittest.skip('No models are available in TF' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = pipeline(
task='zero-shot-audio-classification' , model='laion/clap-htsat-unfused' , )
# This is an audio of a dog
__a : Tuple = load_dataset('ashraq/esc50' )
__a : Tuple = dataset['train']['audio'][-1]['array']
__a : int = audio_classifier(__a , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__a ) , [
{'score': 0.999, 'label': 'Sound of a dog'},
{'score': 0.001, 'label': 'Sound of vaccum cleaner'},
] , )
__a : Optional[Any] = audio_classifier([audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__a ) , [
[
{'score': 0.999, 'label': 'Sound of a dog'},
{'score': 0.001, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
__a : int = audio_classifier(
[audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] , batch_size=5 )
self.assertEqual(
nested_simplify(__a ) , [
[
{'score': 0.999, 'label': 'Sound of a dog'},
{'score': 0.001, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
@unittest.skip('No models are available in TF' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 359
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 360
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = 'laion/clap-htsat-unfused'
__a : Optional[Any] = tempfile.mkdtemp()
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return RobertaTokenizer.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.get_tokenizer()
__a : List[str] = self.get_feature_extractor()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
processor.save_pretrained(self.tmpdirname )
__a : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__a : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__a : List[str] = self.get_feature_extractor(do_normalize=__a , padding_value=1.0 )
__a : Tuple = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : int = self.get_tokenizer()
__a : str = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : int = floats_list((3, 1000) )
__a : str = feature_extractor(__a , return_tensors='np' )
__a : int = processor(audios=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_feature_extractor()
__a : Any = self.get_tokenizer()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Union[str, Any] = 'This is a test string'
__a : Union[str, Any] = processor(text=__a )
__a : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : str = self.get_tokenizer()
__a : List[str] = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Optional[int] = processor.batch_decode(__a )
__a : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_feature_extractor()
__a : Optional[int] = self.get_tokenizer()
__a : int = ClapProcessor(tokenizer=__a , feature_extractor=__a )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 294
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=4 , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : Dict = is_training
__a : Union[str, Any] = use_attention_mask
__a : Any = use_token_type_ids
__a : List[Any] = use_labels
__a : Optional[Any] = vocab_size
__a : Dict = hidden_size
__a : Tuple = num_hidden_layers
__a : Tuple = num_attention_heads
__a : Tuple = intermediate_size
__a : Tuple = hidden_act
__a : int = hidden_dropout_prob
__a : Optional[int] = attention_probs_dropout_prob
__a : str = max_position_embeddings
__a : List[str] = type_vocab_size
__a : List[Any] = type_sequence_label_size
__a : Tuple = initializer_range
__a : str = num_choices
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[str] = None
if self.use_attention_mask:
__a : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__a : Dict = None
if self.use_token_type_ids:
__a : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Dict = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.prepare_config_and_inputs()
__a , __a , __a , __a : int = config_and_inputs
__a : str = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.prepare_config_and_inputs()
__a , __a , __a , __a : Optional[int] = config_and_inputs
__a : List[Any] = True
__a : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__a : Optional[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
class __UpperCamelCase ( __lowercase , unittest.TestCase ):
A_ = True
A_ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = FlaxRobertaModelTester(self )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a : Union[str, Any] = model_class_name.from_pretrained('roberta-base' , from_pt=UpperCAmelCase__ )
__a : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase__ )
| 361
|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : List[str] = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : str = hidden_size
__a : List[str] = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Tuple = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[int] = max_position_embeddings
__a : Dict = type_vocab_size
__a : Any = type_sequence_label_size
__a : Dict = initializer_range
__a : Optional[Any] = num_labels
__a : Optional[Any] = num_choices
__a : Union[str, Any] = relative_attention
__a : List[str] = position_biased_input
__a : List[Any] = pos_att_type
__a : Tuple = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : Any = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[int] = None
__a : int = None
__a : Dict = None
if self.use_labels:
__a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__a : str = model(__a , token_type_ids=__a )[0]
__a : Optional[int] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : int = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.num_labels
__a : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__a : Any = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : str = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : str = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__a : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Dict = config_and_inputs
__a : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = DebertaVaModelTester(self )
__a : List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : str = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__a : Optional[Any] = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : int = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__a : str = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 294
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|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : Optional[Any] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"processing_pix2struct": ["Pix2StructProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[Any] = ["Pix2StructImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Optional[Any] = [
"PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Pix2StructPreTrainedModel",
"Pix2StructForConditionalGeneration",
"Pix2StructVisionModel",
"Pix2StructTextModel",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__lowercase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 362
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294
| 0
|
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( _A , unittest.TestCase ):
A_ = LEDTokenizer
A_ = LEDTokenizerFast
A_ = True
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
__a : int = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
__a : Any = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
__a : str = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__a : Union[str, Any] = {'unk_token': '<unk>'}
__a : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__SCREAMING_SNAKE_CASE ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LEDTokenizer.from_pretrained('allenai/led-base-16384' )
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LEDTokenizerFast.from_pretrained('allenai/led-base-16384' )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__a : Optional[Any] = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Union[str, Any] = tokenizer(__SCREAMING_SNAKE_CASE , max_length=len(__SCREAMING_SNAKE_CASE ) , padding=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
__a : Optional[int] = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : List[str] = tokenizer(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertIn('input_ids' , __SCREAMING_SNAKE_CASE )
self.assertIn('attention_mask' , __SCREAMING_SNAKE_CASE )
self.assertNotIn('labels' , __SCREAMING_SNAKE_CASE )
self.assertNotIn('decoder_attention_mask' , __SCREAMING_SNAKE_CASE )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = [
'Summary of the text.',
'Another summary.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Tuple = tokenizer(text_target=__SCREAMING_SNAKE_CASE , max_length=32 , padding='max_length' , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Tuple = tokenizer(
['I am a small frog' * 1024, 'I am a small frog'] , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual(batch.input_ids.shape , (2, 5122) )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = ['A long paragraph for summarization.']
__a : List[Any] = [
'Summary of the text.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Any = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='pt' )
__a : int = tokenizer(text_target=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
__a : Dict = inputs['input_ids']
__a : Any = targets['input_ids']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def __UpperCAmelCase ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Optional[Any] = ['Summary of the text.', 'Another summary.']
__a : List[str] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
__a : List[str] = tokenizer(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = [[0] * len(__SCREAMING_SNAKE_CASE ) for x in encoded_output['input_ids']]
__a : Dict = tokenizer.pad(__SCREAMING_SNAKE_CASE )
self.assertSequenceEqual(outputs['global_attention_mask'] , __SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : int = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a : Tuple = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a : Optional[int] = 'A, <mask> AllenNLP sentence.'
__a : Optional[int] = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE )
__a : int = tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE )
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
__a : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
__a : List[str] = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
| 363
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294
| 0
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __UpperCamelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Dict = [self.constructed_objects[key_node] for key_node, _ in node.value]
__a : Optional[int] = [tuple(_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else key for key in keys]
__a : Dict = Counter(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}""" )
def __UpperCAmelCase ( self , __a , __a=False ):
'''simple docstring'''
__a : Any = super().construct_mapping(_SCREAMING_SNAKE_CASE , deep=_SCREAMING_SNAKE_CASE )
self._check_no_duplicates_on_constructed_node(_SCREAMING_SNAKE_CASE )
return mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
__a : List[str] = full_content[1:].index('---' ) + 1
__a : str = "\n".join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(__snake_case )
class __UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
A_ = {"train_eval_index"} # train-eval-index in the YAML metadata
@classmethod
def __UpperCAmelCase ( cls , __a ):
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as readme_file:
__a : Dict = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(_SCREAMING_SNAKE_CASE )
else:
return cls()
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if path.exists():
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as readme_file:
__a : List[Any] = readme_file.read()
else:
__a : Optional[int] = None
__a : int = self._to_readme(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self , __a = None ):
'''simple docstring'''
if readme_content is not None:
__a : Optional[Any] = _split_yaml_from_readme(_SCREAMING_SNAKE_CASE )
__a : List[Any] = "---\n" + self.to_yaml_string() + "---\n" + content
else:
__a : str = "---\n" + self.to_yaml_string() + "---\n"
return full_content
@classmethod
def __UpperCAmelCase ( cls , __a ):
'''simple docstring'''
__a : Tuple = yaml.load(_SCREAMING_SNAKE_CASE , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
__a : Union[str, Any] = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=_SCREAMING_SNAKE_CASE , allow_unicode=_SCREAMING_SNAKE_CASE , encoding='utf-8' , ).decode('utf-8' )
__lowercase : int = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__lowercase : Union[str, Any] = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
__lowercase : Optional[Any] = ap.parse_args()
__lowercase : Any = Path(args.readme_filepath)
__lowercase : int = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 364
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
A_ = 42
A_ = None
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : TreeNode | None ):
# Validation
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dataset_infos.json'],
['full:README.md'],
] , )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
__a : List[str] = tmp_path_factory.mktemp('dset_infos_dir' )
if "full:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('---\ndataset_info:\n dataset_size: 42\n---' )
if "empty:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / 'dataset_infos.json' , 'w' ) as f:
f.write('{\"default\": {\"dataset_size\": 42}}' )
__a : int = DatasetInfosDict.from_directory(_a )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'dataset_info' , [
DatasetInfo(),
DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , ),
] , )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : DatasetInfo ) -> Optional[int]:
__a : Optional[Any] = str(_a )
dataset_info.write_to_directory(_a )
__a : Tuple = DatasetInfo.from_directory(_a )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(_a , 'dataset_info.json' ) )
def lowerCamelCase () -> Tuple:
__a : List[Any] = DatasetInfo(
description='foo' , citation='bar' , homepage='https://foo.bar' , license='CC0' , features=Features({'a': Value('int32' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train', 'num_examples': 42}] , download_checksums={} , download_size=1_337 , post_processing_size=442 , dataset_size=1_234 , size_in_bytes=1_337 + 442 + 1_234 , )
__a : Optional[Any] = dataset_info._to_yaml_dict()
assert sorted(_a ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__a : List[Any] = yaml.safe_dump(_a )
__a : Any = yaml.safe_load(_a )
assert dataset_info_yaml_dict == reloaded
def lowerCamelCase () -> List[str]:
__a : List[Any] = DatasetInfo()
__a : Any = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'dataset_infos_dict' , [
DatasetInfosDict(),
DatasetInfosDict({'default': DatasetInfo()} ),
DatasetInfosDict({'my_config_name': DatasetInfo()} ),
DatasetInfosDict(
{
'default': DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'v1': DatasetInfo(dataset_size=42 ),
'v2': DatasetInfo(dataset_size=1_337 ),
} ),
] , )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : DatasetInfosDict ) -> List[Any]:
__a : Dict = str(_a )
dataset_infos_dict.write_to_directory(_a )
__a : Any = DatasetInfosDict.from_directory(_a )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__a : int = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__a : Optional[Any] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(_a , 'README.md' ) )
| 365
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294
| 0
|
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__lowercase : Any = datasets.utils.logging.get_logger(__name__)
__lowercase : int = ["""names""", """prefix"""]
__lowercase : Union[str, Any] = ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__lowercase : List[Any] = ["""encoding_errors""", """on_bad_lines"""]
__lowercase : Dict = ["""date_format"""]
@dataclass
class __UpperCamelCase ( datasets.BuilderConfig ):
A_ = ","
A_ = None
A_ = "infer"
A_ = None
A_ = None
A_ = None
A_ = None
A_ = None
A_ = True
A_ = None
A_ = None
A_ = None
A_ = None
A_ = False
A_ = None
A_ = None
A_ = None
A_ = True
A_ = True
A_ = False
A_ = True
A_ = None
A_ = "."
A_ = None
A_ = '"'
A_ = 0
A_ = None
A_ = None
A_ = None
A_ = None
A_ = True
A_ = True
A_ = 0
A_ = True
A_ = False
A_ = None
A_ = 10000
A_ = None
A_ = "strict"
A_ = "error"
A_ = None
def __UpperCAmelCase ( self ):
'''simple docstring'''
if self.delimiter is not None:
__a : Optional[Any] = self.delimiter
if self.column_names is not None:
__a : Any = self.column_names
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = {
'sep': self.sep,
'header': self.header,
'names': self.names,
'index_col': self.index_col,
'usecols': self.usecols,
'prefix': self.prefix,
'mangle_dupe_cols': self.mangle_dupe_cols,
'engine': self.engine,
'converters': self.converters,
'true_values': self.true_values,
'false_values': self.false_values,
'skipinitialspace': self.skipinitialspace,
'skiprows': self.skiprows,
'nrows': self.nrows,
'na_values': self.na_values,
'keep_default_na': self.keep_default_na,
'na_filter': self.na_filter,
'verbose': self.verbose,
'skip_blank_lines': self.skip_blank_lines,
'thousands': self.thousands,
'decimal': self.decimal,
'lineterminator': self.lineterminator,
'quotechar': self.quotechar,
'quoting': self.quoting,
'escapechar': self.escapechar,
'comment': self.comment,
'encoding': self.encoding,
'dialect': self.dialect,
'error_bad_lines': self.error_bad_lines,
'warn_bad_lines': self.warn_bad_lines,
'skipfooter': self.skipfooter,
'doublequote': self.doublequote,
'memory_map': self.memory_map,
'float_precision': self.float_precision,
'chunksize': self.chunksize,
'encoding_errors': self.encoding_errors,
'on_bad_lines': self.on_bad_lines,
'date_format': self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , _lowercase ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class __UpperCamelCase ( datasets.ArrowBasedBuilder ):
A_ = CsvConfig
def __UpperCAmelCase ( self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
__a : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowercase , (str, list, tuple) ):
__a : str = data_files
if isinstance(_lowercase , _lowercase ):
__a : List[Any] = [files]
__a : Union[str, Any] = [dl_manager.iter_files(_lowercase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
__a : Optional[int] = []
for split_name, files in data_files.items():
if isinstance(_lowercase , _lowercase ):
__a : Any = [files]
__a : Optional[Any] = [dl_manager.iter_files(_lowercase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowercase , gen_kwargs={'files': files} ) )
return splits
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.config.features is not None:
__a : Dict = self.config.features.arrow_schema
if all(not require_storage_cast(_lowercase ) for feature in self.config.features.values() ):
# cheaper cast
__a : str = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=_lowercase )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
__a : Tuple = table_cast(_lowercase , _lowercase )
return pa_table
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Tuple = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
__a : Union[str, Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(_lowercase ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase ) ):
__a : Any = pd.read_csv(_lowercase , iterator=_lowercase , dtype=_lowercase , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(_lowercase ):
__a : Dict = pa.Table.from_pandas(_lowercase )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowercase )
except ValueError as e:
logger.error(f"""Failed to read file '{file}' with error {type(_lowercase )}: {e}""" )
raise
| 366
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
re.sub('<n>' , '' , _SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 294
| 0
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase_ , unittest.TestCase ):
A_ = GPTSanJapaneseTokenizer
A_ = False
A_ = {"""do_clean_text""": False, """add_prefix_space""": False}
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# fmt: off
__a : List[str] = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
__a : Any = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
__a : Optional[Any] = {'unk_token': '<unk>'}
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(_UpperCAmelCase ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Any = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
__a : Union[str, Any] = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a , __a : List[str] = self.get_input_output_texts(_UpperCAmelCase )
__a : int = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
__a : List[str] = tokenizer.decode(_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase )
return text, ids
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass # TODO add if relevant
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass # TODO add if relevant
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass # TODO add if relevant
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.get_tokenizer()
# Testing tokenization
__a : int = 'こんにちは、世界。 こんばんは、㔺界。'
__a : Union[str, Any] = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
__a : str = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing conversion to ids without special tokens
__a : List[Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
__a : List[Any] = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing conversion to ids with special tokens
__a : int = tokens + [tokenizer.unk_token]
__a : Tuple = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
__a : Dict = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.get_tokenizer()
# Testing tokenization
__a : Optional[Any] = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
__a : Tuple = 'こんにちは、、、、世界。こんばんは、、、、世界。'
__a : Union[str, Any] = tokenizer.encode(_UpperCAmelCase )
__a : Optional[Any] = tokenizer.decode(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
__a : int = 'こんにちは、世界。'
__a : str = 'こんばんは、㔺界。😀'
__a : Optional[int] = 'こんにちは、世界。こんばんは、世界。😀'
__a : List[Any] = tokenizer.encode(prefix_text + input_text )
__a : Union[str, Any] = tokenizer.encode('' , prefix_text=prefix_text + input_text )
__a : Tuple = tokenizer.encode(_UpperCAmelCase , prefix_text=_UpperCAmelCase )
__a : Dict = tokenizer.decode(_UpperCAmelCase )
__a : List[str] = tokenizer.decode(_UpperCAmelCase )
__a : Dict = tokenizer.decode(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
__a : Optional[Any] = 'こんにちは、世界。'
__a : Optional[Any] = 'こんばんは、㔺界。😀'
__a : str = len(tokenizer.encode(_UpperCAmelCase ) ) - 2
__a : Optional[Any] = len(tokenizer.encode(_UpperCAmelCase ) ) - 2
__a : int = [1] + [0] * (len_prefix + len_text + 1)
__a : Union[str, Any] = [1] * (len_prefix + len_text + 1) + [0]
__a : Optional[Any] = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
__a : Any = tokenizer(prefix_text + input_text ).token_type_ids
__a : List[Any] = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
__a : str = tokenizer(_UpperCAmelCase , prefix_text=_UpperCAmelCase ).token_type_ids
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
__a : Optional[int] = tokenizer.encode('あンいワ' )
__a : Tuple = tokenizer.encode('' , prefix_text='あンいワ' )
__a : List[str] = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(_UpperCAmelCase ) , tokenizer.decode(_UpperCAmelCase ) )
self.assertEqual(tokenizer.decode(_UpperCAmelCase ) , tokenizer.decode(_UpperCAmelCase ) )
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
__a : Optional[Any] = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
__a : Optional[int] = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase )
__a : Optional[int] = tokenizer.batch_encode_plus(_UpperCAmelCase , padding=_UpperCAmelCase )
# fmt: off
__a : Optional[Any] = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
__a : Union[str, Any] = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
__a : List[str] = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , _UpperCAmelCase )
self.assertListEqual(x_token.token_type_ids , _UpperCAmelCase )
self.assertListEqual(x_token.attention_mask , _UpperCAmelCase )
self.assertListEqual(x_token_a.input_ids , _UpperCAmelCase )
self.assertListEqual(x_token_a.token_type_ids , _UpperCAmelCase )
self.assertListEqual(x_token_a.attention_mask , _UpperCAmelCase )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 367
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__lowercase : Any = [0, 25, 50]
__lowercase : int = [25, 50, 75]
__lowercase : List[str] = fuzz.membership.trimf(X, abca)
__lowercase : Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowercase : List[Any] = np.ones(75)
__lowercase : Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowercase : str = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowercase : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowercase : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowercase : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowercase : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowercase : Union[str, Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 294
| 0
|
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __UpperCamelCase ( _a ):
A_ = 42
A_ = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.26.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version('>=', '0.0.12')
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __UpperCamelCase ( _a ):
A_ = 42
A_ = 42
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 368
|
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = 1
for digit in s:
product *= int(_SCREAMING_SNAKE_CASE )
return product
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = N ):
__a : Optional[int] = -sys.maxsize - 1
__a : Optional[Any] = n[:13]
__a : int = 13
while cur_index < len(_SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__a : List[Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__a : Dict = max(_SCREAMING_SNAKE_CASE , str_eval(_SCREAMING_SNAKE_CASE ) )
__a : Optional[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 294
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = UnCLIPImageVariationPipeline
A_ = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
A_ = IMAGE_VARIATION_BATCH_PARAMS
A_ = [
"generator",
"return_dict",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
A_ = False
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 32
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 32
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 100
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(lowercase_ )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowercase_ )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : List[str] = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
__a : Optional[int] = UnCLIPTextProjModel(**lowercase_ )
return model
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Any = {
'sample_size': 32,
# RGB in channels
'in_channels': 3,
# Out channels is double in channels because predicts mean and variance
'out_channels': 6,
'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,
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': 'identity',
}
__a : List[str] = UNetaDConditionModel(**lowercase_ )
return model
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Union[str, Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(1 )
__a : Union[str, Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.dummy_decoder
__a : int = self.dummy_text_proj
__a : List[Any] = self.dummy_text_encoder
__a : str = self.dummy_tokenizer
__a : Tuple = self.dummy_super_res_first
__a : List[Any] = self.dummy_super_res_last
__a : List[str] = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1000 , )
__a : Optional[int] = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1000 , )
__a : List[str] = CLIPImageProcessor(crop_size=32 , size=32 )
__a : Optional[Any] = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __UpperCAmelCase ( self , __a , __a=0 , __a=True ):
'''simple docstring'''
__a : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith('mps' ):
__a : Tuple = torch.manual_seed(lowercase_ )
else:
__a : int = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
if pil_image:
__a : int = input_image * 0.5 + 0.5
__a : Optional[Any] = input_image.clamp(0 , 1 )
__a : List[Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__a : List[Any] = DiffusionPipeline.numpy_to_pil(lowercase_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = 'cpu'
__a : List[Any] = self.get_dummy_components()
__a : str = self.pipeline_class(**lowercase_ )
__a : Dict = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
__a : str = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : str = pipe(**lowercase_ )
__a : str = output.images
__a : Tuple = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : List[str] = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
__a : List[Any] = image[0, -3:, -3:, -1]
__a : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a : Dict = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
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 __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = 'cpu'
__a : str = self.get_dummy_components()
__a : List[Any] = self.pipeline_class(**lowercase_ )
__a : Optional[int] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
__a : str = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : List[str] = pipe(**lowercase_ )
__a : List[Any] = output.images
__a : int = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : Dict = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
__a : Any = image[0, -3:, -3:, -1]
__a : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a : List[str] = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
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 __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = 'cpu'
__a : Optional[int] = self.get_dummy_components()
__a : List[str] = self.pipeline_class(**lowercase_ )
__a : str = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
__a : Optional[Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : List[Any] = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
__a : str = pipe(**lowercase_ )
__a : Any = output.images
__a : str = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : List[str] = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
__a : List[Any] = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
__a : int = image[0, -3:, -3:, -1]
__a : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
__a : str = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
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 __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = torch.device('cpu' )
class __UpperCamelCase :
A_ = 1
__a : int = self.get_dummy_components()
__a : Optional[int] = self.pipeline_class(**lowercase_ )
__a : Tuple = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
__a : Union[str, Any] = torch.Generator(device=lowercase_ ).manual_seed(0 )
__a : Union[str, Any] = pipe.decoder.dtype
__a : Optional[int] = 1
__a : int = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
__a : str = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
__a : Optional[int] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
__a : Tuple = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
__a : Any = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
__a : Dict = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ ).images
__a : List[Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
# Don't pass image, instead pass embedding
__a : List[str] = pipeline_inputs.pop('image' )
__a : Tuple = pipe.image_encoder(lowercase_ ).image_embeds
__a : Tuple = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ , image_embeddings=lowercase_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
__a : Dict = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowercase_ , expected_max_diff=lowercase_ )
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = torch_device == 'cpu'
__a : Tuple = True
__a : Optional[int] = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=lowercase_ , relax_max_difference=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
__a : Optional[int] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowercase_ )
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
__a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )
__a : str = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa )
__a : str = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
__a : Union[str, Any] = torch.Generator(device='cpu' ).manual_seed(0 )
__a : List[str] = pipeline(
lowercase_ , generator=lowercase_ , output_type='np' , )
__a : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ , 15 )
| 369
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = CodeGenTokenizer
A_ = CodeGenTokenizerFast
A_ = True
A_ = {"add_prefix_space": True}
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__a : Tuple = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
__a : Union[str, Any] = dict(zip(__a , range(len(__a ) ) ) )
__a : Tuple = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__a : Dict = {'unk_token': '<unk>'}
__a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Tuple = 'lower newer'
__a : Tuple = 'lower newer'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a : str = 'lower newer'
__a : Tuple = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
__a : List[str] = tokens + [tokenizer.unk_token]
__a : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a : List[Any] = self.get_tokenizer()
__a : List[str] = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Any = 'lower newer'
# Testing tokenization
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
__a : Dict = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
__a : int = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__a : Tuple = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
__a : Tuple = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Union[str, Any] = tokenizer.encode(__a , add_prefix_space=__a )
__a : int = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
__a : Any = tokens + [rust_tokenizer.unk_token]
__a : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self , __a=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Optional[int] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
__a : List[Any] = 'This is a simple input'
__a : Tuple = ['This is a simple input 1', 'This is a simple input 2']
__a : Tuple = ('This is a simple input', 'This is a pair')
__a : str = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
__a : str = 'This is a simple input'
__a : Any = ['This is a simple input looooooooong', 'This is a simple input']
__a : Optional[int] = ('This is a simple input', 'This is a pair')
__a : Optional[Any] = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
__a : int = tokenizer.pad_token_id
__a : List[Any] = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
__a : Union[str, Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
__a : Optional[Any] = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
__a : List[Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = '$$$'
__a : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
__a : Union[str, Any] = 'This is a simple input'
__a : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
__a : List[Any] = tokenizer.bos_token_id
__a : List[str] = tokenizer(__a )
__a : Optional[Any] = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__a : Any = tokenizer.decode(out_s.input_ids )
__a : Union[str, Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
__a : Optional[int] = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
__a : Tuple = '\nif len_a > len_b: result = a\nelse: result = b'
__a : Optional[int] = tokenizer.encode(__a )
__a : Union[str, Any] = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
__a : Tuple = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 294
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : int = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
'uclanlp/visualbert-vqa-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
'uclanlp/visualbert-vcr-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __UpperCamelCase ( a__ ):
A_ = "visual_bert"
def __init__( self , __a=3_0522 , __a=768 , __a=512 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=False , __a=True , __a=1 , __a=0 , __a=2 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Optional[int] = vocab_size
__a : str = max_position_embeddings
__a : str = hidden_size
__a : Union[str, Any] = visual_embedding_dim
__a : Any = num_hidden_layers
__a : Union[str, Any] = num_attention_heads
__a : Optional[Any] = intermediate_size
__a : List[Any] = hidden_act
__a : Optional[int] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : List[Any] = initializer_range
__a : List[str] = type_vocab_size
__a : str = layer_norm_eps
__a : List[str] = bypass_transformer
__a : Optional[int] = special_visual_initialize
| 370
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
__a : Dict = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(_SCREAMING_SNAKE_CASE )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Union[str, Any] = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
A_ = "nat"
A_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , __a=4 , __a=3 , __a=64 , __a=[3, 4, 6, 5] , __a=[2, 4, 8, 16] , __a=7 , __a=3.0 , __a=True , __a=0.0 , __a=0.0 , __a=0.1 , __a="gelu" , __a=0.02 , __a=1E-5 , __a=0.0 , __a=None , __a=None , **__a , ):
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = patch_size
__a : Optional[int] = num_channels
__a : Any = embed_dim
__a : List[Any] = depths
__a : str = len(_SCREAMING_SNAKE_CASE )
__a : List[Any] = num_heads
__a : Optional[int] = kernel_size
__a : int = mlp_ratio
__a : Optional[int] = qkv_bias
__a : Union[str, Any] = hidden_dropout_prob
__a : List[str] = attention_probs_dropout_prob
__a : Dict = drop_path_rate
__a : str = hidden_act
__a : Any = layer_norm_eps
__a : Dict = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__a : int = int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE ) - 1) )
__a : Optional[Any] = layer_scale_init_value
__a : Union[str, Any] = ['''stem'''] + [f"""stage{idx}""" for idx in range(1 , len(_SCREAMING_SNAKE_CASE ) + 1 )]
__a : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=_SCREAMING_SNAKE_CASE , out_indices=_SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
| 371
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , ):
'''simple docstring'''
__a : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__a : int = parent
__a : Dict = batch_size
__a : Optional[int] = num_channels
__a : List[Any] = image_size
__a : Tuple = min_resolution
__a : str = max_resolution
__a : str = do_resize
__a : Optional[Any] = size
__a : str = apply_ocr
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LayoutLMvaImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'apply_ocr' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
__a : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
__a : Any = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__a : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : Tuple = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__a : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : List[str] = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
__a : str = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
__a : Tuple = Image.open(ds[0]['file'] ).convert('RGB' )
__a : Optional[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__a : Optional[Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__a : Union[str, Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
__a : List[Any] = LayoutLMvaImageProcessor(apply_ocr=__a )
__a : List[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 294
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__lowercase : str = None
__lowercase : Optional[Any] = logging.get_logger(__name__)
__lowercase : Optional[int] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__lowercase : Union[str, Any] = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
__lowercase : List[str] = {
"camembert-base": 5_12,
}
__lowercase : int = "▁"
class __UpperCamelCase ( a_ ):
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ["input_ids", "attention_mask"]
A_ = CamembertTokenizer
def __init__( self , __a=None , __a=None , __a="<s>" , __a="</s>" , __a="</s>" , __a="<s>" , __a="<unk>" , __a="<pad>" , __a="<mask>" , __a=["<s>NOTUSED", "</s>NOTUSED"] , **__a , ):
'''simple docstring'''
__a : List[Any] = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else mask_token
super().__init__(
__a , tokenizer_file=__a , bos_token=__a , eos_token=__a , sep_token=__a , cls_token=__a , unk_token=__a , pad_token=__a , mask_token=__a , additional_special_tokens=__a , **__a , )
__a : Union[str, Any] = vocab_file
__a : Dict = False if not self.vocab_file else True
def __UpperCAmelCase ( self , __a , __a = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__a : Dict = [self.cls_token_id]
__a : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __UpperCAmelCase ( self , __a , __a = None ):
'''simple docstring'''
__a : str = [self.sep_token_id]
__a : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __UpperCAmelCase ( self , __a , __a = 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(__a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a : List[Any] = os.path.join(
__a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ):
copyfile(self.vocab_file , __a )
return (out_vocab_file,)
| 350
|
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "ernie_m"
A_ = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __a = 25_0002 , __a = 768 , __a = 12 , __a = 12 , __a = 3072 , __a = "gelu" , __a = 0.1 , __a = 0.1 , __a = 514 , __a = 0.02 , __a = 1 , __a = 1E-0_5 , __a=None , __a=False , __a=0.0 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
__a : int = vocab_size
__a : Dict = hidden_size
__a : str = num_hidden_layers
__a : Dict = num_attention_heads
__a : List[str] = intermediate_size
__a : Union[str, Any] = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : int = initializer_range
__a : Dict = layer_norm_eps
__a : int = classifier_dropout
__a : Dict = is_decoder
__a : int = act_dropout
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Any=28_123 ):
__a : Tuple = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
__a : Union[str, Any] = set()
__a : Dict = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(_SCREAMING_SNAKE_CASE )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 351
|
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a ):
'''simple docstring'''
super().__init__()
__a : int = module
__a : List[Any] = nn.Sequential(
nn.Linear(module.in_features , __a , bias=__a ) , nn.Linear(__a , module.out_features , bias=__a ) , )
__a : int = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__a )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __UpperCAmelCase ( self , __a , *__a , **__a ):
'''simple docstring'''
return self.module(__a , *__a , **__a ) + self.adapter(__a )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
A_ = "bigscience/bloom-1b7"
# Constant values
A_ = 2.109659552692574
A_ = "Hello my name is"
A_ = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
A_ = 10
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = AutoTokenizer.from_pretrained(self.model_name )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_abit.config
self.assertTrue(hasattr(__a , 'quantization_config' ) )
__a : Union[str, Any] = config.to_dict()
__a : Tuple = config.to_diff_dict()
__a : Tuple = config.to_json_string()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__a : List[Any] = self.model_fpaa.get_memory_footprint()
__a : List[Any] = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__a : Tuple = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__a , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Union[str, Any] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BitsAndBytesConfig()
__a : Tuple = True
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' )
__a : List[Any] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = BitsAndBytesConfig()
with self.assertRaises(__a ):
__a : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , load_in_abit=__a , device_map='auto' , bnb_abit_quant_type='nf4' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(__a ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__a : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Optional[int] = self.model_fpaa.to(torch.floataa )
__a : Tuple = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__a : List[Any] = self.model_fpaa.to('cpu' )
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.half()
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.float()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=__a , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
__a : Any = 't5-small'
__a : Tuple = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
__a : int = AutoTokenizer.from_pretrained(cls.model_name )
__a : Union[str, Any] = 'Translate in German: Hello, my dog is cute'
def __UpperCAmelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__a : Optional[int] = TaForConditionalGeneration._keep_in_fpaa_modules
__a : List[str] = None
# test with `t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : Any = model.generate(**__a )
# test with `flan-t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[Any] = model.generate(**__a )
__a : Optional[int] = modules
def __UpperCAmelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[str] = model.generate(**__a )
# test with `flan-t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : int = model.generate(**__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__a : List[Any] = 'bigscience/bloom-560m'
__a : Union[str, Any] = 't5-small'
# Different types of model
__a : Optional[Any] = AutoModel.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Sequence classification model
__a : Dict = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__a , device_map='auto' )
# CausalLM model
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Seq2seq model
__a : Any = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__a : str = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__a , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__a : List[Any] = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
__a : str = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'facebook/opt-350m'
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__a : Tuple = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__a : Tuple = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__a ) ):
__a : str = LoRALayer(module.q_proj , rank=16 )
__a : str = LoRALayer(module.k_proj , rank=16 )
__a : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__a : List[str] = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__a : int = model.forward(**__a )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__a , __a ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__a , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "gpt2-xl"
A_ = 3.3191854854152187
| 294
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'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowercase : List[Any] = 16
__lowercase : Tuple = 32
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : DatasetDict , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : int = 16 ):
__a : Optional[Any] = AutoTokenizer.from_pretrained('bert-base-cased' )
__a : Union[str, Any] = DatasetDict(
{
'train': dataset['train'].select(UpperCamelCase__ ),
'validation': dataset['train'].select(UpperCamelCase__ ),
'test': dataset['validation'],
} )
def tokenize_function(_SCREAMING_SNAKE_CASE : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
__a : Any = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__a : str = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__a : int = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_SCREAMING_SNAKE_CASE : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__a : Any = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__a : int = 16
elif accelerator.mixed_precision != "no":
__a : List[str] = 8
else:
__a : Optional[int] = None
return tokenizer.pad(
UpperCamelCase__ , padding='longest' , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
__a : Optional[int] = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
__a : Any = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
__a : Union[str, Any] = DataLoader(
tokenized_datasets['test'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader, test_dataloader
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Tuple ):
# New Code #
__a : Optional[Any] = []
# Download the dataset
__a : str = load_dataset('glue' , 'mrpc' )
# Create our splits
__a : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
__a : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a : List[str] = config['''lr''']
__a : Union[str, Any] = int(config['num_epochs'] )
__a : Dict = int(config['seed'] )
__a : Optional[int] = int(config['batch_size'] )
__a : List[str] = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
__a : Union[str, Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__a : str = batch_size // MAX_GPU_BATCH_SIZE
__a : Dict = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
# New Code #
# Create our folds:
__a : Optional[Any] = kfold.split(np.zeros(datasets['train'].num_rows ) , datasets['train']['label'] )
__a : str = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__ ):
__a : int = get_fold_dataloaders(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a : Optional[Any] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__a : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
__a : int = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
__a : Tuple = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__a : Dict = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__a : Optional[Any] = model(**UpperCamelCase__ )
__a : List[Any] = outputs.loss
__a : Dict = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__a : int = model(**UpperCamelCase__ )
__a : Optional[int] = outputs.logits.argmax(dim=-1 )
__a : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
__a : Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
# New Code #
# We also run predictions on the test set at the very end
__a : int = []
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__a : List[Any] = model(**UpperCamelCase__ )
__a : Dict = outputs.logits
__a : Optional[int] = accelerator.gather_for_metrics((predictions, batch['labels']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(UpperCamelCase__ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
__a : Tuple = torch.cat(UpperCamelCase__ , dim=0 )
__a : List[str] = torch.stack(UpperCamelCase__ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
__a : List[Any] = metric.compute(predictions=UpperCamelCase__ , references=UpperCamelCase__ )
accelerator.print('Average test metrics from all folds:' , UpperCamelCase__ )
def lowerCamelCase ():
__a : List[str] = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
# New Code #
parser.add_argument('--num_folds' , type=UpperCamelCase__ , default=3 , help='The number of splits to perform across the dataset' )
__a : Tuple = parser.parse_args()
__a : Optional[int] = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 352
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294
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|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __UpperCamelCase ( _lowerCamelCase , _lowerCamelCase ):
@register_to_config
def __init__( self , __a = 768 , ):
'''simple docstring'''
super().__init__()
__a : Any = nn.Parameter(torch.zeros(1 , __a ) )
__a : Optional[int] = nn.Parameter(torch.ones(1 , __a ) )
def __UpperCAmelCase ( self , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[int] = nn.Parameter(self.mean.to(__a ).to(__a ) )
__a : Optional[Any] = nn.Parameter(self.std.to(__a ).to(__a ) )
return self
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : List[str] = (embeds - self.mean) * 1.0 / self.std
return embeds
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : List[Any] = (embeds * self.std) + self.mean
return embeds
| 353
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
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|
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__lowercase : List[Any] = '''http://www.mocksite.com/file1.txt'''
__lowercase : Optional[int] = '''"text": ["foo", "foo"]'''
__lowercase : int = '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class __UpperCamelCase :
A_ = 200
A_ = {"Content-Length": "100"}
A_ = {}
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return [bytes(__a , 'utf-8' )]
def lowerCamelCase (*_SCREAMING_SNAKE_CASE : Any , **_SCREAMING_SNAKE_CASE : List[Any] ):
return MockResponse()
@pytest.mark.parametrize('urls_type' , [str, list, dict] )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
import requests
monkeypatch.setattr(_lowerCAmelCase , 'request' , _lowerCAmelCase )
__a : Optional[Any] = URL
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : List[Any] = url
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : List[Any] = [url]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : Tuple = {"train": url}
__a : List[str] = "dummy"
__a : Union[str, Any] = "downloads"
__a : Union[str, Any] = tmp_path
__a : str = DownloadConfig(
cache_dir=os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , use_etag=_lowerCAmelCase , )
__a : str = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
__a : Tuple = dl_manager.download(_lowerCAmelCase )
__a : List[Any] = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__a : List[Any] = [downloaded_paths]
__a : Optional[Any] = [urls]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in downloaded_paths.keys()
__a : Any = downloaded_paths.values()
__a : str = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
__a : Tuple = Path(_lowerCAmelCase )
__a : List[str] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
__a : Any = downloaded_path.read_text()
assert content == CONTENT
__a : Optional[Any] = downloaded_path.with_suffix('.json' )
assert metadata_downloaded_path.exists()
__a : Optional[Any] = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('paths_type' , [str, list, dict] )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any ):
__a : Tuple = str(_lowerCAmelCase )
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : Union[str, Any] = filename
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : Optional[int] = [filename]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
__a : List[Any] = {"train": filename}
__a : str = "dummy"
__a : Dict = xz_file.parent
__a : Any = "extracted"
__a : Union[str, Any] = DownloadConfig(
cache_dir=_lowerCAmelCase , use_etag=_lowerCAmelCase , )
__a : int = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
__a : Optional[int] = dl_manager.extract(_lowerCAmelCase )
__a : Union[str, Any] = paths
for extracted_paths in [extracted_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__a : List[str] = [extracted_paths]
__a : List[str] = [paths]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in extracted_paths.keys()
__a : Any = extracted_paths.values()
__a : Optional[int] = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
__a : Union[str, Any] = Path(_lowerCAmelCase )
__a : Optional[int] = extracted_path.parts
assert parts[-1] == hash_url_to_filename(_lowerCAmelCase , etag=_lowerCAmelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
__a : Optional[int] = extracted_path.read_text()
__a : Union[str, Any] = text_file.read_text()
assert extracted_file_content == expected_file_content
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Dict ):
assert path.endswith('.jsonl' )
for num_items, line in enumerate(_lowerCAmelCase , start=1 ):
__a : Any = json.loads(line.decode('utf-8' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('archive_jsonl' , ['tar_jsonl_path', 'zip_jsonl_path'] )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
__a : Dict = request.getfixturevalue(_lowerCAmelCase )
__a : Tuple = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_jsonl == 2
@pytest.mark.parametrize('archive_nested_jsonl' , ['tar_nested_jsonl_path', 'zip_nested_jsonl_path'] )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any ):
__a : List[str] = request.getfixturevalue(_lowerCAmelCase )
__a : List[str] = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_tar == 1
assert num_jsonl == 2
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] ):
__a : Union[str, Any] = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(_lowerCAmelCase ) , start=1 ):
assert os.path.basename(_lowerCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 354
|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowercase : str = logging.get_logger(__name__)
# General docstring
__lowercase : List[str] = 'MobileNetV1Config'
# Base docstring
__lowercase : Tuple = 'google/mobilenet_v1_1.0_224'
__lowercase : List[Any] = [1, 10_24, 7, 7]
# Image classification docstring
__lowercase : int = 'google/mobilenet_v1_1.0_224'
__lowercase : Any = 'tabby, tabby cat'
__lowercase : Dict = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any]=None ):
__a : Dict = {}
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = model.mobilenet_va
else:
__a : List[Any] = model
__a : Dict = 'MobilenetV1/Conv2d_0/'
__a : Dict = backbone.conv_stem.convolution.weight
__a : Optional[Any] = backbone.conv_stem.normalization.bias
__a : int = backbone.conv_stem.normalization.weight
__a : int = backbone.conv_stem.normalization.running_mean
__a : Tuple = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__a : int = i + 1
__a : Dict = i * 2
__a : Dict = backbone.layer[pt_index]
__a : Dict = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__a : Union[str, Any] = pointer.convolution.weight
__a : Optional[Any] = pointer.normalization.bias
__a : Union[str, Any] = pointer.normalization.weight
__a : List[Any] = pointer.normalization.running_mean
__a : Tuple = pointer.normalization.running_var
__a : List[str] = backbone.layer[pt_index + 1]
__a : Optional[Any] = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__a : Optional[int] = pointer.convolution.weight
__a : List[str] = pointer.normalization.bias
__a : Dict = pointer.normalization.weight
__a : Dict = pointer.normalization.running_mean
__a : Optional[int] = pointer.normalization.running_var
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Any = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__a : Optional[int] = model.classifier.weight
__a : List[Any] = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__a : Union[str, Any] = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
__a : List[str] = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Optional[Any] = array
# Build TF to PyTorch weights loading map
__a : Optional[int] = _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
__a : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__a : Optional[Any] = np.transpose(_SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__a : Union[str, Any] = array.squeeze().transpose()
else:
__a : Dict = np.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
__a : List[str] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
tf_weights.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp_1' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/ExponentialMovingAverage' , _SCREAMING_SNAKE_CASE )
logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}""" )
return model
def lowerCamelCase (_SCREAMING_SNAKE_CASE : torch.Tensor , _SCREAMING_SNAKE_CASE : nn.Convad ):
__a , __a : Any = features.shape[-2:]
__a , __a : int = conv_layer.stride
__a , __a : Any = conv_layer.kernel_size
if in_height % stride_height == 0:
__a : int = max(kernel_height - stride_height , 0 )
else:
__a : int = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__a : Any = max(kernel_width - stride_width , 0 )
else:
__a : str = max(kernel_width - (in_width % stride_width) , 0 )
__a : int = pad_along_width // 2
__a : Dict = pad_along_width - pad_left
__a : List[str] = pad_along_height // 2
__a : Union[str, Any] = pad_along_height - pad_top
__a : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'constant' , 0.0 )
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a , __a , __a , __a = 1 , __a = 1 , __a = False , __a = True , __a = True , ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
__a : Dict = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
__a : Union[str, Any] = nn.Convad(
in_channels=__a , out_channels=__a , kernel_size=__a , stride=__a , padding=__a , groups=__a , bias=__a , padding_mode='zeros' , )
if use_normalization:
__a : List[str] = nn.BatchNormad(
num_features=__a , eps=config.layer_norm_eps , momentum=0.9997 , affine=__a , track_running_stats=__a , )
else:
__a : Tuple = None
if use_activation:
if isinstance(__a , __a ):
__a : Tuple = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __a ):
__a : Union[str, Any] = ACTaFN[config.hidden_act]
else:
__a : Dict = config.hidden_act
else:
__a : List[Any] = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.config.tf_padding:
__a : Union[str, Any] = apply_tf_padding(__a , self.convolution )
__a : Union[str, Any] = self.convolution(__a )
if self.normalization is not None:
__a : str = self.normalization(__a )
if self.activation is not None:
__a : Optional[int] = self.activation(__a )
return features
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = MobileNetVaConfig
A_ = load_tf_weights_in_mobilenet_va
A_ = "mobilenet_v1"
A_ = "pixel_values"
A_ = False
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__lowercase : Any = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowercase : Optional[int] = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a = True ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[int] = config
__a : str = 32
__a : Dict = max(int(depth * config.depth_multiplier ) , config.min_depth )
__a : Union[str, Any] = MobileNetVaConvLayer(
__a , in_channels=config.num_channels , out_channels=__a , kernel_size=3 , stride=2 , )
__a : Tuple = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__a : Any = nn.ModuleList()
for i in range(13 ):
__a : Union[str, Any] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__a : List[Any] = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=3 , stride=strides[i] , groups=__a , ) )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=1 , ) )
__a : Optional[int] = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__a : int = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__a : Union[str, Any] = self.conv_stem(__a )
__a : Any = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
__a : List[str] = layer_module(__a )
if output_hidden_states:
__a : List[Any] = all_hidden_states + (hidden_states,)
__a : str = hidden_states
if self.pooler is not None:
__a : Union[str, Any] = torch.flatten(self.pooler(__a ) , start_dim=1 )
else:
__a : int = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__a , pooler_output=__a , hidden_states=__a , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Tuple = config.num_labels
__a : Tuple = MobileNetVaModel(__a )
__a : Optional[int] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__a : Any = nn.Dropout(config.classifier_dropout_prob , inplace=__a )
__a : Any = nn.Linear(__a , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Dict = self.mobilenet_va(__a , output_hidden_states=__a , return_dict=__a )
__a : List[str] = outputs.pooler_output if return_dict else outputs[1]
__a : int = self.classifier(self.dropout(__a ) )
__a : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__a : str = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__a : int = 'single_label_classification'
else:
__a : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__a : Optional[Any] = MSELoss()
if self.num_labels == 1:
__a : List[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__a : Any = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__a : List[str] = CrossEntropyLoss()
__a : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__a : Tuple = BCEWithLogitsLoss()
__a : Optional[int] = loss_fct(__a , __a )
if not return_dict:
__a : List[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__a , logits=__a , hidden_states=outputs.hidden_states , )
| 294
| 0
|
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/grid.txt' ) as f:
__a : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(_SCREAMING_SNAKE_CASE ) for x in f.readline().split()] )
__a : Tuple = 0
# right
for i in range(20 ):
for j in range(17 ):
__a : Optional[int] = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
__a : Optional[int] = temp
# down
for i in range(17 ):
for j in range(20 ):
__a : str = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
__a : Union[str, Any] = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
__a : Dict = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
__a : Optional[int] = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
__a : Tuple = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
__a : Tuple = temp
return maximum
if __name__ == "__main__":
print(solution())
| 355
|
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowercase : str = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "dhaka" , _SCREAMING_SNAKE_CASE : int = 5 ):
__a : Optional[Any] = min(_SCREAMING_SNAKE_CASE , 50 ) # Prevent abuse!
__a : Optional[Any] = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
__a : Tuple = requests.get('https://www.google.com/search' , params=_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE )
__a : Dict = BeautifulSoup(html.text , 'html.parser' )
__a : List[str] = ''.join(
re.findall(r'AF_initDataCallback\(([^<]+)\);' , str(soup.select('script' ) ) ) )
__a : Optional[Any] = json.dumps(_SCREAMING_SNAKE_CASE )
__a : List[str] = json.loads(_SCREAMING_SNAKE_CASE )
__a : List[Any] = re.findall(
r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",' , _SCREAMING_SNAKE_CASE , )
if not matched_google_image_data:
return 0
__a : Tuple = re.sub(
r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]' , '' , str(_SCREAMING_SNAKE_CASE ) , )
__a : Optional[Any] = re.findall(
r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]' , _SCREAMING_SNAKE_CASE , )
for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
__a : List[str] = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Tuple = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Dict = urllib.request.build_opener()
__a : Union[str, Any] = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_SCREAMING_SNAKE_CASE )
__a : List[Any] = F"""query_{query.replace(" " , "_" )}"""
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
_SCREAMING_SNAKE_CASE , F"""{path_name}/original_size_img_{index}.jpg""" )
return index
if __name__ == "__main__":
try:
__lowercase : Optional[int] = download_images_from_google_query(sys.argv[1])
print(f'''{image_count} images were downloaded to disk.''')
except IndexError:
print('Please provide a search term.')
raise
| 294
| 0
|
'''simple docstring'''
from datetime import datetime
import requests
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] ):
__a : List[str] = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__a : Optional[Any] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(__UpperCAmelCase ).content
if __name__ == "__main__":
__lowercase : Optional[Any] = input('Enter Video/IGTV url: ').strip()
__lowercase : Union[str, Any] = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, 'wb') as fp:
fp.write(download_video(url))
print(f'''Done. Video saved to disk as {file_name}.''')
| 356
|
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split(',' )
names.sort()
__a : Union[str, Any] = 0
__a : Tuple = 0
for i, name in enumerate(_SCREAMING_SNAKE_CASE ):
for letter in name:
name_score += ord(_SCREAMING_SNAKE_CASE ) - 64
total_score += (i + 1) * name_score
__a : Any = 0
return total_score
if __name__ == "__main__":
print(solution())
| 294
| 0
|
'''simple docstring'''
__lowercase : List[str] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : List[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 100_000]
number //= 100_000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
__lowercase : list[bool | None] = [None] * 10_00_00_00
__lowercase : Optional[Any] = True
__lowercase : Optional[int] = False
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__a : Tuple = chain(next_number(lowercase_ ) )
__a : int = number_chain
while number < 10_000_000:
__a : Optional[Any] = number_chain
number *= 10
return number_chain
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 10_000_000 ):
for i in range(1 , lowercase_ ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution() = }''')
| 357
|
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
| 294
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|
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__lowercase : Optional[Any] = 16
__lowercase : Dict = 32
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] = 16 , _SCREAMING_SNAKE_CASE : List[Any] = "bert-base-cased" ):
__a : Union[str, Any] = AutoTokenizer.from_pretrained(A_ )
__a : Union[str, Any] = load_dataset('glue' , 'mrpc' )
def tokenize_function(_SCREAMING_SNAKE_CASE : str ):
# max_length=None => use the model max length (it's actually the default)
__a : Optional[int] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=A_ , max_length=A_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__a : List[Any] = datasets.map(
A_ , batched=A_ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=A_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__a : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_SCREAMING_SNAKE_CASE : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(A_ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(A_ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
__a : Optional[Any] = DataLoader(
tokenized_datasets['train'] , shuffle=A_ , collate_fn=A_ , batch_size=A_ )
__a : Tuple = DataLoader(
tokenized_datasets['validation'] , shuffle=A_ , collate_fn=A_ , batch_size=A_ )
return train_dataloader, eval_dataloader
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Any ):
# Initialize accelerator
__a : Optional[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a : Optional[int] = config['''lr''']
__a : Optional[int] = int(config['num_epochs'] )
__a : List[Any] = int(config['seed'] )
__a : Optional[int] = int(config['batch_size'] )
__a : Union[str, Any] = args.model_name_or_path
set_seed(A_ )
__a : Optional[int] = get_dataloaders(A_ , A_ , A_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a : List[Any] = AutoModelForSequenceClassification.from_pretrained(A_ , return_dict=A_ )
# Instantiate optimizer
__a : List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__a : Tuple = optimizer_cls(params=model.parameters() , lr=A_ )
if accelerator.state.deepspeed_plugin is not None:
__a : Any = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
__a : Any = 1
__a : str = (len(A_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__a : Dict = get_linear_schedule_with_warmup(
optimizer=A_ , num_warmup_steps=0 , num_training_steps=A_ , )
else:
__a : Any = DummyScheduler(A_ , total_num_steps=A_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__a : int = accelerator.prepare(
A_ , A_ , A_ , A_ , A_ )
# We need to keep track of how many total steps we have iterated over
__a : Tuple = 0
# We also need to keep track of the stating epoch so files are named properly
__a : int = 0
# Now we train the model
__a : Tuple = evaluate.load('glue' , 'mrpc' )
__a : Dict = 0
__a : Any = {}
for epoch in range(A_ , A_ ):
model.train()
for step, batch in enumerate(A_ ):
__a : Optional[Any] = model(**A_ )
__a : List[Any] = outputs.loss
__a : str = loss / gradient_accumulation_steps
accelerator.backward(A_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
__a : Union[str, Any] = 0
for step, batch in enumerate(A_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__a : int = model(**A_ )
__a : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
__a : Optional[Any] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(A_ ) - 1:
__a : List[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__a : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=A_ , references=A_ , )
__a : Optional[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , A_ )
__a : Optional[int] = eval_metric['''accuracy''']
if best_performance < eval_metric["accuracy"]:
__a : str = eval_metric['''accuracy''']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(A_ , A_ )
def lowerCamelCase ():
__a : Tuple = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=A_ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=A_ , )
parser.add_argument(
'--output_dir' , type=A_ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=A_ , default=A_ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=A_ , default=3 , help='Number of train epochs.' , )
__a : int = parser.parse_args()
__a : Any = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(A_ , A_ )
if __name__ == "__main__":
main()
| 358
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[int] ):
if len(_lowercase ) != len(_lowercase ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
if any(p < 0 for p in profit ):
raise ValueError('Profit can not be negative.' )
if any(w < 0 for w in weight ):
raise ValueError('Weight can not be negative.' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
__a : Optional[int] = [p / w for p, w in zip(_lowercase , _lowercase )]
# Creating a copy of the list and sorting profit/weight in ascending order
__a : List[str] = sorted(_lowercase )
# declaring useful variables
__a : Tuple = len(_lowercase )
__a : Any = 0
__a : Optional[Any] = 0
__a : Union[str, Any] = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
__a : str = sorted_profit_by_weight[length - i - 1]
__a : List[str] = profit_by_weight.index(_lowercase )
__a : Dict = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
'Input profits, weights, and then max_weight (all positive ints) separated by '
'spaces.'
)
__lowercase : List[Any] = [int(x) for x in input('Input profits separated by spaces: ').split()]
__lowercase : Tuple = [int(x) for x in input('Input weights separated by spaces: ').split()]
__lowercase : Union[str, Any] = int(input('Max weight allowed: '))
# Function Call
calc_profit(profit, weight, max_weight)
| 359
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class __UpperCamelCase :
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : str = parent
__a : Dict = batch_size
__a : str = seq_length
__a : Optional[Any] = is_training
__a : Union[str, Any] = use_input_mask
__a : int = use_token_type_ids
__a : Union[str, Any] = use_labels
__a : int = vocab_size
__a : Optional[Any] = hidden_size
__a : Dict = num_hidden_layers
__a : Optional[int] = num_attention_heads
__a : List[Any] = intermediate_size
__a : List[Any] = hidden_act
__a : Tuple = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : int = max_position_embeddings
__a : Tuple = type_vocab_size
__a : List[Any] = type_sequence_label_size
__a : Optional[int] = initializer_range
__a : int = num_labels
__a : str = num_choices
__a : int = relative_attention
__a : Dict = position_biased_input
__a : Tuple = pos_att_type
__a : Optional[Any] = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : int = None
if self.use_input_mask:
__a : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
__a : List[Any] = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[Any] = None
__a : str = None
__a : Any = None
if self.use_labels:
__a : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : Tuple = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=_SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : str = TFDebertaVaModel(config=_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
__a : Optional[Any] = [input_ids, input_mask]
__a : Optional[int] = model(_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = TFDebertaVaForMaskedLM(config=_SCREAMING_SNAKE_CASE )
__a : Tuple = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__a : Any = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = self.num_labels
__a : List[Any] = TFDebertaVaForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
__a : Tuple = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__a : Dict = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = self.num_labels
__a : Optional[Any] = TFDebertaVaForTokenClassification(config=_SCREAMING_SNAKE_CASE )
__a : str = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__a : Union[str, Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = TFDebertaVaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
__a : Tuple = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__a : Optional[int] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.prepare_config_and_inputs()
(
__a
) : Optional[int] = config_and_inputs
__a : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
A_ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
A_ = (
{
'feature-extraction': TFDebertaVaModel,
'fill-mask': TFDebertaVaForMaskedLM,
'question-answering': TFDebertaVaForQuestionAnswering,
'text-classification': TFDebertaVaForSequenceClassification,
'token-classification': TFDebertaVaForTokenClassification,
'zero-shot': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = TFDebertaVaModelTester(self )
__a : str = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
__a : Tuple = tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : int = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__a : Optional[int] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )[0]
__a : List[str] = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
| 360
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = 'laion/clap-htsat-unfused'
__a : Optional[Any] = tempfile.mkdtemp()
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return RobertaTokenizer.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.get_tokenizer()
__a : List[str] = self.get_feature_extractor()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
processor.save_pretrained(self.tmpdirname )
__a : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__a : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__a : List[str] = self.get_feature_extractor(do_normalize=__a , padding_value=1.0 )
__a : Tuple = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : int = self.get_tokenizer()
__a : str = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : int = floats_list((3, 1000) )
__a : str = feature_extractor(__a , return_tensors='np' )
__a : int = processor(audios=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_feature_extractor()
__a : Any = self.get_tokenizer()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Union[str, Any] = 'This is a test string'
__a : Union[str, Any] = processor(text=__a )
__a : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : str = self.get_tokenizer()
__a : List[str] = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Optional[int] = processor.batch_decode(__a )
__a : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_feature_extractor()
__a : Optional[int] = self.get_tokenizer()
__a : int = ClapProcessor(tokenizer=__a , feature_extractor=__a )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 294
| 0
|
'''simple docstring'''
from math import factorial
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 100 ):
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 361
|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : List[str] = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : str = hidden_size
__a : List[str] = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Tuple = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[int] = max_position_embeddings
__a : Dict = type_vocab_size
__a : Any = type_sequence_label_size
__a : Dict = initializer_range
__a : Optional[Any] = num_labels
__a : Optional[Any] = num_choices
__a : Union[str, Any] = relative_attention
__a : List[str] = position_biased_input
__a : List[Any] = pos_att_type
__a : Tuple = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : Any = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[int] = None
__a : int = None
__a : Dict = None
if self.use_labels:
__a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__a : str = model(__a , token_type_ids=__a )[0]
__a : Optional[int] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : int = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.num_labels
__a : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__a : Any = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : str = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : str = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__a : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Dict = config_and_inputs
__a : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = DebertaVaModelTester(self )
__a : List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : str = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__a : Optional[Any] = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : int = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__a : str = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 294
| 0
|
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( lowerCamelCase__ ):
A_ = ["""image_processor""", """tokenizer"""]
A_ = """LayoutLMv3ImageProcessor"""
A_ = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
def __init__( self , __a=None , __a=None , **__a ):
'''simple docstring'''
__a : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , snake_case__ , )
__a : int = kwargs.pop('feature_extractor' )
__a : Optional[int] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case__ , snake_case__ )
def __call__( self , __a , __a = None , __a = None , __a = None , __a = None , __a = True , __a = False , __a = None , __a = None , __a = 0 , __a = None , __a = None , __a = None , __a = False , __a = False , __a = False , __a = False , __a = True , __a = None , **__a , ):
'''simple docstring'''
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__a : Optional[int] = self.image_processor(images=snake_case__ , return_tensors=snake_case__ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(snake_case__ , snake_case__ ):
__a : int = [text] # add batch dimension (as the image processor always adds a batch dimension)
__a : List[Any] = features['''words''']
__a : Any = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=snake_case__ , add_special_tokens=snake_case__ , padding=snake_case__ , truncation=snake_case__ , max_length=snake_case__ , stride=snake_case__ , pad_to_multiple_of=snake_case__ , return_token_type_ids=snake_case__ , return_attention_mask=snake_case__ , return_overflowing_tokens=snake_case__ , return_special_tokens_mask=snake_case__ , return_offsets_mapping=snake_case__ , return_length=snake_case__ , verbose=snake_case__ , return_tensors=snake_case__ , **snake_case__ , )
# add pixel values
__a : str = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__a : Dict = self.get_overflowing_images(snake_case__ , encoded_inputs['overflow_to_sample_mapping'] )
__a : int = images
return encoded_inputs
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : Any = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
f""" {len(snake_case__ )} and {len(snake_case__ )}""" )
return images_with_overflow
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , snake_case__ , )
return self.image_processor_class
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case__ , )
return self.image_processor
| 362
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294
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'''simple docstring'''
from __future__ import annotations
__lowercase : Optional[Any] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] , ):
__a : Dict = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
] # the reference grid
__a : Any = 1
__a : List[Any] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
] # the action grid
__a : str = init[0]
__a : Any = init[1]
__a : List[str] = 0
__a : int = g + heuristic[x][y] # cost from starting cell to destination cell
__a : Dict = [[f, g, x, y]]
__a : List[Any] = False # flag that is set when search is complete
__a : Optional[int] = False # flag set if we can't find expand
while not found and not resign:
if len(_lowercase ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__a : Optional[int] = cell.pop()
__a : Dict = next_cell[2]
__a : int = next_cell[3]
__a : Union[str, Any] = next_cell[1]
if x == goal[0] and y == goal[1]:
__a : List[Any] = True
else:
for i in range(len(_lowercase ) ): # to try out different valid actions
__a : Optional[Any] = x + DIRECTIONS[i][0]
__a : List[Any] = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_lowercase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__a : Dict = g + cost
__a : List[Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__a : Any = 1
__a : Union[str, Any] = i
__a : Tuple = []
__a : Tuple = goal[0]
__a : Optional[Any] = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__a : Dict = x - DIRECTIONS[action[x][y]][0]
__a : Union[str, Any] = y - DIRECTIONS[action[x][y]][1]
__a : Optional[int] = xa
__a : Tuple = ya
invpath.append([x, y] )
__a : Optional[int] = []
for i in range(len(_lowercase ) ):
path.append(invpath[len(_lowercase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__lowercase : str = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__lowercase : int = [0, 0]
# all coordinates are given in format [y,x]
__lowercase : Dict = [len(grid) - 1, len(grid[0]) - 1]
__lowercase : int = 1
# the cost map which pushes the path closer to the goal
__lowercase : Optional[int] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__lowercase : Optional[Any] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__lowercase : Optional[Any] = 99
__lowercase : Optional[int] = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 363
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294
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|
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : bool , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : float ):
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if not scores:
raise ValueError('Scores cannot be empty' )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , )
)
def lowerCamelCase ():
__a : Tuple = [90, 23, 6, 33, 21, 65, 123, 34_423]
__a : List[Any] = math.log(len(_SCREAMING_SNAKE_CASE ) , 2 )
print(F"""Optimal value : {minimax(0 , 0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 364
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
A_ = 42
A_ = None
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : TreeNode | None ):
# Validation
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
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|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__lowercase : str = logging.get_logger(__name__)
class __UpperCamelCase ( __lowerCAmelCase ):
A_ = ['''pixel_values''']
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 255 , __a = None , __a = True , __a = None , __a = None , **__a , ):
'''simple docstring'''
super().__init__(**lowerCAmelCase_ )
__a : Any = size if size is not None else {'height': 224, 'width': 224}
__a : Union[str, Any] = get_size_dict(lowerCAmelCase_ )
__a : Any = crop_size if crop_size is not None else {'height': 224, 'width': 224}
__a : Dict = get_size_dict(lowerCAmelCase_ , default_to_square=lowerCAmelCase_ , param_name='crop_size' )
__a : Optional[int] = do_resize
__a : List[Any] = do_rescale
__a : Union[str, Any] = do_normalize
__a : Any = do_center_crop
__a : Optional[Any] = crop_size
__a : List[str] = size
__a : List[Any] = resample
__a : List[Any] = rescale_factor
__a : Optional[Any] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__a : Tuple = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ):
'''simple docstring'''
__a : Optional[int] = get_size_dict(lowerCAmelCase_ )
if "shortest_edge" in size:
__a : Any = get_resize_output_image_size(lowerCAmelCase_ , size=size['shortest_edge'] , default_to_square=lowerCAmelCase_ )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
__a : str = (size['height'], size['width'])
else:
raise ValueError(f"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}""" )
return resize(lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def __UpperCAmelCase ( self , __a , __a , __a = None , **__a , ):
'''simple docstring'''
__a : List[str] = get_size_dict(lowerCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(lowerCAmelCase_ , size=(size['height'], size['width']) , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def __UpperCAmelCase ( self , __a , __a , __a = None , **__a ):
'''simple docstring'''
return rescale(lowerCAmelCase_ , scale=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def __UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ):
'''simple docstring'''
return normalize(lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def __UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ):
'''simple docstring'''
__a : Optional[Any] = do_resize if do_resize is not None else self.do_resize
__a : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
__a : Tuple = do_normalize if do_normalize is not None else self.do_normalize
__a : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
__a : List[str] = crop_size if crop_size is not None else self.crop_size
__a : int = get_size_dict(lowerCAmelCase_ , param_name='crop_size' , default_to_square=lowerCAmelCase_ )
__a : Any = resample if resample is not None else self.resample
__a : str = rescale_factor if rescale_factor is not None else self.rescale_factor
__a : Dict = image_mean if image_mean is not None else self.image_mean
__a : Dict = image_std if image_std is not None else self.image_std
__a : Optional[Any] = size if size is not None else self.size
__a : List[Any] = get_size_dict(lowerCAmelCase_ )
if not is_batched(lowerCAmelCase_ ):
__a : Tuple = [images]
if not valid_images(lowerCAmelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
# All transformations expect numpy arrays.
__a : str = [to_numpy_array(lowerCAmelCase_ ) for image in images]
if do_resize:
__a : int = [self.resize(image=lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ ) for image in images]
if do_center_crop:
__a : Optional[Any] = [self.center_crop(image=lowerCAmelCase_ , size=lowerCAmelCase_ ) for image in images]
if do_rescale:
__a : List[str] = [self.rescale(image=lowerCAmelCase_ , scale=lowerCAmelCase_ ) for image in images]
if do_normalize:
__a : Optional[Any] = [self.normalize(image=lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ ) for image in images]
__a : Tuple = [to_channel_dimension_format(lowerCAmelCase_ , lowerCAmelCase_ ) for image in images]
__a : Optional[Any] = {'pixel_values': images}
return BatchFeature(data=lowerCAmelCase_ , tensor_type=lowerCAmelCase_ )
| 365
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294
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|
'''simple docstring'''
__lowercase : Tuple = 'Input must be a string of 8 numbers plus letter'
__lowercase : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE'
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__a : List[Any] = F"""Expected string as input, found {type(lowerCAmelCase__ ).__name__}"""
raise TypeError(lowerCAmelCase__ )
__a : List[str] = spanish_id.replace('-' , '' ).upper()
if len(lowerCAmelCase__ ) != 9:
raise ValueError(lowerCAmelCase__ )
try:
__a : Union[str, Any] = int(spanish_id_clean[0:8] )
__a : 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()
| 366
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
re.sub('<n>' , '' , _SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 294
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|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
A_ = XGLMConfig
A_ = {}
A_ = "gelu"
def __init__( self , __a , __a=14 , __a=7 , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=0.02 , ):
'''simple docstring'''
__a : Union[str, Any] = parent
__a : Dict = batch_size
__a : int = seq_length
__a : str = is_training
__a : List[str] = use_input_mask
__a : Dict = use_labels
__a : str = vocab_size
__a : Tuple = d_model
__a : Union[str, Any] = num_hidden_layers
__a : str = num_attention_heads
__a : int = ffn_dim
__a : Optional[int] = activation_function
__a : Optional[Any] = activation_dropout
__a : Dict = attention_dropout
__a : str = max_position_embeddings
__a : Tuple = initializer_range
__a : int = None
__a : List[Any] = 0
__a : int = 2
__a : Dict = 1
def __UpperCAmelCase ( self ):
'''simple docstring'''
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
__a : List[Any] = None
if self.use_input_mask:
__a : str = random_attention_mask([self.batch_size, self.seq_length] )
__a : Dict = self.get_config()
__a : Union[str, Any] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def __UpperCAmelCase ( self ):
'''simple docstring'''
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=UpperCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=UpperCamelCase__ , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.prepare_config_and_inputs()
(
__a
) : Union[str, Any] = config_and_inputs
__a : List[Any] = {
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A_ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
A_ = (TFXGLMForCausalLM,) if is_tf_available() else ()
A_ = (
{"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
)
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = TFXGLMModelTester(self )
__a : List[Any] = ConfigTester(self , config_class=UpperCamelCase__ , n_embd=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Union[str, Any] = TFXGLMModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def __UpperCAmelCase ( self , __a=True ):
'''simple docstring'''
__a : Dict = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__a : List[Any] = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__a : List[Any] = [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
# fmt: on
__a : int = model.generate(UpperCamelCase__ , do_sample=UpperCamelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCamelCase__ )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__a : Dict = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
__a : List[Any] = tokenizer('Today is a nice day and' , return_tensors='tf' )
__a : Union[str, Any] = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
__a : Optional[int] = model.generate(UpperCamelCase__ , do_sample=UpperCamelCase__ , seed=[7, 0] )
__a : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=UpperCamelCase__ )
__a : Tuple = (
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__a : List[Any] = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__a : List[Any] = '''left'''
# use different length sentences to test batching
__a : int = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
__a : Dict = tokenizer(UpperCamelCase__ , return_tensors='tf' , padding=UpperCamelCase__ )
__a : str = inputs['''input_ids''']
__a : Union[str, Any] = model.generate(input_ids=UpperCamelCase__ , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
__a : Tuple = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__a : List[Any] = model.generate(input_ids=UpperCamelCase__ , max_new_tokens=12 )
__a : Union[str, Any] = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__a : Dict = model.generate(input_ids=UpperCamelCase__ , max_new_tokens=12 )
__a : Optional[int] = tokenizer.batch_decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
__a : List[Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=UpperCamelCase__ )
__a : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=UpperCamelCase__ )
__a : Union[str, Any] = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , [non_padded_sentence, padded_sentence] )
| 367
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__lowercase : Any = [0, 25, 50]
__lowercase : int = [25, 50, 75]
__lowercase : List[str] = fuzz.membership.trimf(X, abca)
__lowercase : Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowercase : List[Any] = np.ones(75)
__lowercase : Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowercase : str = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowercase : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowercase : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowercase : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowercase : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowercase : Union[str, Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 294
| 0
|
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : Optional[int] = get_tests_dir('fixtures/spiece.model')
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( _a , unittest.TestCase ):
A_ = DebertaVaTokenizer
A_ = DebertaVaTokenizerFast
A_ = True
A_ = True
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a : List[str] = DebertaVaTokenizer(__a , unk_token='<unk>' )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Union[str, Any] = 'this is a test'
__a : int = 'this is a test'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = '<pad>'
__a : Tuple = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a ) , __a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '[PAD]' )
self.assertEqual(len(__a ) , 3_0001 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ' \tHeLLo!how \n Are yoU? '
__a : str = ['▁hello', '!', 'how', '▁are', '▁you', '?']
# fmt: on
__a : Union[str, Any] = DebertaVaTokenizer(__a , do_lower_case=__a )
__a : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : Dict = DebertaVaTokenizerFast(__a , do_lower_case=__a )
__a : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = 'I was born in 92000, and this is falsé.'
__a : Optional[int] = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
__a : List[Any] = DebertaVaTokenizer(__a , split_by_punct=__a )
__a : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : Optional[int] = DebertaVaTokenizerFast(__a , split_by_punct=__a )
__a : Dict = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = 'I was born in 92000, and this is falsé.'
__a : Any = ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
__a : Optional[int] = DebertaVaTokenizer(__a , do_lower_case=__a , split_by_punct=__a )
__a : List[str] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : List[str] = DebertaVaTokenizerFast(__a , do_lower_case=__a , split_by_punct=__a )
__a : Tuple = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = 'I was born in 92000, and this is falsé.'
__a : List[Any] = ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
__a : str = DebertaVaTokenizer(__a , do_lower_case=__a , split_by_punct=__a )
__a : List[str] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : Dict = DebertaVaTokenizerFast(__a , do_lower_case=__a , split_by_punct=__a )
__a : Tuple = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = 'I was born in 92000, and this is falsé.'
__a : Optional[Any] = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
__a : str = DebertaVaTokenizer(__a , do_lower_case=__a , split_by_punct=__a )
__a : str = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : Tuple = DebertaVaTokenizerFast(__a , do_lower_case=__a , split_by_punct=__a )
__a : str = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ' \tHeLLo!how \n Are yoU? '
__a : int = ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
# fmt: on
__a : Union[str, Any] = DebertaVaTokenizer(__a , do_lower_case=__a , split_by_punct=__a )
__a : Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : List[Any] = DebertaVaTokenizerFast(__a , do_lower_case=__a , split_by_punct=__a )
__a : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.get_tokenizer()
__a : List[Any] = self.get_rust_tokenizer()
__a : List[str] = 'I was born in 92000, and this is falsé.'
__a : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(__a , add_special_tokens=__a ) )
__a : List[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__a , add_special_tokens=__a ) )
self.assertListEqual(__a , __a )
__a : Union[str, Any] = tokenizer.encode(__a , add_special_tokens=__a )
__a : Dict = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : Optional[Any] = self.get_rust_tokenizer()
__a : List[str] = tokenizer.encode(__a )
__a : Tuple = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'This is a test'
__a : Tuple = [13, 1, 4398, 25, 21, 1289]
__a : List[Any] = ['▁', 'T', 'his', '▁is', '▁a', '▁test']
__a : Union[str, Any] = ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']
__a : Dict = DebertaVaTokenizer(__a , keep_accents=__a )
__a : Optional[int] = DebertaVaTokenizerFast(__a , keep_accents=__a )
__a : Union[str, Any] = tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : int = tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : Dict = tokenizer.convert_ids_to_tokens(__a )
self.assertListEqual(__a , __a )
__a : List[str] = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : List[Any] = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : List[str] = rust_tokenizer.convert_ids_to_tokens(__a )
self.assertListEqual(__a , __a )
# fmt: off
__a : Any = 'I was born in 92000, and this is falsé.'
__a : int = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
__a : Tuple = ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
__a : Dict = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
__a : Tuple = tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : int = tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : Optional[Any] = tokenizer.convert_ids_to_tokens(__a )
self.assertListEqual(__a , __a )
__a : List[str] = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : List[Any] = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : int = rust_tokenizer.convert_ids_to_tokens(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaTokenizer(__a )
__a : Any = tokenizer.encode('sequence builders' )
__a : Optional[Any] = tokenizer.encode('multi-sequence build' )
__a : Dict = tokenizer.build_inputs_with_special_tokens(__a )
__a : List[str] = tokenizer.build_inputs_with_special_tokens(__a , __a )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , __a )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , __a , )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = {'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__a , model_name='microsoft/deberta-v2-xlarge' , revision='ad6e42c1532ddf3a15c39246b63f5559d558b670' , )
| 368
|
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = 1
for digit in s:
product *= int(_SCREAMING_SNAKE_CASE )
return product
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = N ):
__a : Optional[int] = -sys.maxsize - 1
__a : Optional[Any] = n[:13]
__a : int = 13
while cur_index < len(_SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__a : List[Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__a : Dict = max(_SCREAMING_SNAKE_CASE , str_eval(_SCREAMING_SNAKE_CASE ) )
__a : Optional[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 294
| 0
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCamelCase (_SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : set , _SCREAMING_SNAKE_CASE : set , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : PriorityQueue , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__a : str = cst_fwd.get(__UpperCamelCase , np.inf )
__a : Optional[int] = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__a : str = new_cost_f
__a : Tuple = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__a : List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : dict ):
__a : List[str] = -1
__a : Any = set()
__a : List[str] = set()
__a : Tuple = {source: 0}
__a : Optional[Any] = {destination: 0}
__a : int = {source: None}
__a : Optional[int] = {destination: None}
__a : Union[str, Any] = PriorityQueue()
__a : Any = PriorityQueue()
__a : Any = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__a , __a : str = queue_forward.get()
visited_forward.add(__UpperCamelCase )
__a , __a : Dict = queue_backward.get()
visited_backward.add(__UpperCamelCase )
__a : Optional[int] = pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
__a : int = pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__a : Tuple = shortest_distance
return shortest_path_distance
__lowercase : Optional[int] = {
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
__lowercase : Union[str, Any] = {
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = CodeGenTokenizer
A_ = CodeGenTokenizerFast
A_ = True
A_ = {"add_prefix_space": True}
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__a : Tuple = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
__a : Union[str, Any] = dict(zip(__a , range(len(__a ) ) ) )
__a : Tuple = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__a : Dict = {'unk_token': '<unk>'}
__a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Tuple = 'lower newer'
__a : Tuple = 'lower newer'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a : str = 'lower newer'
__a : Tuple = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
__a : List[str] = tokens + [tokenizer.unk_token]
__a : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a : List[Any] = self.get_tokenizer()
__a : List[str] = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Any = 'lower newer'
# Testing tokenization
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
__a : Dict = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
__a : int = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__a : Tuple = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
__a : Tuple = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Union[str, Any] = tokenizer.encode(__a , add_prefix_space=__a )
__a : int = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
__a : Any = tokens + [rust_tokenizer.unk_token]
__a : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self , __a=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Optional[int] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
__a : List[Any] = 'This is a simple input'
__a : Tuple = ['This is a simple input 1', 'This is a simple input 2']
__a : Tuple = ('This is a simple input', 'This is a pair')
__a : str = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
__a : str = 'This is a simple input'
__a : Any = ['This is a simple input looooooooong', 'This is a simple input']
__a : Optional[int] = ('This is a simple input', 'This is a pair')
__a : Optional[Any] = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
__a : int = tokenizer.pad_token_id
__a : List[Any] = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
__a : Union[str, Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
__a : Optional[Any] = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
__a : List[Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = '$$$'
__a : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
__a : Union[str, Any] = 'This is a simple input'
__a : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
__a : List[Any] = tokenizer.bos_token_id
__a : List[str] = tokenizer(__a )
__a : Optional[Any] = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__a : Any = tokenizer.decode(out_s.input_ids )
__a : Union[str, Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
__a : Optional[int] = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
__a : Tuple = '\nif len_a > len_b: result = a\nelse: result = b'
__a : Optional[int] = tokenizer.encode(__a )
__a : Union[str, Any] = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
__a : Tuple = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 294
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : List[Any] = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecTextConfig',
'Data2VecTextOnnxConfig',
],
'configuration_data2vec_vision': [
'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecVisionConfig',
'Data2VecVisionOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Optional[Any] = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
__lowercase : str = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
__lowercase : Union[str, Any] = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
__lowercase : str = [
'TFData2VecVisionForImageClassification',
'TFData2VecVisionForSemanticSegmentation',
'TFData2VecVisionModel',
'TFData2VecVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__lowercase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 370
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
__a : Dict = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(_SCREAMING_SNAKE_CASE )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowercase : str = 16
__lowercase : int = 32
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : int = 16 ):
__a : Optional[Any] = AutoTokenizer.from_pretrained('bert-base-cased' )
__a : Dict = load_dataset('glue' , 'mrpc' )
def tokenize_function(_SCREAMING_SNAKE_CASE : Any ):
# max_length=None => use the model max length (it's actually the default)
__a : str = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=a__ , max_length=a__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__a : Any = datasets.map(
a__ , batched=a__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__a : List[str] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_SCREAMING_SNAKE_CASE : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__a : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__a : Tuple = 16
elif accelerator.mixed_precision != "no":
__a : Union[str, Any] = 8
else:
__a : List[Any] = None
return tokenizer.pad(
a__ , padding='longest' , max_length=a__ , pad_to_multiple_of=a__ , return_tensors='pt' , )
# Instantiate dataloaders.
__a : Optional[Any] = DataLoader(
tokenized_datasets['train'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
__a : Tuple = DataLoader(
tokenized_datasets['validation'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowercase : List[str] = mocked_dataloaders # noqa: F811
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
if os.environ.get('TESTING_MOCKED_DATALOADERS' , a__ ) == "1":
__a : Tuple = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
__a : Union[str, Any] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='all' , project_dir=args.project_dir )
else:
__a : Any = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a : List[str] = config['lr']
__a : str = int(config['num_epochs'] )
__a : List[str] = int(config['seed'] )
__a : str = int(config['batch_size'] )
set_seed(a__ )
__a , __a : Union[str, Any] = get_dataloaders(a__ , a__ )
__a : Optional[Any] = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
__a : Union[str, Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__a : Any = batch_size // MAX_GPU_BATCH_SIZE
__a : Optional[Any] = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a : Dict = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=a__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__a : Any = model.to(accelerator.device )
# Instantiate optimizer
__a : Tuple = AdamW(params=model.parameters() , lr=a__ )
# Instantiate scheduler
__a : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=a__ , num_warmup_steps=100 , num_training_steps=(len(a__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__a , __a , __a , __a , __a : Tuple = accelerator.prepare(
a__ , a__ , a__ , a__ , a__ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
__a : List[Any] = os.path.split(a__ )[-1].split('.' )[0]
accelerator.init_trackers(a__ , a__ )
# Now we train the model
for epoch in range(a__ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
__a : Optional[Any] = 0
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__a : List[Any] = model(**a__ )
__a : int = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
__a : str = loss / gradient_accumulation_steps
accelerator.backward(a__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
__a : Optional[int] = model(**a__ )
__a : Optional[Any] = outputs.logits.argmax(dim=-1 )
__a , __a : str = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=a__ , references=a__ , )
__a : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , a__ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'accuracy': eval_metric['accuracy'],
'f1': eval_metric['f1'],
'train_loss': total_loss.item() / len(a__ ),
'epoch': epoch,
} , step=a__ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowerCamelCase ():
__a : Tuple = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=a__ , default=a__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
parser.add_argument(
'--with_tracking' , action='store_true' , help='Whether to load in all available experiment trackers from the environment and use them for logging.' , )
parser.add_argument(
'--project_dir' , type=a__ , default='logs' , help='Location on where to store experiment tracking logs` and relevent project information' , )
__a : Tuple = parser.parse_args()
__a : int = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(a__ , a__ )
if __name__ == "__main__":
main()
| 371
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , ):
'''simple docstring'''
__a : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__a : int = parent
__a : Dict = batch_size
__a : Optional[int] = num_channels
__a : List[Any] = image_size
__a : Tuple = min_resolution
__a : str = max_resolution
__a : str = do_resize
__a : Optional[Any] = size
__a : str = apply_ocr
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LayoutLMvaImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'apply_ocr' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
__a : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
__a : Any = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__a : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : Tuple = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__a : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : List[str] = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
__a : str = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
__a : Tuple = Image.open(ds[0]['file'] ).convert('RGB' )
__a : Optional[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__a : Optional[Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__a : Union[str, Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
__a : List[Any] = LayoutLMvaImageProcessor(apply_ocr=__a )
__a : List[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 294
| 0
|
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCamelCase ( snake_case_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_A , 'tf_padding' ) )
self.parent.assertTrue(hasattr(_A , 'depth_multiplier' ) )
class __UpperCamelCase :
def __init__( self , __a , __a=13 , __a=3 , __a=32 , __a=0.25 , __a=8 , __a=True , __a=1024 , __a=32 , __a="relu6" , __a=0.1 , __a=0.02 , __a=True , __a=True , __a=10 , __a=None , ):
'''simple docstring'''
__a : Optional[int] = parent
__a : List[str] = batch_size
__a : Optional[int] = num_channels
__a : int = image_size
__a : Optional[Any] = depth_multiplier
__a : str = min_depth
__a : Any = tf_padding
__a : str = int(last_hidden_size * depth_multiplier )
__a : List[Any] = output_stride
__a : Optional[int] = hidden_act
__a : str = classifier_dropout_prob
__a : Any = use_labels
__a : Optional[Any] = is_training
__a : Tuple = num_labels
__a : Tuple = initializer_range
__a : Optional[int] = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : Union[str, Any] = None
__a : int = None
if self.use_labels:
__a : Tuple = ids_tensor([self.batch_size] , self.num_labels )
__a : Tuple = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__a : List[str] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __UpperCAmelCase ( self , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = MobileNetVaModel(config=_A )
model.to(_A )
model.eval()
__a : List[Any] = model(_A )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __UpperCAmelCase ( self , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : str = MobileNetVaForImageClassification(_A )
model.to(_A )
model.eval()
__a : Union[str, Any] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.prepare_config_and_inputs()
__a : Dict = config_and_inputs
__a : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
A_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
A_ = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = MobileNetVaModelTester(self )
__a : Tuple = MobileNetVaConfigTester(self , config_class=_A , has_text_modality=_A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileNetV1 does not use inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='MobileNetV1 does not support input and output embeddings' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='MobileNetV1 does not output attentions' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Dict = model_class(_A )
__a : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : int = [*signature.parameters.keys()]
__a : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , _A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a ):
__a : Dict = model_class(_A )
model.to(_A )
model.eval()
with torch.no_grad():
__a : Optional[Any] = model(**self._prepare_for_class(_A , _A ) )
__a : str = outputs.hidden_states
__a : Dict = 26
self.assertEqual(len(_A ) , _A )
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Union[str, Any] = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : Optional[int] = True
check_hidden_states_output(_A , _A , _A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Tuple = MobileNetVaModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def lowerCamelCase ():
__a : str = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224' ) if is_vision_available() else None
)
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224' ).to(_A )
__a : int = self.default_image_processor
__a : List[Any] = prepare_img()
__a : List[Any] = image_processor(images=_A , return_tensors='pt' ).to(_A )
# forward pass
with torch.no_grad():
__a : int = model(**_A )
# verify the logits
__a : Dict = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , _A )
__a : Tuple = torch.tensor([-4.1739, -1.1233, 3.1205] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
| 350
|
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "ernie_m"
A_ = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __a = 25_0002 , __a = 768 , __a = 12 , __a = 12 , __a = 3072 , __a = "gelu" , __a = 0.1 , __a = 0.1 , __a = 514 , __a = 0.02 , __a = 1 , __a = 1E-0_5 , __a=None , __a=False , __a=0.0 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
__a : int = vocab_size
__a : Dict = hidden_size
__a : str = num_hidden_layers
__a : Dict = num_attention_heads
__a : List[str] = intermediate_size
__a : Union[str, Any] = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : int = initializer_range
__a : Dict = layer_norm_eps
__a : int = classifier_dropout
__a : Dict = is_decoder
__a : int = act_dropout
| 294
| 0
|
'''simple docstring'''
from typing import Any
class __UpperCamelCase :
def __init__( self , __a ):
'''simple docstring'''
__a : List[Any] = data
__a : Dict = None
def __repr__( self ):
'''simple docstring'''
return f"""Node({self.data})"""
class __UpperCamelCase :
def __init__( self ):
'''simple docstring'''
__a : Optional[Any] = None
def __iter__( self ):
'''simple docstring'''
__a : Optional[Any] = self.head
while node:
yield node.data
__a : Any = node.next
def __len__( self ):
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self ):
'''simple docstring'''
return "->".join([str(__a ) for item in self] )
def __getitem__( self , __a ):
'''simple docstring'''
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , __a , __a ):
'''simple docstring'''
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
__a : int = self.head
for _ in range(__a ):
__a : List[str] = current.next
__a : int = data
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.insert_nth(len(self ) , __a )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.insert_nth(0 , __a )
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
if not 0 <= index <= len(self ):
raise IndexError('list index out of range' )
__a : Optional[int] = Node(__a )
if self.head is None:
__a : Union[str, Any] = new_node
elif index == 0:
__a : List[Any] = self.head # link new_node to head
__a : int = new_node
else:
__a : List[Any] = self.head
for _ in range(index - 1 ):
__a : Optional[Any] = temp.next
__a : str = temp.next
__a : Tuple = new_node
def __UpperCAmelCase ( self ): # print every node data
'''simple docstring'''
print(self )
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.delete_nth(0 )
def __UpperCAmelCase ( self ): # delete from tail
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def __UpperCAmelCase ( self , __a = 0 ):
'''simple docstring'''
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('List index out of range.' )
__a : List[Any] = self.head # default first node
if index == 0:
__a : Optional[int] = self.head.next
else:
__a : int = self.head
for _ in range(index - 1 ):
__a : int = temp.next
__a : Union[str, Any] = temp.next
__a : int = temp.next.next
return delete_node.data
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.head is None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = None
__a : Optional[Any] = self.head
while current:
# Store the current node's next node.
__a : List[Any] = current.next
# Make the current node's next point backwards
__a : Optional[int] = prev
# Make the previous node be the current node
__a : List[str] = current
# Make the current node the next node (to progress iteration)
__a : Tuple = next_node
# Return prev in order to put the head at the end
__a : List[Any] = prev
def lowerCamelCase ():
__a : int = LinkedList()
assert linked_list.is_empty() is True
assert str(A__ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(A__ ) == i
linked_list.insert_nth(A__ , i + 1 )
assert str(A__ ) == "->".join(str(A__ ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(A__ ) == "->".join(str(A__ ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(A__ ) == 9
assert str(A__ ) == "->".join(str(A__ ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
__a : List[Any] = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(A__ ) == "->".join(str(A__ ) for i in range(-8 , 1 ) )
def lowerCamelCase ():
__a : Tuple = [
-9,
100,
Node(77_345_112 ),
"""dlrow olleH""",
7,
5_555,
0,
-1_9_2.5_5_5_5_5,
"""Hello, world!""",
7_7.9,
Node(10 ),
None,
None,
1_2.2_0,
]
__a : Tuple = LinkedList()
for i in test_input:
linked_list.insert_tail(A__ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(A__ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
__a : List[Any] = linked_list.delete_head()
assert result == -9
assert (
str(A__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
__a : List[Any] = linked_list.delete_tail()
assert result == 1_2.2
assert (
str(A__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
__a : List[Any] = linked_list.delete_nth(10 )
assert result is None
assert (
str(A__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('Hello again, world!' ) )
assert (
str(A__ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(A__ )
assert (
str(A__ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(A__ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowerCamelCase ():
from doctest import testmod
testmod()
__a : str = LinkedList()
linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
print('\nDelete head' )
linked_list.delete_head()
print('Delete tail' )
linked_list.delete_tail()
print('\nPrint list:' )
linked_list.print_list()
print('\nReverse linked list' )
linked_list.reverse()
print('\nPrint list:' )
linked_list.print_list()
print('\nString representation of linked list:' )
print(A__ )
print('\nReading/changing Node data using indexing:' )
print(F"""Element at Position 1: {linked_list[1]}""" )
__a : Tuple = input('Enter New Value: ' ).strip()
print('New list:' )
print(A__ )
print(F"""length of linked_list is : {len(A__ )}""" )
if __name__ == "__main__":
main()
| 351
|
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a ):
'''simple docstring'''
super().__init__()
__a : int = module
__a : List[Any] = nn.Sequential(
nn.Linear(module.in_features , __a , bias=__a ) , nn.Linear(__a , module.out_features , bias=__a ) , )
__a : int = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__a )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __UpperCAmelCase ( self , __a , *__a , **__a ):
'''simple docstring'''
return self.module(__a , *__a , **__a ) + self.adapter(__a )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
A_ = "bigscience/bloom-1b7"
# Constant values
A_ = 2.109659552692574
A_ = "Hello my name is"
A_ = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
A_ = 10
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = AutoTokenizer.from_pretrained(self.model_name )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_abit.config
self.assertTrue(hasattr(__a , 'quantization_config' ) )
__a : Union[str, Any] = config.to_dict()
__a : Tuple = config.to_diff_dict()
__a : Tuple = config.to_json_string()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__a : List[Any] = self.model_fpaa.get_memory_footprint()
__a : List[Any] = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__a : Tuple = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__a , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Union[str, Any] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BitsAndBytesConfig()
__a : Tuple = True
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' )
__a : List[Any] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = BitsAndBytesConfig()
with self.assertRaises(__a ):
__a : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , load_in_abit=__a , device_map='auto' , bnb_abit_quant_type='nf4' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(__a ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__a : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Optional[int] = self.model_fpaa.to(torch.floataa )
__a : Tuple = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__a : List[Any] = self.model_fpaa.to('cpu' )
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.half()
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.float()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=__a , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
__a : Any = 't5-small'
__a : Tuple = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
__a : int = AutoTokenizer.from_pretrained(cls.model_name )
__a : Union[str, Any] = 'Translate in German: Hello, my dog is cute'
def __UpperCAmelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__a : Optional[int] = TaForConditionalGeneration._keep_in_fpaa_modules
__a : List[str] = None
# test with `t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : Any = model.generate(**__a )
# test with `flan-t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[Any] = model.generate(**__a )
__a : Optional[int] = modules
def __UpperCAmelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[str] = model.generate(**__a )
# test with `flan-t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : int = model.generate(**__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__a : List[Any] = 'bigscience/bloom-560m'
__a : Union[str, Any] = 't5-small'
# Different types of model
__a : Optional[Any] = AutoModel.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Sequence classification model
__a : Dict = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__a , device_map='auto' )
# CausalLM model
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Seq2seq model
__a : Any = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__a : str = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__a , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__a : List[Any] = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
__a : str = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'facebook/opt-350m'
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__a : Tuple = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__a : Tuple = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__a ) ):
__a : str = LoRALayer(module.q_proj , rank=16 )
__a : str = LoRALayer(module.k_proj , rank=16 )
__a : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__a : List[str] = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__a : int = model.forward(**__a )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__a , __a ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__a , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "gpt2-xl"
A_ = 3.3191854854152187
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list ):
def merge(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(lowerCAmelCase__ ) <= 1:
return collection
__a : str = len(lowerCAmelCase__ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase : Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
__lowercase : Optional[int] = [int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 352
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , __a=None , __a=True , __a=[0.5, 0.5, 0.5] , __a=[0.5, 0.5, 0.5] , __a=False , ):
'''simple docstring'''
__a : Optional[Any] = size if size is not None else {'height': 20, 'width': 20}
__a : Union[str, Any] = crop_size if crop_size is not None else {'height': 18, 'width': 18}
__a : Tuple = parent
__a : Optional[int] = batch_size
__a : List[str] = num_channels
__a : Dict = image_size
__a : Optional[Any] = min_resolution
__a : Optional[Any] = max_resolution
__a : Optional[Any] = do_resize
__a : Any = size
__a : Union[str, Any] = do_center_crop
__a : str = crop_size
__a : int = do_normalize
__a : Any = image_mean
__a : int = image_std
__a : str = do_reduce_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase ():
__a : Dict = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
__a : Dict = Image.open(dataset[0]['file'] )
__a : int = Image.open(dataset[1]['file'] )
return image, map
def lowerCamelCase ():
__a : Any = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
__a : Union[str, Any] = Image.open(ds[0]['file'] )
__a : str = Image.open(ds[1]['file'] )
__a : List[str] = Image.open(ds[2]['file'] )
__a : Any = Image.open(ds[3]['file'] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = BeitImageProcessor if is_vision_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BeitImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'do_center_crop' ) )
self.assertTrue(hasattr(__a , 'center_crop' ) )
self.assertTrue(hasattr(__a , 'do_normalize' ) )
self.assertTrue(hasattr(__a , 'image_mean' ) )
self.assertTrue(hasattr(__a , 'image_std' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 20, 'width': 20} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
self.assertEqual(image_processor.do_reduce_labels , __a )
__a : Optional[Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__a )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
self.assertEqual(image_processor.do_reduce_labels , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Tuple = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a : Dict = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__a : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a : Optional[int] = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__a : Dict = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a : Any = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
__a : Tuple = []
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
__a : Any = image_processing(image_inputs[0] , maps[0] , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
1,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test batched
__a : int = image_processing(__a , __a , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test not batched input (PIL images)
__a , __a : Union[str, Any] = prepare_semantic_single_inputs()
__a : Any = image_processing(__a , __a , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
1,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test batched input (PIL images)
__a , __a : Tuple = prepare_semantic_batch_inputs()
__a : Optional[int] = image_processing(__a , __a , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
2,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
__a , __a : Tuple = prepare_semantic_single_inputs()
__a : List[str] = image_processing(__a , __a , return_tensors='pt' )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 150 )
__a : Union[str, Any] = True
__a : Tuple = image_processing(__a , __a , return_tensors='pt' )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
| 353
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 1_000 ):
__a : List[Any] = 3
__a : Optional[int] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 354
|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowercase : str = logging.get_logger(__name__)
# General docstring
__lowercase : List[str] = 'MobileNetV1Config'
# Base docstring
__lowercase : Tuple = 'google/mobilenet_v1_1.0_224'
__lowercase : List[Any] = [1, 10_24, 7, 7]
# Image classification docstring
__lowercase : int = 'google/mobilenet_v1_1.0_224'
__lowercase : Any = 'tabby, tabby cat'
__lowercase : Dict = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any]=None ):
__a : Dict = {}
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = model.mobilenet_va
else:
__a : List[Any] = model
__a : Dict = 'MobilenetV1/Conv2d_0/'
__a : Dict = backbone.conv_stem.convolution.weight
__a : Optional[Any] = backbone.conv_stem.normalization.bias
__a : int = backbone.conv_stem.normalization.weight
__a : int = backbone.conv_stem.normalization.running_mean
__a : Tuple = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__a : int = i + 1
__a : Dict = i * 2
__a : Dict = backbone.layer[pt_index]
__a : Dict = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__a : Union[str, Any] = pointer.convolution.weight
__a : Optional[Any] = pointer.normalization.bias
__a : Union[str, Any] = pointer.normalization.weight
__a : List[Any] = pointer.normalization.running_mean
__a : Tuple = pointer.normalization.running_var
__a : List[str] = backbone.layer[pt_index + 1]
__a : Optional[Any] = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__a : Optional[int] = pointer.convolution.weight
__a : List[str] = pointer.normalization.bias
__a : Dict = pointer.normalization.weight
__a : Dict = pointer.normalization.running_mean
__a : Optional[int] = pointer.normalization.running_var
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Any = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__a : Optional[int] = model.classifier.weight
__a : List[Any] = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__a : Union[str, Any] = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
__a : List[str] = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Optional[Any] = array
# Build TF to PyTorch weights loading map
__a : Optional[int] = _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
__a : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__a : Optional[Any] = np.transpose(_SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__a : Union[str, Any] = array.squeeze().transpose()
else:
__a : Dict = np.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
__a : List[str] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
tf_weights.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp_1' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/ExponentialMovingAverage' , _SCREAMING_SNAKE_CASE )
logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}""" )
return model
def lowerCamelCase (_SCREAMING_SNAKE_CASE : torch.Tensor , _SCREAMING_SNAKE_CASE : nn.Convad ):
__a , __a : Any = features.shape[-2:]
__a , __a : int = conv_layer.stride
__a , __a : Any = conv_layer.kernel_size
if in_height % stride_height == 0:
__a : int = max(kernel_height - stride_height , 0 )
else:
__a : int = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__a : Any = max(kernel_width - stride_width , 0 )
else:
__a : str = max(kernel_width - (in_width % stride_width) , 0 )
__a : int = pad_along_width // 2
__a : Dict = pad_along_width - pad_left
__a : List[str] = pad_along_height // 2
__a : Union[str, Any] = pad_along_height - pad_top
__a : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'constant' , 0.0 )
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a , __a , __a , __a = 1 , __a = 1 , __a = False , __a = True , __a = True , ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
__a : Dict = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
__a : Union[str, Any] = nn.Convad(
in_channels=__a , out_channels=__a , kernel_size=__a , stride=__a , padding=__a , groups=__a , bias=__a , padding_mode='zeros' , )
if use_normalization:
__a : List[str] = nn.BatchNormad(
num_features=__a , eps=config.layer_norm_eps , momentum=0.9997 , affine=__a , track_running_stats=__a , )
else:
__a : Tuple = None
if use_activation:
if isinstance(__a , __a ):
__a : Tuple = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __a ):
__a : Union[str, Any] = ACTaFN[config.hidden_act]
else:
__a : Dict = config.hidden_act
else:
__a : List[Any] = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.config.tf_padding:
__a : Union[str, Any] = apply_tf_padding(__a , self.convolution )
__a : Union[str, Any] = self.convolution(__a )
if self.normalization is not None:
__a : str = self.normalization(__a )
if self.activation is not None:
__a : Optional[int] = self.activation(__a )
return features
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = MobileNetVaConfig
A_ = load_tf_weights_in_mobilenet_va
A_ = "mobilenet_v1"
A_ = "pixel_values"
A_ = False
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__lowercase : Any = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowercase : Optional[int] = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a = True ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[int] = config
__a : str = 32
__a : Dict = max(int(depth * config.depth_multiplier ) , config.min_depth )
__a : Union[str, Any] = MobileNetVaConvLayer(
__a , in_channels=config.num_channels , out_channels=__a , kernel_size=3 , stride=2 , )
__a : Tuple = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__a : Any = nn.ModuleList()
for i in range(13 ):
__a : Union[str, Any] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__a : List[Any] = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=3 , stride=strides[i] , groups=__a , ) )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=1 , ) )
__a : Optional[int] = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__a : int = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__a : Union[str, Any] = self.conv_stem(__a )
__a : Any = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
__a : List[str] = layer_module(__a )
if output_hidden_states:
__a : List[Any] = all_hidden_states + (hidden_states,)
__a : str = hidden_states
if self.pooler is not None:
__a : Union[str, Any] = torch.flatten(self.pooler(__a ) , start_dim=1 )
else:
__a : int = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__a , pooler_output=__a , hidden_states=__a , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Tuple = config.num_labels
__a : Tuple = MobileNetVaModel(__a )
__a : Optional[int] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__a : Any = nn.Dropout(config.classifier_dropout_prob , inplace=__a )
__a : Any = nn.Linear(__a , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Dict = self.mobilenet_va(__a , output_hidden_states=__a , return_dict=__a )
__a : List[str] = outputs.pooler_output if return_dict else outputs[1]
__a : int = self.classifier(self.dropout(__a ) )
__a : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__a : str = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__a : int = 'single_label_classification'
else:
__a : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__a : Optional[Any] = MSELoss()
if self.num_labels == 1:
__a : List[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__a : Any = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__a : List[str] = CrossEntropyLoss()
__a : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__a : Tuple = BCEWithLogitsLoss()
__a : Optional[int] = loss_fct(__a , __a )
if not return_dict:
__a : List[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__a , logits=__a , hidden_states=outputs.hidden_states , )
| 294
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|
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict = 1_000 ):
__a : Dict = 2**power
__a : int = str(_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = list(_SCREAMING_SNAKE_CASE )
__a : List[Any] = 0
for i in list_num:
sum_of_num += int(_SCREAMING_SNAKE_CASE )
return sum_of_num
if __name__ == "__main__":
__lowercase : Optional[int] = int(input('Enter the power of 2: ').strip())
print('2 ^ ', power, ' = ', 2**power)
__lowercase : Optional[Any] = solution(power)
print('Sum of the digits is: ', result)
| 355
|
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowercase : str = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "dhaka" , _SCREAMING_SNAKE_CASE : int = 5 ):
__a : Optional[Any] = min(_SCREAMING_SNAKE_CASE , 50 ) # Prevent abuse!
__a : Optional[Any] = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
__a : Tuple = requests.get('https://www.google.com/search' , params=_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE )
__a : Dict = BeautifulSoup(html.text , 'html.parser' )
__a : List[str] = ''.join(
re.findall(r'AF_initDataCallback\(([^<]+)\);' , str(soup.select('script' ) ) ) )
__a : Optional[Any] = json.dumps(_SCREAMING_SNAKE_CASE )
__a : List[str] = json.loads(_SCREAMING_SNAKE_CASE )
__a : List[Any] = re.findall(
r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",' , _SCREAMING_SNAKE_CASE , )
if not matched_google_image_data:
return 0
__a : Tuple = re.sub(
r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]' , '' , str(_SCREAMING_SNAKE_CASE ) , )
__a : Optional[Any] = re.findall(
r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]' , _SCREAMING_SNAKE_CASE , )
for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
__a : List[str] = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Tuple = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Dict = urllib.request.build_opener()
__a : Union[str, Any] = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_SCREAMING_SNAKE_CASE )
__a : List[Any] = F"""query_{query.replace(" " , "_" )}"""
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
_SCREAMING_SNAKE_CASE , F"""{path_name}/original_size_img_{index}.jpg""" )
return index
if __name__ == "__main__":
try:
__lowercase : Optional[int] = download_images_from_google_query(sys.argv[1])
print(f'''{image_count} images were downloaded to disk.''')
except IndexError:
print('Please provide a search term.')
raise
| 294
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|
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list[int] ):
__a : List[str] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
__a : Dict = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356
|
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split(',' )
names.sort()
__a : Union[str, Any] = 0
__a : Tuple = 0
for i, name in enumerate(_SCREAMING_SNAKE_CASE ):
for letter in name:
name_score += ord(_SCREAMING_SNAKE_CASE ) - 64
total_score += (i + 1) * name_score
__a : Any = 0
return total_score
if __name__ == "__main__":
print(solution())
| 294
| 0
|
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a ):
'''simple docstring'''
__a : str = params
__a : Union[str, Any] = np.array(_A )
__a : List[str] = np.array([len(_A ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , __a ):
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self ):
'''simple docstring'''
return len(self.lengths )
def __UpperCAmelCase ( self ):
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.params.max_model_input_size
__a : Union[str, Any] = self.lengths > max_len
logger.info(f"""Splitting {sum(_A )} too long sequences.""" )
def divide_chunks(__a , __a ):
return [l[i : i + n] for i in range(0 , len(_A ) , _A )]
__a : Optional[int] = []
__a : int = []
if self.params.mlm:
__a : int = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
else:
__a : Optional[Any] = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
__a : Optional[Any] = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
__a : Optional[Any] = np.insert(_A , 0 , _A )
if sub_s[-1] != sep_id:
__a : List[Any] = np.insert(_A , len(_A ) , _A )
assert len(_A ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(_A )
new_tok_ids.extend(_A )
new_lengths.extend([len(_A ) for l in sub_seqs] )
__a : Union[str, Any] = np.array(_A )
__a : Union[str, Any] = np.array(_A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = len(self )
__a : Dict = self.lengths > 11
__a : Union[str, Any] = self.token_ids[indices]
__a : str = self.lengths[indices]
__a : Any = len(self )
logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
__a : Optional[Any] = self.params.special_tok_ids['unk_token']
__a : Optional[Any] = len(self )
__a : Tuple = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
__a : Any = (unk_occs / self.lengths) < 0.5
__a : Any = self.token_ids[indices]
__a : str = self.lengths[indices]
__a : Optional[Any] = len(self )
logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.params.is_master:
return
logger.info(f"""{len(self )} sequences""" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : int = [t[0] for t in batch]
__a : Any = [t[1] for t in batch]
assert len(_A ) == len(_A )
# Max for paddings
__a : Any = max(_A )
# Pad token ids
if self.params.mlm:
__a : Optional[int] = self.params.special_tok_ids['pad_token']
else:
__a : Tuple = self.params.special_tok_ids['unk_token']
__a : List[str] = [list(t.astype(_A ) ) + [pad_idx] * (max_seq_len_ - len(_A )) for t in token_ids]
assert len(tk_ ) == len(_A )
assert all(len(_A ) == max_seq_len_ for t in tk_ )
__a : Tuple = torch.tensor(tk_ ) # (bs, max_seq_len_)
__a : Any = torch.tensor(_A ) # (bs)
return tk_t, lg_t
| 357
|
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
| 294
| 0
|
'''simple docstring'''
__lowercase : Optional[int] = tuple[float, float, float]
__lowercase : Optional[Any] = tuple[float, float, float]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Any ):
__a : Union[str, Any] = end_pointa[0] - end_pointa[0]
__a : Dict = end_pointa[1] - end_pointa[1]
__a : int = end_pointa[2] - end_pointa[2]
return (x, y, z)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int ):
__a : Union[str, Any] = ab[1] * ac[2] - ab[2] * ac[1] # *i
__a : Any = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
__a : str = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : List[str] ):
return tuple(round(_A , _A ) for x in vector ) == (0, 0, 0)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int = 10 ):
__a : Dict = create_vector(_A , _A )
__a : Optional[int] = create_vector(_A , _A )
return is_zero_vector(get_ad_vectors_cross(_A , _A ) , _A )
| 358
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A__ , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(A__ , 'num_attention_heads' ) )
class __UpperCamelCase :
def __init__( self , __a , __a=13 , __a=64 , __a=3 , __a=3 , __a=2 , __a=1 , __a=16 , __a=[128, 256, 384] , __a=[4, 6, 8] , __a=[2, 3, 4] , __a=[16, 16, 16] , __a=0 , __a=[2, 2, 2] , __a=[2, 2, 2] , __a=0.02 , __a=True , __a=True , __a=2 , ):
'''simple docstring'''
__a : Union[str, Any] = parent
__a : List[Any] = batch_size
__a : Dict = image_size
__a : List[str] = num_channels
__a : List[Any] = kernel_size
__a : Optional[Any] = stride
__a : List[Any] = padding
__a : Union[str, Any] = hidden_sizes
__a : Union[str, Any] = num_attention_heads
__a : Tuple = depths
__a : List[Any] = key_dim
__a : Dict = drop_path_rate
__a : str = patch_size
__a : int = attention_ratio
__a : Any = mlp_ratio
__a : Any = initializer_range
__a : List[str] = [
['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
__a : Tuple = is_training
__a : Optional[Any] = use_labels
__a : List[Any] = num_labels
__a : List[str] = initializer_range
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : Optional[Any] = None
if self.use_labels:
__a : Tuple = ids_tensor([self.batch_size] , self.num_labels )
__a : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : str = LevitModel(config=A__ )
model.to(A__ )
model.eval()
__a : Optional[Any] = model(A__ )
__a : Tuple = (self.image_size, self.image_size)
__a , __a : str = image_size[0], image_size[1]
for _ in range(4 ):
__a : str = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
__a : List[str] = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : Tuple = self.num_labels
__a : Optional[int] = LevitForImageClassification(A__ )
model.to(A__ )
model.eval()
__a : Union[str, Any] = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.prepare_config_and_inputs()
__a , __a , __a : Tuple = config_and_inputs
__a : int = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
A_ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
A_ = (
{
'''feature-extraction''': LevitModel,
'''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LevitModelTester(self )
__a : int = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __UpperCAmelCase ( self ):
'''simple docstring'''
return
@unittest.skip(reason='Levit does not use inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='Levit does not support input and output embeddings' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='Levit does not output attentions' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a , __a : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Union[str, Any] = model_class(A__ )
__a : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : Optional[Any] = [*signature.parameters.keys()]
__a : List[str] = ['pixel_values']
self.assertListEqual(arg_names[:1] , A__ )
def __UpperCAmelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a ):
__a : Union[str, Any] = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
__a : Union[str, Any] = model(**self._prepare_for_class(A__ , A__ ) )
__a : str = outputs.hidden_states
__a : Any = len(self.model_tester.depths ) + 1
self.assertEqual(len(A__ ) , A__ )
__a : Tuple = (self.model_tester.image_size, self.model_tester.image_size)
__a , __a : Dict = image_size[0], image_size[1]
for _ in range(4 ):
__a : Optional[Any] = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
__a : Tuple = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
__a , __a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[Any] = True
check_hidden_states_output(A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : Optional[Any] = True
check_hidden_states_output(A__ , A__ , A__ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self , __a , __a , __a=False ):
'''simple docstring'''
__a : int = super()._prepare_for_class(A__ , A__ , return_labels=A__ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
__a , __a : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__a : int = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(A__ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
__a : int = model_class(A__ )
model.to(A__ )
model.train()
__a : Tuple = self._prepare_for_class(A__ , A__ , return_labels=A__ )
__a : Optional[Any] = model(**A__ ).loss
loss.backward()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a , __a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__a : Optional[int] = False
__a : int = True
for model_class in self.all_model_classes:
if model_class in get_values(A__ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
__a : List[str] = model_class(A__ )
model.gradient_checkpointing_enable()
model.to(A__ )
model.train()
__a : int = self._prepare_for_class(A__ , A__ , return_labels=A__ )
__a : List[str] = model(**A__ ).loss
loss.backward()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a , __a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__a : Union[str, Any] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(A__ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
__a : Optional[int] = problem_type['title']
__a : Dict = problem_type['num_labels']
__a : int = model_class(A__ )
model.to(A__ )
model.train()
__a : List[str] = self._prepare_for_class(A__ , A__ , return_labels=A__ )
if problem_type["num_labels"] > 1:
__a : Any = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
__a : Dict = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=A__ ) as warning_list:
__a : Dict = model(**A__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Union[str, Any] = LevitModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def lowerCamelCase ():
__a : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
A__ )
__a : Dict = self.default_image_processor
__a : Tuple = prepare_img()
__a : Optional[Any] = image_processor(images=A__ , return_tensors='pt' ).to(A__ )
# forward pass
with torch.no_grad():
__a : Any = model(**A__ )
# verify the logits
__a : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , A__ )
__a : str = torch.tensor([1.0448, -0.3745, -1.8317] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1E-4 ) )
| 359
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowercase : Tuple = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'),
('gamma', 'weight'),
('pegasus', 'model'),
]
__lowercase : List[Any] = [
('.output.dense', '.fc2'),
('intermediate.LayerNorm', 'final_layer_norm'),
('intermediate.dense', 'fc1'),
]
__lowercase : Any = (
INIT_COMMON
+ [
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.out_proj'),
('attention.self', 'self_attn'),
('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
('attention.encdec_output.dense', 'encoder_attn.out_proj'),
('attention.encdec', 'encoder_attn'),
('key', 'k_proj'),
('value', 'v_proj'),
('query', 'q_proj'),
('decoder.LayerNorm', 'decoder.layernorm_embedding'),
]
+ END_COMMON
)
__lowercase : List[str] = (
INIT_COMMON
+ [
('embeddings.word_embeddings', 'shared.weight'),
('embeddings.position_embeddings', 'embed_positions.weight'),
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.output'),
('attention.self', 'self_attn.self'),
('encoder.LayerNorm', 'encoder.layernorm_embedding'),
]
+ END_COMMON
)
__lowercase : Dict = [
'encdec/key/bias',
'encdec/query/bias',
'encdec/value/bias',
'self/key/bias',
'self/query/bias',
'self/value/bias',
'encdec_output/dense/bias',
'attention/output/dense/bias',
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
for tf_name, hf_name in patterns:
__a : Any = k.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return k
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Dict = BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE )
__a : Any = torch_model.state_dict()
__a : Dict = {}
# separating decoder weights
__a : str = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
__a : Optional[int] = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
__a : str = [k.endswith(_SCREAMING_SNAKE_CASE ) for ending in KEYS_TO_IGNORE]
if any(_SCREAMING_SNAKE_CASE ):
continue
__a : int = DECODER_PATTERNS
__a : Dict = rename_state_dict_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
__a : List[Any] = v.T
__a : List[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
__a : str = [k.endswith(_SCREAMING_SNAKE_CASE ) for ending in KEYS_TO_IGNORE]
if any(_SCREAMING_SNAKE_CASE ):
continue
__a : Any = REMAINING_PATTERNS
__a : Union[str, Any] = rename_state_dict_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
__a : Dict = v.T
__a : Optional[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
__a : Union[str, Any] = mapping['model.embed_positions.weight']
__a : Optional[Any] = mapping.pop('model.embed_positions.weight' )
__a , __a : Optional[Any] = torch_model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : Optional[int] = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
__a : str = {}
__a : int = ['global_step']
for name, shape in tqdm(_SCREAMING_SNAKE_CASE , desc='converting tf checkpoint to dict' ):
__a : List[Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
__a : Dict = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : List[Any] = array
return tf_weights
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Tuple ):
__a : int = get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE )
__a : Optional[Any] = convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
torch_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
__lowercase : List[str] = parser.parse_args()
__lowercase : Union[str, Any] = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 360
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = 'laion/clap-htsat-unfused'
__a : Optional[Any] = tempfile.mkdtemp()
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return RobertaTokenizer.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.get_tokenizer()
__a : List[str] = self.get_feature_extractor()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
processor.save_pretrained(self.tmpdirname )
__a : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__a : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__a : List[str] = self.get_feature_extractor(do_normalize=__a , padding_value=1.0 )
__a : Tuple = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : int = self.get_tokenizer()
__a : str = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : int = floats_list((3, 1000) )
__a : str = feature_extractor(__a , return_tensors='np' )
__a : int = processor(audios=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_feature_extractor()
__a : Any = self.get_tokenizer()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Union[str, Any] = 'This is a test string'
__a : Union[str, Any] = processor(text=__a )
__a : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : str = self.get_tokenizer()
__a : List[str] = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Optional[int] = processor.batch_decode(__a )
__a : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_feature_extractor()
__a : Optional[int] = self.get_tokenizer()
__a : int = ClapProcessor(tokenizer=__a , feature_extractor=__a )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
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'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=2 , __a=56 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=2 , __a=7 , __a="gelu_new" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=4 , __a="block_sparse" , __a=True , __a=False , __a=2 , __a=3 , ):
'''simple docstring'''
__a : List[Any] = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : Optional[int] = is_training
__a : Optional[Any] = use_attention_mask
__a : Dict = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : List[Any] = hidden_size
__a : Tuple = num_hidden_layers
__a : Any = num_attention_heads
__a : int = intermediate_size
__a : List[Any] = hidden_act
__a : Optional[Any] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Optional[Any] = max_position_embeddings
__a : Tuple = type_vocab_size
__a : Tuple = type_sequence_label_size
__a : Any = initializer_range
__a : List[Any] = num_choices
__a : str = rescale_embeddings
__a : Any = attention_type
__a : Optional[int] = use_bias
__a : Optional[Any] = block_size
__a : Any = num_random_blocks
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_attention_mask:
__a : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__a : Any = None
if self.use_token_type_ids:
__a : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : str = BigBirdConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__A , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.prepare_config_and_inputs()
__a : List[str] = config_and_inputs
__a : Optional[int] = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_flax
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
A_ = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_hidden_states_output()
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a : List[str] = model_class_name.from_pretrained('google/bigbird-roberta-base' )
self.assertIsNotNone(__A )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__a : Any = self._prepare_for_class(__A , __A )
__a : Optional[Any] = model_class(__A )
@jax.jit
def model_jitted(__a , __a=None , **__a ):
return model(input_ids=__A , attention_mask=__A , **__A )
with self.subTest('JIT Enabled' ):
__a : List[Any] = model_jitted(**__A ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
__a : str = model_jitted(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) )
for jitted_output, output in zip(__A , __A ):
self.assertEqual(jitted_output.shape , output.shape )
def __UpperCAmelCase ( self , __a , __a , __a , __a=1E-5 , __a="outputs" , __a=None ):
'''simple docstring'''
if name.startswith('outputs.attentions' ):
return
else:
super().check_pt_flax_outputs(__A , __A , __A , __A , __A , __A )
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|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : List[str] = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : str = hidden_size
__a : List[str] = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Tuple = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[int] = max_position_embeddings
__a : Dict = type_vocab_size
__a : Any = type_sequence_label_size
__a : Dict = initializer_range
__a : Optional[Any] = num_labels
__a : Optional[Any] = num_choices
__a : Union[str, Any] = relative_attention
__a : List[str] = position_biased_input
__a : List[Any] = pos_att_type
__a : Tuple = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : Any = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[int] = None
__a : int = None
__a : Dict = None
if self.use_labels:
__a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__a : str = model(__a , token_type_ids=__a )[0]
__a : Optional[int] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : int = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.num_labels
__a : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__a : Any = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : str = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : str = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__a : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Dict = config_and_inputs
__a : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = DebertaVaModelTester(self )
__a : List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : str = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__a : Optional[Any] = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : int = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__a : str = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
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|
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowercase : List[str] = '\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W15-3049\",\n doi = \"10.18653/v1/W15-3049\",\n pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n title = \"chr{F}++: words helping character n-grams\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n month = sep,\n year = \"2017\",\n address = \"Copenhagen, Denmark\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-4770\",\n doi = \"10.18653/v1/W17-4770\",\n pages = \"612--618\",\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'
__lowercase : List[Any] = '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'
__lowercase : int = '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n \'score\' (float): The chrF (chrF++) score,\n \'char_order\' (int): The character n-gram order,\n \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
raise ImportWarning(
'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
'You can install it with `pip install "sacrebleu>=1.4.12"`.' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf'] , reference_urls=[
'https://github.com/m-popovic/chrF',
] , )
def __UpperCAmelCase ( self , __a , __a , __a = CHRF.CHAR_ORDER , __a = CHRF.WORD_ORDER , __a = CHRF.BETA , __a = False , __a = False , __a = False , ):
'''simple docstring'''
__a : List[Any] = len(references[0] )
if any(len(__a ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
__a : List[Any] = [[refs[i] for refs in references] for i in range(__a )]
__a : List[Any] = CHRF(__a , __a , __a , __a , __a , __a )
__a : int = sb_chrf.corpus_score(__a , __a )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 362
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294
| 0
|
'''simple docstring'''
from ... import PretrainedConfig
__lowercase : Optional[Any] = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __UpperCamelCase ( _snake_case ):
A_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
A_ = "nezha"
def __init__( self , __a=2_1128 , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=64 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=0.1 , __a=0 , __a=2 , __a=3 , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
__a : Union[str, Any] = vocab_size
__a : Optional[int] = hidden_size
__a : str = num_hidden_layers
__a : int = num_attention_heads
__a : int = hidden_act
__a : Union[str, Any] = intermediate_size
__a : Optional[Any] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Optional[Any] = max_position_embeddings
__a : Tuple = max_relative_position
__a : Union[str, Any] = type_vocab_size
__a : List[str] = initializer_range
__a : Any = layer_norm_eps
__a : Dict = classifier_dropout
__a : Any = use_cache
| 363
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : int = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Tuple = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__lowercase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 364
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
A_ = 42
A_ = None
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : TreeNode | None ):
# Validation
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase ( a__ ):
A_ = ["image_processor", "tokenizer"]
A_ = "AutoImageProcessor"
A_ = "AutoTokenizer"
def __init__( self , __a , __a ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__a : Optional[Any] = self.image_processor
def __call__( self , __a=None , __a=None , __a=None , **__a ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
__a : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if images is not None:
__a : int = self.image_processor(SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is not None and images is not None:
__a : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_ ) , tensor_type=SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return ["input_ids", "attention_mask", "pixel_values"]
| 365
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294
| 0
|
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bytes ):
if len(lowerCAmelCase__ ) != 32:
raise ValueError('Input must be of length 32' )
__a : List[Any] = B''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if i < 0:
raise ValueError('Input must be non-negative' )
__a : List[Any] = format(lowerCAmelCase__ , '08x' )[-8:]
__a : str = B''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
return little_endian_hex
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bytes ):
__a : Union[str, Any] = B''
for char in message:
bit_string += format(lowerCAmelCase__ , '08b' ).encode('utf-8' )
__a : Any = format(len(lowerCAmelCase__ ) , '064b' ).encode('utf-8' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(lowerCAmelCase__ ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bytes ):
if len(lowerCAmelCase__ ) % 512 != 0:
raise ValueError('Input must have length that\'s a multiple of 512' )
for pos in range(0 , len(lowerCAmelCase__ ) , 512 ):
__a : str = bit_string[pos : pos + 512]
__a : int = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if i < 0:
raise ValueError('Input must be non-negative' )
__a : Optional[Any] = format(lowerCAmelCase__ , '032b' )
__a : int = ''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(lowerCAmelCase__ , 2 )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
return (a + b) % 2**32
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if i < 0:
raise ValueError('Input must be non-negative' )
if shift < 0:
raise ValueError('Shift must be non-negative' )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bytes ):
__a : str = preprocess(lowerCAmelCase__ )
__a : Dict = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
__a : Optional[int] = 0X67_45_23_01
__a : str = 0Xef_cd_ab_89
__a : Tuple = 0X98_ba_dc_fe
__a : List[str] = 0X10_32_54_76
__a : Union[str, Any] = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(lowerCAmelCase__ ):
__a : Optional[Any] = aa
__a : Dict = ba
__a : Tuple = ca
__a : Optional[Any] = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
__a : Tuple = d ^ (b & (c ^ d))
__a : Optional[int] = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
__a : int = c ^ (d & (b ^ c))
__a : List[str] = (5 * i + 1) % 16
elif i <= 47:
__a : List[Any] = b ^ c ^ d
__a : List[Any] = (3 * i + 5) % 16
else:
__a : Dict = c ^ (b | not_aa(lowerCAmelCase__ ))
__a : List[Any] = (7 * i) % 16
__a : int = (f + a + added_consts[i] + block_words[g]) % 2**32
__a : Tuple = d
__a : Optional[Any] = c
__a : Dict = b
__a : Dict = sum_aa(lowerCAmelCase__ , left_rotate_aa(lowerCAmelCase__ , shift_amounts[i] ) )
# Add hashed chunk to running total
__a : int = sum_aa(lowerCAmelCase__ , lowerCAmelCase__ )
__a : Any = sum_aa(lowerCAmelCase__ , lowerCAmelCase__ )
__a : Optional[int] = sum_aa(lowerCAmelCase__ , lowerCAmelCase__ )
__a : int = sum_aa(lowerCAmelCase__ , lowerCAmelCase__ )
__a : List[str] = reformat_hex(lowerCAmelCase__ ) + reformat_hex(lowerCAmelCase__ ) + reformat_hex(lowerCAmelCase__ ) + reformat_hex(lowerCAmelCase__ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
re.sub('<n>' , '' , _SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 294
| 0
|
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowercase : Any = TypeVar('KT')
__lowercase : Optional[int] = TypeVar('VT')
class __UpperCamelCase ( Generic[KT, VT] ):
def __init__( self , __a = "root" , __a = None ):
'''simple docstring'''
__a : Any = key
__a : Optional[int] = value
__a : list[Node[KT, VT]] = []
def __repr__( self ):
'''simple docstring'''
return f"""Node({self.key}: {self.value})"""
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return len(self.forward )
class __UpperCamelCase ( Generic[KT, VT] ):
def __init__( self , __a = 0.5 , __a = 16 ):
'''simple docstring'''
__a : Node[KT, VT] = Node[KT, VT]()
__a : str = 0
__a : List[Any] = p
__a : Optional[int] = max_level
def __str__( self ):
'''simple docstring'''
__a : List[Any] = list(self )
if len(__lowercase ) == 0:
return f"""SkipList(level={self.level})"""
__a : List[str] = max((len(str(__lowercase ) ) for item in items) , default=4 )
__a : Union[str, Any] = max(__lowercase , 4 ) + 4
__a : Optional[int] = self.head
__a : Optional[Any] = []
__a : List[str] = node.forward.copy()
lines.append(f"""[{node.key}]""".ljust(__lowercase , '-' ) + '* ' * len(__lowercase ) )
lines.append(' ' * label_size + '| ' * len(__lowercase ) )
while len(node.forward ) != 0:
__a : Tuple = node.forward[0]
lines.append(
f"""[{node.key}]""".ljust(__lowercase , '-' )
+ ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
lines.append(' ' * label_size + '| ' * len(__lowercase ) )
__a : List[str] = node.forward
lines.append('None'.ljust(__lowercase ) + '* ' * len(__lowercase ) )
return f"""SkipList(level={self.level})\n""" + "\n".join(__lowercase )
def __iter__( self ):
'''simple docstring'''
__a : Optional[int] = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
__a : Optional[Any] = node.forward[0]
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : List[Any] = []
__a : Dict = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
__a : int = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(__lowercase )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Optional[int] = self._locate_node(__lowercase )
if node is not None:
for i, update_node in enumerate(__lowercase ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
__a : Dict = node.forward[i]
else:
__a : List[str] = update_node.forward[:i]
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : Dict = self._locate_node(__lowercase )
if node is not None:
__a : Tuple = value
else:
__a : Union[str, Any] = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , __lowercase ):
update_vector.append(self.head )
__a : Optional[int] = level
__a : List[str] = Node(__lowercase , __lowercase )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(__lowercase )
else:
__a : Optional[int] = new_node
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : int = self._locate_node(__lowercase )
if node is not None:
return node.value
return None
def lowerCamelCase ():
__a : List[str] = SkipList()
skip_list.insert('Key1' , 3 )
skip_list.insert('Key2' , 12 )
skip_list.insert('Key3' , 41 )
skip_list.insert('Key4' , -19 )
__a : Optional[Any] = skip_list.head
__a : List[str] = {}
while node.level != 0:
__a : str = node.forward[0]
__a : Optional[int] = node.value
assert len(_SCREAMING_SNAKE_CASE ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def lowerCamelCase ():
__a : int = SkipList()
skip_list.insert('Key1' , 10 )
skip_list.insert('Key1' , 12 )
skip_list.insert('Key5' , 7 )
skip_list.insert('Key7' , 10 )
skip_list.insert('Key10' , 5 )
skip_list.insert('Key7' , 7 )
skip_list.insert('Key5' , 5 )
skip_list.insert('Key10' , 10 )
__a : int = skip_list.head
__a : Optional[Any] = {}
while node.level != 0:
__a : Union[str, Any] = node.forward[0]
__a : Any = node.value
if len(_SCREAMING_SNAKE_CASE ) != 4:
print()
assert len(_SCREAMING_SNAKE_CASE ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def lowerCamelCase ():
__a : Any = SkipList()
assert skip_list.find('Some key' ) is None
def lowerCamelCase ():
__a : List[Any] = SkipList()
skip_list.insert('Key2' , 20 )
assert skip_list.find('Key2' ) == 20
skip_list.insert('Some Key' , 10 )
skip_list.insert('Key2' , 8 )
skip_list.insert('V' , 13 )
assert skip_list.find('Y' ) is None
assert skip_list.find('Key2' ) == 8
assert skip_list.find('Some Key' ) == 10
assert skip_list.find('V' ) == 13
def lowerCamelCase ():
__a : Union[str, Any] = SkipList()
skip_list.delete('Some key' )
assert len(skip_list.head.forward ) == 0
def lowerCamelCase ():
__a : List[str] = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 14 )
skip_list.insert('Key2' , 15 )
skip_list.delete('V' )
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('Key2' ) is None
def lowerCamelCase ():
__a : str = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 14 )
skip_list.insert('Key2' , 15 )
skip_list.delete('V' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) == 14
assert skip_list.find('Key1' ) == 12
assert skip_list.find('Key2' ) == 15
skip_list.delete('X' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) == 12
assert skip_list.find('Key2' ) == 15
skip_list.delete('Key1' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) == 15
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) is None
def lowerCamelCase ():
__a : Union[str, Any] = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 142 )
skip_list.insert('Key2' , 15 )
skip_list.delete('X' )
def traverse_keys(_SCREAMING_SNAKE_CASE : List[str] ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(_SCREAMING_SNAKE_CASE )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def lowerCamelCase ():
def is_sorted(_SCREAMING_SNAKE_CASE : List[str] ):
return all(next_item >= item for item, next_item in zip(_SCREAMING_SNAKE_CASE , lst[1:] ) )
__a : Dict = SkipList()
for i in range(10 ):
skip_list.insert(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert is_sorted(list(_SCREAMING_SNAKE_CASE ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(_SCREAMING_SNAKE_CASE ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(_SCREAMING_SNAKE_CASE ) )
def lowerCamelCase ():
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def lowerCamelCase ():
__a : Union[str, Any] = SkipList()
skip_list.insert(2 , '2' )
skip_list.insert(4 , '4' )
skip_list.insert(6 , '4' )
skip_list.insert(4 , '5' )
skip_list.insert(8 , '4' )
skip_list.insert(9 , '4' )
skip_list.delete(4 )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 367
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__lowercase : Any = [0, 25, 50]
__lowercase : int = [25, 50, 75]
__lowercase : List[str] = fuzz.membership.trimf(X, abca)
__lowercase : Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowercase : List[Any] = np.ones(75)
__lowercase : Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowercase : str = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowercase : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowercase : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowercase : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowercase : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowercase : Union[str, Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 294
| 0
|
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__lowercase : List[str] = HfArgumentParser(InitializationArguments)
__lowercase : int = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
__lowercase : List[str] = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__lowercase : Optional[Any] = {
'vocab_size': len(tokenizer),
'scale_attn_by_inverse_layer_idx': True,
'reorder_and_upcast_attn': True,
}
# Load model config (GPT-2 large in this case)
__lowercase : int = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
__lowercase : Dict = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 368
|
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = 1
for digit in s:
product *= int(_SCREAMING_SNAKE_CASE )
return product
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = N ):
__a : Optional[int] = -sys.maxsize - 1
__a : Optional[Any] = n[:13]
__a : int = 13
while cur_index < len(_SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__a : List[Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__a : Dict = max(_SCREAMING_SNAKE_CASE , str_eval(_SCREAMING_SNAKE_CASE ) )
__a : Optional[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 294
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : str = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class __UpperCamelCase ( _UpperCamelCase ):
A_ = 'albert'
def __init__( self , __a=3_0000 , __a=128 , __a=4096 , __a=12 , __a=1 , __a=64 , __a=1_6384 , __a=1 , __a="gelu_new" , __a=0 , __a=0 , __a=512 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=0.1 , __a="absolute" , __a=0 , __a=2 , __a=3 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
__a : Optional[Any] = vocab_size
__a : List[str] = embedding_size
__a : Union[str, Any] = hidden_size
__a : List[Any] = num_hidden_layers
__a : List[Any] = num_hidden_groups
__a : int = num_attention_heads
__a : List[str] = inner_group_num
__a : int = hidden_act
__a : Optional[int] = intermediate_size
__a : List[str] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : str = type_vocab_size
__a : str = initializer_range
__a : Tuple = layer_norm_eps
__a : List[str] = classifier_dropout_prob
__a : str = position_embedding_type
class __UpperCamelCase ( _UpperCamelCase ):
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__a : Optional[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__a : Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 369
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = CodeGenTokenizer
A_ = CodeGenTokenizerFast
A_ = True
A_ = {"add_prefix_space": True}
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__a : Tuple = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
__a : Union[str, Any] = dict(zip(__a , range(len(__a ) ) ) )
__a : Tuple = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__a : Dict = {'unk_token': '<unk>'}
__a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Tuple = 'lower newer'
__a : Tuple = 'lower newer'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__a : str = 'lower newer'
__a : Tuple = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
__a : List[str] = tokens + [tokenizer.unk_token]
__a : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a : List[Any] = self.get_tokenizer()
__a : List[str] = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Any = 'lower newer'
# Testing tokenization
__a : Dict = tokenizer.tokenize(__a , add_prefix_space=__a )
__a : Dict = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
__a : int = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__a : Tuple = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
__a : Tuple = self.get_rust_tokenizer(add_prefix_space=__a )
__a : Union[str, Any] = tokenizer.encode(__a , add_prefix_space=__a )
__a : int = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
__a : Any = tokens + [rust_tokenizer.unk_token]
__a : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def __UpperCAmelCase ( self , *__a , **__a ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self , __a=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Optional[int] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
__a : List[Any] = 'This is a simple input'
__a : Tuple = ['This is a simple input 1', 'This is a simple input 2']
__a : Tuple = ('This is a simple input', 'This is a pair')
__a : str = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
__a : str = 'This is a simple input'
__a : Any = ['This is a simple input looooooooong', 'This is a simple input']
__a : Optional[int] = ('This is a simple input', 'This is a pair')
__a : Optional[Any] = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
__a : int = tokenizer.pad_token_id
__a : List[Any] = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
__a : Union[str, Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
__a : Optional[Any] = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
__a : List[Any] = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = '$$$'
__a : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
__a : Union[str, Any] = 'This is a simple input'
__a : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
__a : List[Any] = tokenizer.bos_token_id
__a : List[str] = tokenizer(__a )
__a : Optional[Any] = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__a : Any = tokenizer.decode(out_s.input_ids )
__a : Union[str, Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
__a : Optional[int] = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
__a : Tuple = '\nif len_a > len_b: result = a\nelse: result = b'
__a : Optional[int] = tokenizer.encode(__a )
__a : Union[str, Any] = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
__a : Tuple = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
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|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : str ):
# Base Case
if curr_ind == len(__SCREAMING_SNAKE_CASE ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(__SCREAMING_SNAKE_CASE ) ):
if valid_connection(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# Insert current vertex into path as next transition
__a : List[str] = next_ver
# Validate created path
if util_hamilton_cycle(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , curr_ind + 1 ):
return True
# Backtrack
__a : List[Any] = -1
return False
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] = 0 ):
__a : Any = [-1] * (len(__SCREAMING_SNAKE_CASE ) + 1)
# initialize start and end of path with starting index
__a : Union[str, Any] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 1 ) else []
| 370
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
__a : Dict = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(_SCREAMING_SNAKE_CASE )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
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|
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowercase : List[str] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__lowercase : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__lowercase : Any = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__lowercase : List[Any] = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.0_02,
'''sigma_max''': 80.0,
}
__lowercase : Optional[Any] = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.0_02,
'''sigma_max''': 80.0,
}
__lowercase : Dict = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.0_02,
'''sigma_max''': 80.0,
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('boolean value expected' )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str]=False ):
__a : Tuple = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
__a : Dict = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
__a : Any = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
__a : Tuple = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
__a : Optional[Any] = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
__a : Optional[Any] = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
__a : Optional[int] = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
__a : int = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
__a : Tuple = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
__a : List[str] = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
__a : Union[str, Any] = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
__a : str = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
__a , __a , __a : Optional[Any] = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
__a , __a , __a : Tuple = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
__a : Dict = checkpoint[F"""{old_prefix}.norm.weight"""]
__a : str = checkpoint[F"""{old_prefix}.norm.bias"""]
__a : List[str] = weight_q.squeeze(-1 ).squeeze(-1 )
__a : List[Any] = bias_q.squeeze(-1 ).squeeze(-1 )
__a : Any = weight_k.squeeze(-1 ).squeeze(-1 )
__a : List[Any] = bias_k.squeeze(-1 ).squeeze(-1 )
__a : Tuple = weight_v.squeeze(-1 ).squeeze(-1 )
__a : Dict = bias_v.squeeze(-1 ).squeeze(-1 )
__a : Dict = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
__a : Dict = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int ):
__a : int = torch.load(__lowerCAmelCase , map_location='cpu' )
__a : List[str] = {}
__a : Optional[int] = checkpoint['time_embed.0.weight']
__a : str = checkpoint['time_embed.0.bias']
__a : Optional[Any] = checkpoint['time_embed.2.weight']
__a : List[str] = checkpoint['time_embed.2.bias']
if unet_config["num_class_embeds"] is not None:
__a : str = checkpoint['label_emb.weight']
__a : List[str] = checkpoint['input_blocks.0.0.weight']
__a : Any = checkpoint['input_blocks.0.0.bias']
__a : Union[str, Any] = unet_config['down_block_types']
__a : int = unet_config['layers_per_block']
__a : Optional[int] = unet_config['attention_head_dim']
__a : List[str] = unet_config['block_out_channels']
__a : List[str] = 1
__a : List[str] = channels_list[0]
for i, layer_type in enumerate(__lowerCAmelCase ):
__a : Tuple = channels_list[i]
__a : Optional[Any] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(__lowerCAmelCase ):
__a : List[str] = F"""down_blocks.{i}.resnets.{j}"""
__a : Optional[Any] = F"""input_blocks.{current_layer}.0"""
__a : str = True if j == 0 and downsample_block_has_skip else False
__a : List[str] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(__lowerCAmelCase ):
__a : Dict = F"""down_blocks.{i}.resnets.{j}"""
__a : Union[str, Any] = F"""input_blocks.{current_layer}.0"""
__a : int = True if j == 0 and downsample_block_has_skip else False
__a : int = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
__a : int = F"""down_blocks.{i}.attentions.{j}"""
__a : Dict = F"""input_blocks.{current_layer}.1"""
__a : List[str] = convert_attention(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
__a : Optional[int] = F"""down_blocks.{i}.downsamplers.0"""
__a : Dict = F"""input_blocks.{current_layer}.0"""
__a : str = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
__a : Any = current_channels
# hardcoded the mid-block for now
__a : List[str] = 'mid_block.resnets.0'
__a : Optional[int] = 'middle_block.0'
__a : Optional[int] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__a : str = 'mid_block.attentions.0'
__a : Any = 'middle_block.1'
__a : List[Any] = convert_attention(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__a : List[str] = 'mid_block.resnets.1'
__a : str = 'middle_block.2'
__a : Optional[Any] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__a : List[Any] = 0
__a : Dict = unet_config['up_block_types']
for i, layer_type in enumerate(__lowerCAmelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
__a : Any = F"""up_blocks.{i}.resnets.{j}"""
__a : Tuple = F"""output_blocks.{current_layer}.0"""
__a : int = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
__a : int = F"""up_blocks.{i}.upsamplers.0"""
__a : Dict = F"""output_blocks.{current_layer-1}.1"""
__a : List[Any] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
__a : str = F"""up_blocks.{i}.resnets.{j}"""
__a : Dict = F"""output_blocks.{current_layer}.0"""
__a : Any = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
__a : Optional[Any] = F"""up_blocks.{i}.attentions.{j}"""
__a : Union[str, Any] = F"""output_blocks.{current_layer}.1"""
__a : str = convert_attention(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
__a : Any = F"""up_blocks.{i}.upsamplers.0"""
__a : str = F"""output_blocks.{current_layer-1}.2"""
__a : List[str] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
__a : Tuple = checkpoint['out.0.weight']
__a : Optional[Any] = checkpoint['out.0.bias']
__a : int = checkpoint['out.2.weight']
__a : List[str] = checkpoint['out.2.bias']
return new_checkpoint
if __name__ == "__main__":
__lowercase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
)
parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')
__lowercase : Union[str, Any] = parser.parse_args()
__lowercase : Dict = strabool(args.class_cond)
__lowercase : Dict = os.path.basename(args.unet_path)
print(f'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__lowercase : Optional[Any] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__lowercase : Optional[Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__lowercase : Optional[int] = TEST_UNET_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__lowercase : List[Any] = None
__lowercase : Union[str, Any] = con_pt_to_diffuser(args.unet_path, unet_config)
__lowercase : Dict = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__lowercase : Dict = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__lowercase : List[Any] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__lowercase : List[Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
__lowercase : Dict = CMStochasticIterativeScheduler(**scheduler_config)
__lowercase : Optional[Any] = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 371
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , ):
'''simple docstring'''
__a : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__a : int = parent
__a : Dict = batch_size
__a : Optional[int] = num_channels
__a : List[Any] = image_size
__a : Tuple = min_resolution
__a : str = max_resolution
__a : str = do_resize
__a : Optional[Any] = size
__a : str = apply_ocr
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LayoutLMvaImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'apply_ocr' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
__a : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
__a : Any = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__a : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : Tuple = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__a : List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
__a : List[str] = image_processing(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
__a : str = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
__a : Tuple = Image.open(ds[0]['file'] ).convert('RGB' )
__a : Optional[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__a : Optional[Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__a : Union[str, Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
__a : List[Any] = LayoutLMvaImageProcessor(apply_ocr=__a )
__a : List[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
# Check if the input is valid
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
__a : Optional[int] = equationa
__a : List[Any] = equationa
# Calculate the determinants of the matrices
__a : Dict = aa * ba - aa * ba
__a : Union[str, Any] = ca * ba - ca * ba
__a : str = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__a : Any = determinant_x / determinant
__a : Union[str, Any] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 350
|
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "ernie_m"
A_ = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __a = 25_0002 , __a = 768 , __a = 12 , __a = 12 , __a = 3072 , __a = "gelu" , __a = 0.1 , __a = 0.1 , __a = 514 , __a = 0.02 , __a = 1 , __a = 1E-0_5 , __a=None , __a=False , __a=0.0 , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
__a : int = vocab_size
__a : Dict = hidden_size
__a : str = num_hidden_layers
__a : Dict = num_attention_heads
__a : List[str] = intermediate_size
__a : Union[str, Any] = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : int = initializer_range
__a : Dict = layer_norm_eps
__a : int = classifier_dropout
__a : Dict = is_decoder
__a : int = act_dropout
| 294
| 0
|
'''simple docstring'''
from math import factorial
__lowercase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueError('Parameter number must be greater than or equal to 0' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(_SCREAMING_SNAKE_CASE ) )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 60 , _SCREAMING_SNAKE_CASE : int = 1_000_000 ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('Parameters chain_length and number_limit must be int' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'Parameters chain_length and number_limit must be greater than 0' )
# the counter for the chains with the exact desired length
__a : Optional[Any] = 0
# the cached sizes of the previous chains
__a : dict[int, int] = {}
for start_chain_element in range(1 , _SCREAMING_SNAKE_CASE ):
# The temporary set will contain the elements of the chain
__a : Union[str, Any] = set()
__a : Dict = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__a : Any = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(_SCREAMING_SNAKE_CASE )
chain_set_length += 1
__a : int = digit_factorial_sum(_SCREAMING_SNAKE_CASE )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__a : str = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution()}''')
| 351
|
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a ):
'''simple docstring'''
super().__init__()
__a : int = module
__a : List[Any] = nn.Sequential(
nn.Linear(module.in_features , __a , bias=__a ) , nn.Linear(__a , module.out_features , bias=__a ) , )
__a : int = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=__a )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __UpperCAmelCase ( self , __a , *__a , **__a ):
'''simple docstring'''
return self.module(__a , *__a , **__a ) + self.adapter(__a )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
A_ = "bigscience/bloom-1b7"
# Constant values
A_ = 2.109659552692574
A_ = "Hello my name is"
A_ = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
A_ = 10
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = AutoTokenizer.from_pretrained(self.model_name )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Models and tokenizer
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto' )
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_abit.config
self.assertTrue(hasattr(__a , 'quantization_config' ) )
__a : Union[str, Any] = config.to_dict()
__a : Tuple = config.to_diff_dict()
__a : Tuple = config.to_json_string()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
__a : List[Any] = self.model_fpaa.get_memory_footprint()
__a : List[Any] = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
__a : Tuple = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(__a , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Union[str, Any] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BitsAndBytesConfig()
__a : Tuple = True
__a : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' )
__a : List[Any] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = BitsAndBytesConfig()
with self.assertRaises(__a ):
__a : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=__a , load_in_abit=__a , device_map='auto' , bnb_abit_quant_type='nf4' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
with self.assertRaises(__a ):
# Tries with `str`
self.model_abit.to('cpu' )
with self.assertRaises(__a ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0' ) )
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(__a ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
__a : List[str] = self.tokenizer(self.input_text , return_tensors='pt' )
__a : Optional[int] = self.model_fpaa.to(torch.floataa )
__a : Tuple = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
__a : List[Any] = self.model_fpaa.to('cpu' )
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.half()
# Check this does not throw an error
__a : Union[str, Any] = self.model_fpaa.float()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=__a , device_map='auto' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ):
'''simple docstring'''
__a : Any = 't5-small'
__a : Tuple = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
__a : int = AutoTokenizer.from_pretrained(cls.model_name )
__a : Union[str, Any] = 'Translate in German: Hello, my dog is cute'
def __UpperCAmelCase ( self ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from transformers import TaForConditionalGeneration
__a : Optional[int] = TaForConditionalGeneration._keep_in_fpaa_modules
__a : List[str] = None
# test with `t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : Any = model.generate(**__a )
# test with `flan-t5-small`
__a : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[Any] = model.generate(**__a )
__a : Optional[int] = modules
def __UpperCAmelCase ( self ):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
__a : str = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : List[str] = model.generate(**__a )
# test with `flan-t5-small`
__a : List[Any] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=__a , device_map='auto' )
__a : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt' ).to(0 )
__a : int = model.generate(**__a )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# model_name
__a : List[Any] = 'bigscience/bloom-560m'
__a : Union[str, Any] = 't5-small'
# Different types of model
__a : Optional[Any] = AutoModel.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Sequence classification model
__a : Dict = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=__a , device_map='auto' )
# CausalLM model
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a , device_map='auto' )
# Seq2seq model
__a : Any = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=__a , device_map='auto' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
__a : str = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=__a , device_map='balanced' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
__a : List[Any] = self.tokenizer(self.input_text , return_tensors='pt' )
# Second real batch
__a : str = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=__a ) , self.EXPECTED_OUTPUTS )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = 'facebook/opt-350m'
super().setUp()
def __UpperCAmelCase ( self ):
'''simple docstring'''
if version.parse(importlib.metadata.version('bitsandbytes' ) ) < version.parse('0.37.0' ):
return
# Step 1: freeze all parameters
__a : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=__a )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
__a : Tuple = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
__a : Tuple = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(__a ) ):
__a : str = LoRALayer(module.q_proj , rank=16 )
__a : str = LoRALayer(module.k_proj , rank=16 )
__a : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
__a : List[str] = self.tokenizer('Test batch ' , return_tensors='pt' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
__a : int = model.forward(**__a )
out.logits.norm().backward()
for module in model.modules():
if isinstance(__a , __a ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(__a , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "gpt2-xl"
A_ = 3.3191854854152187
| 294
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|
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
"""simple docstring"""
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=24 , __a=2 , __a=6 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=None , __a=1000 , ):
'''simple docstring'''
__a : str = parent
__a : Optional[int] = batch_size
__a : List[Any] = seq_length
__a : Dict = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Optional[int] = use_labels
__a : List[str] = vocab_size
__a : Dict = hidden_size
__a : Union[str, Any] = num_hidden_layers
__a : List[str] = num_attention_heads
__a : Any = intermediate_size
__a : Any = hidden_act
__a : Optional[Any] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : Dict = max_position_embeddings
__a : str = type_vocab_size
__a : Dict = type_sequence_label_size
__a : str = initializer_range
__a : List[Any] = num_labels
__a : str = scope
__a : Tuple = range_bbox
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : int = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__a : Optional[int] = bbox[i, j, 3]
__a : Optional[int] = bbox[i, j, 1]
__a : List[str] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__a : Optional[int] = bbox[i, j, 2]
__a : Optional[Any] = bbox[i, j, 0]
__a : int = t
__a : Any = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : List[Any] = None
if self.use_token_type_ids:
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Tuple = None
__a : int = None
if self.use_labels:
__a : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : str = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : Optional[int] = LiltModel(config=__a )
model.to(__a )
model.eval()
__a : Any = model(__a , bbox=__a , attention_mask=__a , token_type_ids=__a )
__a : List[str] = model(__a , bbox=__a , token_type_ids=__a )
__a : Any = model(__a , bbox=__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : Optional[int] = self.num_labels
__a : str = LiltForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(
__a , bbox=__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ):
'''simple docstring'''
__a : str = LiltForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : int = model(
__a , bbox=__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.prepare_config_and_inputs()
(
__a
) : Tuple = config_and_inputs
__a : int = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a ):
'''simple docstring'''
return True
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LiltModelTester(self )
__a : Union[str, Any] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__a : Dict = type
self.model_tester.create_and_check_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Optional[int] = LiltModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(__a )
__a : List[Any] = torch.tensor([[1, 2]] , device=__a )
__a : List[Any] = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__a )
# forward pass
with torch.no_grad():
__a : Union[str, Any] = model(input_ids=__a , bbox=__a )
__a : Union[str, Any] = torch.Size([1, 2, 768] )
__a : Dict = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=__a , )
self.assertTrue(outputs.last_hidden_state.shape , __a )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __a , atol=1E-3 ) )
| 352
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowercase = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , *__a , **__a ):
'''simple docstring'''
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , __a , )
super().__init__(*__a , **__a )
| 353
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = BertTokenizer
A_ = BertTokenizerFast
A_ = True
A_ = True
A_ = filter_non_english
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
__a : Union[str, Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__a : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Union[str, Any] = 'UNwant\u00E9d,running'
__a : List[str] = 'unwanted, running'
return input_text, output_text
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.tokenizer_class(self.vocab_file )
__a : int = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(__a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__a : Union[str, Any] = self.get_tokenizer()
__a : Dict = self.get_rust_tokenizer()
__a : int = 'UNwant\u00E9d,running'
__a : Tuple = tokenizer.tokenize(__a )
__a : List[Any] = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : Tuple = tokenizer.encode(__a , add_special_tokens=__a )
__a : str = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : Dict = self.get_rust_tokenizer()
__a : int = tokenizer.encode(__a )
__a : Tuple = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# With lower casing
__a : Optional[int] = self.get_tokenizer(do_lower_case=__a )
__a : Any = self.get_rust_tokenizer(do_lower_case=__a )
__a : List[str] = 'UNwant\u00E9d,running'
__a : int = tokenizer.tokenize(__a )
__a : List[Any] = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : Any = tokenizer.encode(__a , add_special_tokens=__a )
__a : str = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
__a : Dict = self.get_rust_tokenizer()
__a : Optional[Any] = tokenizer.encode(__a )
__a : Optional[int] = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = BasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = BasicTokenizer()
__a : Union[str, Any] = 'a\n\'ll !!to?\'d of, can\'t.'
__a : Any = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(__a ) , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__a : str = {}
for i, token in enumerate(__a ):
__a : Union[str, Any] = i
__a : Dict = WordpieceTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.get_tokenizer()
__a : List[str] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(__a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.tokenizer_class.from_pretrained('bert-base-uncased' )
__a : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__a : Tuple = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__a : Any = tokenizer.build_inputs_with_special_tokens(__a )
__a : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def __UpperCAmelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : str = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : Dict = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
__a : str = tokenizer_r.encode_plus(
__a , return_attention_mask=__a , return_token_type_ids=__a , return_offsets_mapping=__a , add_special_tokens=__a , )
__a : Optional[int] = tokenizer_r.do_lower_case if hasattr(__a , 'do_lower_case' ) else False
__a : int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = ['的', '人', '有']
__a : Optional[Any] = ''.join(__a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : List[Any] = True
__a : Union[str, Any] = self.tokenizer_class.from_pretrained(__a , **__a )
__a : Tuple = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : Optional[int] = tokenizer_p.encode(__a , add_special_tokens=__a )
__a : str = tokenizer_r.encode(__a , add_special_tokens=__a )
__a : Tuple = tokenizer_r.convert_ids_to_tokens(__a )
__a : Any = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
__a : Optional[int] = False
__a : List[Any] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : List[Any] = self.tokenizer_class.from_pretrained(__a , **__a )
__a : Tuple = tokenizer_r.encode(__a , add_special_tokens=__a )
__a : Union[str, Any] = tokenizer_p.encode(__a , add_special_tokens=__a )
__a : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(__a )
__a : str = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that only the first Chinese character is not preceded by "##".
__a : int = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(__a )
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
| 354
|
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowercase : str = logging.get_logger(__name__)
# General docstring
__lowercase : List[str] = 'MobileNetV1Config'
# Base docstring
__lowercase : Tuple = 'google/mobilenet_v1_1.0_224'
__lowercase : List[Any] = [1, 10_24, 7, 7]
# Image classification docstring
__lowercase : int = 'google/mobilenet_v1_1.0_224'
__lowercase : Any = 'tabby, tabby cat'
__lowercase : Dict = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any]=None ):
__a : Dict = {}
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = model.mobilenet_va
else:
__a : List[Any] = model
__a : Dict = 'MobilenetV1/Conv2d_0/'
__a : Dict = backbone.conv_stem.convolution.weight
__a : Optional[Any] = backbone.conv_stem.normalization.bias
__a : int = backbone.conv_stem.normalization.weight
__a : int = backbone.conv_stem.normalization.running_mean
__a : Tuple = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__a : int = i + 1
__a : Dict = i * 2
__a : Dict = backbone.layer[pt_index]
__a : Dict = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__a : Union[str, Any] = pointer.convolution.weight
__a : Optional[Any] = pointer.normalization.bias
__a : Union[str, Any] = pointer.normalization.weight
__a : List[Any] = pointer.normalization.running_mean
__a : Tuple = pointer.normalization.running_var
__a : List[str] = backbone.layer[pt_index + 1]
__a : Optional[Any] = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__a : Optional[int] = pointer.convolution.weight
__a : List[str] = pointer.normalization.bias
__a : Dict = pointer.normalization.weight
__a : Dict = pointer.normalization.running_mean
__a : Optional[int] = pointer.normalization.running_var
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Any = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__a : Optional[int] = model.classifier.weight
__a : List[Any] = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__a : Union[str, Any] = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
__a : List[str] = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Optional[Any] = array
# Build TF to PyTorch weights loading map
__a : Optional[int] = _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
__a : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__a : Optional[Any] = np.transpose(_SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__a : Union[str, Any] = array.squeeze().transpose()
else:
__a : Dict = np.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
__a : List[str] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
tf_weights.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/RMSProp_1' , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '/ExponentialMovingAverage' , _SCREAMING_SNAKE_CASE )
logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}""" )
return model
def lowerCamelCase (_SCREAMING_SNAKE_CASE : torch.Tensor , _SCREAMING_SNAKE_CASE : nn.Convad ):
__a , __a : Any = features.shape[-2:]
__a , __a : int = conv_layer.stride
__a , __a : Any = conv_layer.kernel_size
if in_height % stride_height == 0:
__a : int = max(kernel_height - stride_height , 0 )
else:
__a : int = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__a : Any = max(kernel_width - stride_width , 0 )
else:
__a : str = max(kernel_width - (in_width % stride_width) , 0 )
__a : int = pad_along_width // 2
__a : Dict = pad_along_width - pad_left
__a : List[str] = pad_along_height // 2
__a : Union[str, Any] = pad_along_height - pad_top
__a : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'constant' , 0.0 )
class __UpperCamelCase ( nn.Module ):
def __init__( self , __a , __a , __a , __a , __a = 1 , __a = 1 , __a = False , __a = True , __a = True , ):
'''simple docstring'''
super().__init__()
__a : Optional[int] = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
__a : Dict = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
__a : Union[str, Any] = nn.Convad(
in_channels=__a , out_channels=__a , kernel_size=__a , stride=__a , padding=__a , groups=__a , bias=__a , padding_mode='zeros' , )
if use_normalization:
__a : List[str] = nn.BatchNormad(
num_features=__a , eps=config.layer_norm_eps , momentum=0.9997 , affine=__a , track_running_stats=__a , )
else:
__a : Tuple = None
if use_activation:
if isinstance(__a , __a ):
__a : Tuple = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __a ):
__a : Union[str, Any] = ACTaFN[config.hidden_act]
else:
__a : Dict = config.hidden_act
else:
__a : List[Any] = None
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if self.config.tf_padding:
__a : Union[str, Any] = apply_tf_padding(__a , self.convolution )
__a : Union[str, Any] = self.convolution(__a )
if self.normalization is not None:
__a : str = self.normalization(__a )
if self.activation is not None:
__a : Optional[int] = self.activation(__a )
return features
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = MobileNetVaConfig
A_ = load_tf_weights_in_mobilenet_va
A_ = "mobilenet_v1"
A_ = "pixel_values"
A_ = False
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__lowercase : Any = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowercase : Optional[int] = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a = True ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[int] = config
__a : str = 32
__a : Dict = max(int(depth * config.depth_multiplier ) , config.min_depth )
__a : Union[str, Any] = MobileNetVaConvLayer(
__a , in_channels=config.num_channels , out_channels=__a , kernel_size=3 , stride=2 , )
__a : Tuple = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__a : Any = nn.ModuleList()
for i in range(13 ):
__a : Union[str, Any] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__a : List[Any] = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=3 , stride=strides[i] , groups=__a , ) )
self.layer.append(
MobileNetVaConvLayer(
__a , in_channels=__a , out_channels=__a , kernel_size=1 , ) )
__a : Optional[int] = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__a : int = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__a : Union[str, Any] = self.conv_stem(__a )
__a : Any = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
__a : List[str] = layer_module(__a )
if output_hidden_states:
__a : List[Any] = all_hidden_states + (hidden_states,)
__a : str = hidden_states
if self.pooler is not None:
__a : Union[str, Any] = torch.flatten(self.pooler(__a ) , start_dim=1 )
else:
__a : int = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__a , pooler_output=__a , hidden_states=__a , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCAmelCase_ , )
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Tuple = config.num_labels
__a : Tuple = MobileNetVaModel(__a )
__a : Optional[int] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__a : Any = nn.Dropout(config.classifier_dropout_prob , inplace=__a )
__a : Any = nn.Linear(__a , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Dict = self.mobilenet_va(__a , output_hidden_states=__a , return_dict=__a )
__a : List[str] = outputs.pooler_output if return_dict else outputs[1]
__a : int = self.classifier(self.dropout(__a ) )
__a : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__a : str = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__a : int = 'single_label_classification'
else:
__a : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__a : Optional[Any] = MSELoss()
if self.num_labels == 1:
__a : List[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__a : Any = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__a : List[str] = CrossEntropyLoss()
__a : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__a : Tuple = BCEWithLogitsLoss()
__a : Optional[int] = loss_fct(__a , __a )
if not return_dict:
__a : List[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__a , logits=__a , hidden_states=outputs.hidden_states , )
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from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 355
|
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowercase : str = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "dhaka" , _SCREAMING_SNAKE_CASE : int = 5 ):
__a : Optional[Any] = min(_SCREAMING_SNAKE_CASE , 50 ) # Prevent abuse!
__a : Optional[Any] = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
__a : Tuple = requests.get('https://www.google.com/search' , params=_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE )
__a : Dict = BeautifulSoup(html.text , 'html.parser' )
__a : List[str] = ''.join(
re.findall(r'AF_initDataCallback\(([^<]+)\);' , str(soup.select('script' ) ) ) )
__a : Optional[Any] = json.dumps(_SCREAMING_SNAKE_CASE )
__a : List[str] = json.loads(_SCREAMING_SNAKE_CASE )
__a : List[Any] = re.findall(
r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",' , _SCREAMING_SNAKE_CASE , )
if not matched_google_image_data:
return 0
__a : Tuple = re.sub(
r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]' , '' , str(_SCREAMING_SNAKE_CASE ) , )
__a : Optional[Any] = re.findall(
r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]' , _SCREAMING_SNAKE_CASE , )
for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
__a : List[str] = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Tuple = bytes(_SCREAMING_SNAKE_CASE , 'ascii' ).decode(
'unicode-escape' )
__a : Dict = urllib.request.build_opener()
__a : Union[str, Any] = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_SCREAMING_SNAKE_CASE )
__a : List[Any] = F"""query_{query.replace(" " , "_" )}"""
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
_SCREAMING_SNAKE_CASE , F"""{path_name}/original_size_img_{index}.jpg""" )
return index
if __name__ == "__main__":
try:
__lowercase : Optional[int] = download_images_from_google_query(sys.argv[1])
print(f'''{image_count} images were downloaded to disk.''')
except IndexError:
print('Please provide a search term.')
raise
| 294
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|
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase ():
__a : str = 9
__a : List[str] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__a : Tuple = kruskal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Tuple = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_SCREAMING_SNAKE_CASE ) == sorted(_SCREAMING_SNAKE_CASE )
| 356
|
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split(',' )
names.sort()
__a : Union[str, Any] = 0
__a : Tuple = 0
for i, name in enumerate(_SCREAMING_SNAKE_CASE ):
for letter in name:
name_score += ord(_SCREAMING_SNAKE_CASE ) - 64
total_score += (i + 1) * name_score
__a : Any = 0
return total_score
if __name__ == "__main__":
print(solution())
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|
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
__lowercase : Any = {
'169M': 12,
'430M': 24,
'1B5': 24,
'3B': 32,
'7B': 32,
'14B': 40,
}
__lowercase : Union[str, Any] = {
'169M': 7_68,
'430M': 10_24,
'1B5': 20_48,
'3B': 25_60,
'7B': 40_96,
'14B': 51_20,
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] ):
__a : List[str] = list(state_dict.keys() )
for name in state_dict_keys:
__a : Optional[Any] = state_dict.pop(_SCREAMING_SNAKE_CASE )
# emb -> embedding
if name.startswith('emb.' ):
__a : int = name.replace('emb.' , 'embeddings.' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('blocks.0.ln0' ):
__a : Dict = name.replace('blocks.0.ln0' , 'blocks.0.pre_ln' )
# att -> attention
__a : Optional[int] = re.sub(r'blocks\.(\d+)\.att' , r'blocks.\1.attention' , _SCREAMING_SNAKE_CASE )
# ffn -> feed_forward
__a : Tuple = re.sub(r'blocks\.(\d+)\.ffn' , r'blocks.\1.feed_forward' , _SCREAMING_SNAKE_CASE )
# time_mix_k -> time_mix_key and reshape
if name.endswith('.time_mix_k' ):
__a : str = name.replace('.time_mix_k' , '.time_mix_key' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('.time_mix_v' ):
__a : List[Any] = name.replace('.time_mix_v' , '.time_mix_value' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('.time_mix_r' ):
__a : Any = name.replace('.time_mix_r' , '.time_mix_receptance' )
if name != "head.weight":
__a : int = 'rwkv.' + name
__a : Optional[int] = weight
return state_dict
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any=None , _SCREAMING_SNAKE_CASE : Optional[Any]=None , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : List[Any]=None ):
# 1. If possible, build the tokenizer.
if tokenizer_file is None:
print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
__a : Union[str, Any] = 50_277
__a : Optional[Any] = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
else:
__a : Optional[Any] = PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE )
__a : int = len(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
# 2. Build the config
__a : Any = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
__a : int = candidate
break
if size is None:
raise ValueError('Could not infer the size, please provide it with the `--size` argument.' )
if size not in possible_sizes:
raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}.""" )
__a : Optional[int] = RwkvConfig(
vocab_size=_SCREAMING_SNAKE_CASE , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(_SCREAMING_SNAKE_CASE )
# 3. Download model file then convert state_dict
__a : List[str] = hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )
__a : Any = convert_state_dict(_SCREAMING_SNAKE_CASE )
# 4. Split in shards and save
__a : Any = shard_checkpoint(_SCREAMING_SNAKE_CASE )
for shard_file, shard in shards.items():
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if index is not None:
__a : int = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Save the index as well
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as f:
__a : Union[str, Any] = json.dumps(_SCREAMING_SNAKE_CASE , indent=2 , sort_keys=_SCREAMING_SNAKE_CASE ) + '\n'
f.write(_SCREAMING_SNAKE_CASE )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.' )
__a : Dict = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
__a : int = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('Please provide a `model_name` to push the model to the Hub.' )
__a : Optional[int] = AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE , max_shard_size='2GB' )
tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
)
parser.add_argument(
'--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
)
parser.add_argument(
'--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
)
parser.add_argument(
'--tokenizer_file',
default=None,
type=str,
help='Path to the tokenizer file to use (if not provided, only the model is converted).',
)
parser.add_argument(
'--size',
default=None,
type=str,
help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Push to the Hub the converted model.',
)
parser.add_argument(
'--model_name',
default=None,
type=str,
help='Name of the pushed model on the Hub, including the username / organization.',
)
__lowercase : Union[str, Any] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 357
|
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
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|
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class __UpperCamelCase :
def __init__( self , __a , __a=None , __a=None , __a=None , __a="resnet50" , __a=3 , __a=32 , __a=3 , __a=True , __a=True , ):
'''simple docstring'''
__a : List[Any] = parent
__a : Dict = out_indices if out_indices is not None else [4]
__a : str = stage_names
__a : int = out_features
__a : List[str] = backbone
__a : List[Any] = batch_size
__a : Tuple = image_size
__a : Dict = num_channels
__a : int = use_pretrained_backbone
__a : int = is_training
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : Any = self.get_config()
return config, pixel_values
def __UpperCAmelCase ( self ):
'''simple docstring'''
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : List[Any] = TimmBackbone(config=__a )
model.to(__a )
model.eval()
with torch.no_grad():
__a : int = model(__a )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.prepare_config_and_inputs()
__a : int = config_and_inputs
__a : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (TimmBackbone,) if is_torch_available() else ()
A_ = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = TimmBackboneModelTester(self )
__a : Any = ConfigTester(self , config_class=__a , has_text_modality=__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = 'resnet18'
__a : str = 'microsoft/resnet-18'
__a : Union[str, Any] = AutoBackbone.from_pretrained(__a , use_timm_backbone=__a )
__a : str = AutoBackbone.from_pretrained(__a )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
__a : Optional[int] = AutoBackbone.from_pretrained(__a , use_timm_backbone=__a , out_indices=[1, 2, 3] )
__a : str = AutoBackbone.from_pretrained(__a , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('TimmBackbone doesn\'t support feed forward chunking' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone initialization is managed on the timm side' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t support output_attentions.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Safetensors is not supported by timm.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[int] = model_class(__a )
__a : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : Optional[Any] = [*signature.parameters.keys()]
__a : Union[str, Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__a : Optional[Any] = True
__a : Optional[Any] = self.has_attentions
# no need to test all models as different heads yield the same functionality
__a : Tuple = self.all_model_classes[0]
__a : int = model_class(__a )
model.to(__a )
__a : int = self._prepare_for_class(__a , __a )
__a : Any = model(**__a )
__a : Tuple = outputs[0][-1]
# Encoder-/Decoder-only models
__a : List[str] = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
__a : Union[str, Any] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=__a )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : int = model_class(__a )
model.to(__a )
model.eval()
__a : List[str] = model(**__a )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
__a : Dict = copy.deepcopy(__a )
__a : Tuple = None
__a : Any = model_class(__a )
model.to(__a )
model.eval()
__a : List[str] = model(**__a )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
__a : Union[str, Any] = copy.deepcopy(__a )
__a : Union[str, Any] = False
__a : Any = model_class(__a )
model.to(__a )
model.eval()
__a : Union[str, Any] = model(**__a )
| 358
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , __a , __a=13 , __a=3 , __a=True , __a=True , __a=0.1 , __a=0.1 , __a=224 , __a=1000 , __a=[3, 3, 6, 4] , __a=[48, 56, 112, 220] , ):
'''simple docstring'''
__a : Optional[int] = parent
__a : Dict = batch_size
__a : Dict = num_channels
__a : str = is_training
__a : Optional[int] = use_labels
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[Any] = num_labels
__a : Tuple = image_size
__a : Optional[Any] = layer_depths
__a : Dict = embed_dims
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : int = None
if self.use_labels:
__a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
__a : List[str] = self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='gelu' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=__a , layer_scale_init_value=1E-5 , )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : int = SwiftFormerModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[Any] = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : Tuple = self.num_labels
__a : Optional[int] = SwiftFormerForImageClassification(__a )
model.to(__a )
model.eval()
__a : int = model(__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
__a : int = SwiftFormerForImageClassification(__a )
model.to(__a )
model.eval()
__a : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : Optional[Any] = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
(__a) : Tuple = self.prepare_config_and_inputs()
__a : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
A_ = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = SwiftFormerModelTester(self )
__a : Dict = ConfigTester(
self , config_class=__a , has_text_modality=__a , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='SwiftFormer does not use inputs_embeds' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[Any] = model_class(__a )
__a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : List[Any] = model_class(__a )
__a : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : Optional[int] = [*signature.parameters.keys()]
__a : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : int = SwiftFormerModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@unittest.skip(reason='SwiftFormer does not output attentions' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a ):
__a : Optional[Any] = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
__a : Union[str, Any] = model(**self._prepare_for_class(__a , __a ) )
__a : Any = outputs.hidden_states
__a : Tuple = 8
self.assertEqual(len(__a ) , __a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(__a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
__a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Any = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a : Union[str, Any] = True
check_hidden_states_output(__a , __a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
def _config_zero_init(__a ):
__a : List[Any] = copy.deepcopy(__a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(__a , __a , 1E-1_0 )
if isinstance(getattr(__a , __a , __a ) , __a ):
__a : List[Any] = _config_zero_init(getattr(__a , __a ) )
setattr(__a , __a , __a )
return configs_no_init
__a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__a : Optional[Any] = _config_zero_init(__a )
for model_class in self.all_model_classes:
__a : Tuple = model_class(config=__a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ():
__a : Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs' ) if is_vision_available() else None
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs' ).to(__a )
__a : Optional[Any] = self.default_image_processor
__a : Tuple = prepare_img()
__a : int = image_processor(images=__a , return_tensors='pt' ).to(__a )
# forward pass
with torch.no_grad():
__a : Optional[Any] = model(**__a )
# verify the logits
__a : Tuple = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
__a : int = torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]] ).to(__a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1E-4 ) )
| 359
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 294
| 0
|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : Tuple = SwinConfig(image_size=192 )
if "base" in model_name:
__a : Optional[int] = 6
__a : Optional[Any] = 128
__a : Union[str, Any] = (2, 2, 18, 2)
__a : Tuple = (4, 8, 16, 32)
elif "large" in model_name:
__a : List[Any] = 12
__a : str = 192
__a : List[str] = (2, 2, 18, 2)
__a : Optional[int] = (6, 12, 24, 48)
else:
raise ValueError('Model not supported, only supports base and large variants' )
__a : Tuple = window_size
__a : Optional[int] = embed_dim
__a : str = depths
__a : Dict = num_heads
return config
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
if "encoder.mask_token" in name:
__a : List[Any] = name.replace('encoder.mask_token' , 'embeddings.mask_token' )
if "encoder.patch_embed.proj" in name:
__a : int = name.replace('encoder.patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "encoder.patch_embed.norm" in name:
__a : List[str] = name.replace('encoder.patch_embed.norm' , 'embeddings.norm' )
if "attn.proj" in name:
__a : List[Any] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
__a : Union[str, Any] = name.replace('attn' , 'attention.self' )
if "norm1" in name:
__a : Any = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__a : Any = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__a : Dict = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__a : Dict = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
__a : List[str] = 'layernorm.weight'
if name == "encoder.norm.bias":
__a : Optional[Any] = 'layernorm.bias'
if "decoder" in name:
pass
else:
__a : Optional[Any] = 'swin.' + name
return name
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] ):
for key in orig_state_dict.copy().keys():
__a : int = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "attn_mask" in key:
pass
elif "qkv" in key:
__a : List[str] = key.split('.' )
__a : str = int(key_split[2] )
__a : List[str] = int(key_split[4] )
__a : Optional[int] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__a : List[Any] = val[:dim, :]
__a : Optional[int] = val[
dim : dim * 2, :
]
__a : Dict = val[-dim:, :]
else:
__a : int = val[
:dim
]
__a : Optional[Any] = val[
dim : dim * 2
]
__a : List[Any] = val[
-dim:
]
else:
__a : Any = val
return orig_state_dict
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] ):
__a : int = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )['model']
__a : Any = get_swin_config(_SCREAMING_SNAKE_CASE )
__a : List[str] = SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE )
model.eval()
__a : List[Any] = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__a : Dict = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__a : Any = ViTImageProcessor(size={'height': 192, 'width': 192} )
__a : Optional[int] = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__a : List[str] = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
with torch.no_grad():
__a : Optional[Any] = model(**_SCREAMING_SNAKE_CASE ).logits
print(outputs.keys() )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print(F"""Pushing model and image processor for {model_name} to hub""" )
model.push_to_hub(F"""microsoft/{model_name}""" )
image_processor.push_to_hub(F"""microsoft/{model_name}""" )
if __name__ == "__main__":
__lowercase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='swin-base-simmim-window6-192',
type=str,
choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
help='Name of the Swin SimMIM model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__lowercase : Tuple = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 360
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = 'laion/clap-htsat-unfused'
__a : Optional[Any] = tempfile.mkdtemp()
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return RobertaTokenizer.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self , **__a ):
'''simple docstring'''
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.get_tokenizer()
__a : List[str] = self.get_feature_extractor()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
processor.save_pretrained(self.tmpdirname )
__a : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__a : int = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__a : List[str] = self.get_feature_extractor(do_normalize=__a , padding_value=1.0 )
__a : Tuple = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : int = self.get_tokenizer()
__a : str = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : int = floats_list((3, 1000) )
__a : str = feature_extractor(__a , return_tensors='np' )
__a : int = processor(audios=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_feature_extractor()
__a : Any = self.get_tokenizer()
__a : Any = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Union[str, Any] = 'This is a test string'
__a : Union[str, Any] = processor(text=__a )
__a : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.get_feature_extractor()
__a : str = self.get_tokenizer()
__a : List[str] = ClapProcessor(tokenizer=__a , feature_extractor=__a )
__a : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a : Optional[int] = processor.batch_decode(__a )
__a : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_feature_extractor()
__a : Optional[int] = self.get_tokenizer()
__a : int = ClapProcessor(tokenizer=__a , feature_extractor=__a )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Optional[int] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'GraphormerForGraphClassification',
'GraphormerModel',
'GraphormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__lowercase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 361
|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
'''simple docstring'''
__a : int = parent
__a : Union[str, Any] = batch_size
__a : Optional[int] = seq_length
__a : List[str] = is_training
__a : Any = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : Any = use_labels
__a : List[str] = vocab_size
__a : str = hidden_size
__a : List[str] = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Tuple = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Optional[int] = max_position_embeddings
__a : Dict = type_vocab_size
__a : Any = type_sequence_label_size
__a : Dict = initializer_range
__a : Optional[Any] = num_labels
__a : Optional[Any] = num_choices
__a : Union[str, Any] = relative_attention
__a : List[str] = position_biased_input
__a : List[Any] = pos_att_type
__a : Tuple = scope
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[Any] = None
if self.use_input_mask:
__a : Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__a : Any = None
if self.use_token_type_ids:
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Optional[int] = None
__a : int = None
__a : Dict = None
if self.use_labels:
__a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCAmelCase ( self ):
'''simple docstring'''
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Dict = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__a : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__a : str = model(__a , token_type_ids=__a )[0]
__a : Optional[int] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : int = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__a : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[Any] = self.num_labels
__a : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__a : Any = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__a : str = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : List[str] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__a : str = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a ):
'''simple docstring'''
__a : Optional[int] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__a : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Dict = config_and_inputs
__a : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = DebertaVaModelTester(self )
__a : List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : str = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__a : Optional[Any] = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
__a : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : int = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__a : str = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
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|
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : List[str] = 1
for digit in s:
product *= int(_SCREAMING_SNAKE_CASE )
return product
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = N ):
__a : Optional[int] = -sys.maxsize - 1
__a : Optional[Any] = n[:13]
__a : int = 13
while cur_index < len(_SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__a : List[Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__a : Dict = max(_SCREAMING_SNAKE_CASE , str_eval(_SCREAMING_SNAKE_CASE ) )
__a : Optional[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 362
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294
| 0
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : Tuple = {
'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
for attribute in key.split('.' ):
__a : Optional[int] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
__a : Any = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
__a : int = 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":
__a : Union[str, Any] = value
elif weight_type == "weight_g":
__a : str = value
elif weight_type == "weight_v":
__a : Any = value
elif weight_type == "bias":
__a : int = value
else:
__a : Optional[int] = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : int ):
__a : Dict = []
__a : Any = fairseq_model.state_dict()
__a : Union[str, Any] = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__a : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
__a : List[str] = True
else:
for key, mapped_key in MAPPING.items():
__a : Optional[Any] = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__a : Dict = True
if "*" in mapped_key:
__a : Tuple = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
__a : Optional[int] = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
__a : Union[str, Any] = 'weight_g'
elif "weight_v" in name:
__a : Dict = 'weight_v'
elif "weight" in name:
__a : Union[str, Any] = 'weight'
elif "bias" in name:
__a : Optional[int] = 'bias'
else:
__a : int = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
__a : Union[str, Any] = full_name.split('conv_layers.' )[-1]
__a : Dict = name.split('.' )
__a : Optional[Any] = int(items[0] )
__a : Optional[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."""
)
__a : List[str] = 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."""
)
__a : Optional[int] = 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."
)
__a : Dict = 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."""
)
__a : Optional[Any] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int ):
__a : Optional[Any] = SEWConfig()
if is_finetuned:
__a : Optional[int] = model.wav_encoder.wav_model.cfg
else:
__a : List[str] = model.cfg
__a : Any = fs_config.conv_bias
__a : Optional[Any] = eval(fs_config.conv_feature_layers )
__a : int = [x[0] for x in conv_layers]
__a : str = [x[1] for x in conv_layers]
__a : Optional[Any] = [x[2] for x in conv_layers]
__a : Optional[int] = 'gelu'
__a : List[Any] = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
__a : Optional[Any] = 0.0
__a : List[Any] = fs_config.activation_fn.name
__a : Any = fs_config.encoder_embed_dim
__a : Tuple = 0.0_2
__a : Optional[Any] = fs_config.encoder_ffn_embed_dim
__a : Tuple = 1e-5
__a : int = fs_config.encoder_layerdrop
__a : List[str] = fs_config.encoder_attention_heads
__a : Optional[int] = fs_config.conv_pos_groups
__a : Optional[Any] = fs_config.conv_pos
__a : List[Any] = len(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = fs_config.encoder_layers
__a : Optional[Any] = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
__a : Tuple = model.cfg
__a : List[Any] = fs_config.final_dropout
__a : Any = fs_config.layerdrop
__a : List[str] = fs_config.activation_dropout
__a : List[str] = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
__a : Any = fs_config.attention_dropout
__a : Any = fs_config.dropout_input
__a : Tuple = fs_config.dropout
__a : List[Any] = fs_config.mask_channel_length
__a : Union[str, Any] = fs_config.mask_channel_prob
__a : List[Any] = fs_config.mask_length
__a : Dict = fs_config.mask_prob
__a : str = 'Wav2Vec2FeatureExtractor'
__a : Any = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : int=None , _SCREAMING_SNAKE_CASE : str=True ):
if is_finetuned:
__a : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
__a : str = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
__a : Dict = SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
__a : List[str] = convert_config(model[0] , _SCREAMING_SNAKE_CASE )
__a : int = model[0].eval()
__a : List[str] = True if config.feat_extract_norm == 'layer' else False
__a : Tuple = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
if is_finetuned:
if dict_path:
__a : Any = Dictionary.load(_SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__a : List[str] = target_dict.pad_index
__a : Optional[Any] = target_dict.bos_index
__a : Tuple = target_dict.pad_index
__a : Dict = target_dict.bos_index
__a : str = target_dict.eos_index
__a : List[Any] = len(target_dict.symbols )
__a : Tuple = os.path.join(_SCREAMING_SNAKE_CASE , 'vocab.json' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE ) )
return
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices , _SCREAMING_SNAKE_CASE )
__a : Optional[int] = WavaVecaCTCTokenizer(
_SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=_SCREAMING_SNAKE_CASE , )
__a : Optional[int] = WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
__a : Any = SEWForCTC(_SCREAMING_SNAKE_CASE )
else:
__a : str = SEWModel(_SCREAMING_SNAKE_CASE )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__lowercase : str = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 363
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
__lowercase : Optional[int] = None
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
__lowercase : List[Any] = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
},
'tokenizer_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
},
}
__lowercase : List[Any] = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
__lowercase : str = '▁'
# Segments (not really needed)
__lowercase : Dict = 0
__lowercase : Optional[int] = 1
__lowercase : Any = 2
__lowercase : List[str] = 3
__lowercase : int = 4
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = "left"
A_ = XLNetTokenizer
def __init__( self , __a=None , __a=None , __a=False , __a=True , __a=False , __a="<s>" , __a="</s>" , __a="<unk>" , __a="<sep>" , __a="<pad>" , __a="<cls>" , __a="<mask>" , __a=["<eop>", "<eod>"] , **__a , ):
'''simple docstring'''
__a : str = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else mask_token
super().__init__(
vocab_file=__a , tokenizer_file=__a , do_lower_case=__a , remove_space=__a , keep_accents=__a , bos_token=__a , eos_token=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , additional_special_tokens=__a , **__a , )
__a : Any = 3
__a : Optional[int] = do_lower_case
__a : Optional[int] = remove_space
__a : Optional[Any] = keep_accents
__a : Union[str, Any] = vocab_file
__a : Tuple = False if not self.vocab_file else True
def __UpperCAmelCase ( self , __a , __a = None ):
'''simple docstring'''
__a : Dict = [self.sep_token_id]
__a : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __UpperCAmelCase ( self , __a , __a = None ):
'''simple docstring'''
__a : List[str] = [self.sep_token_id]
__a : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __UpperCAmelCase ( self , __a , __a = 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(__a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a : Union[str, Any] = os.path.join(
__a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ):
copyfile(self.vocab_file , __a )
return (out_vocab_file,)
| 364
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
A_ = 42
A_ = None
A_ = None
def lowerCamelCase (_SCREAMING_SNAKE_CASE : TreeNode | None ):
# Validation
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
__a : Optional[int] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 365
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294
| 0
|
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "sew-d"
def __init__( self , __a=32 , __a=768 , __a=12 , __a=12 , __a=3072 , __a=2 , __a=512 , __a=256 , __a=True , __a=True , __a=("p2c", "c2p") , __a="layer_norm" , __a="gelu_python" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.02 , __a=1E-7 , __a=1E-5 , __a="group" , __a="gelu" , __a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __a=False , __a=128 , __a=16 , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=0 , __a="mean" , __a=False , __a=False , __a=256 , __a=0 , __a=1 , __a=2 , **__a , ):
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a )
__a : Any = hidden_size
__a : List[str] = feat_extract_norm
__a : List[Any] = feat_extract_activation
__a : Tuple = list(__a )
__a : Union[str, Any] = list(__a )
__a : List[Any] = list(__a )
__a : Dict = conv_bias
__a : str = num_conv_pos_embeddings
__a : Optional[Any] = num_conv_pos_embedding_groups
__a : List[str] = len(self.conv_dim )
__a : str = num_hidden_layers
__a : Any = intermediate_size
__a : int = squeeze_factor
__a : str = max_position_embeddings
__a : int = position_buckets
__a : Any = share_att_key
__a : Dict = relative_attention
__a : List[Any] = norm_rel_ebd
__a : str = list(__a )
__a : Any = hidden_act
__a : List[Any] = num_attention_heads
__a : Dict = hidden_dropout
__a : Optional[Any] = attention_dropout
__a : List[str] = activation_dropout
__a : Any = feat_proj_dropout
__a : Dict = final_dropout
__a : int = layer_norm_eps
__a : Optional[Any] = feature_layer_norm_eps
__a : Optional[int] = initializer_range
__a : Optional[Any] = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__a : List[Any] = apply_spec_augment
__a : int = mask_time_prob
__a : int = mask_time_length
__a : str = mask_time_min_masks
__a : List[str] = mask_feature_prob
__a : Optional[int] = mask_feature_length
__a : Optional[int] = mask_feature_min_masks
# ctc loss
__a : Optional[Any] = ctc_loss_reduction
__a : int = ctc_zero_infinity
# sequence classification
__a : int = use_weighted_layer_sum
__a : Any = classifier_proj_size
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 366
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
re.sub('<n>' , '' , _SCREAMING_SNAKE_CASE ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )
| 294
| 0
|
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
while b:
__a : Tuple = b, a % b
return a
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
return a if b == 0 else euclidean_gcd_recursive(_SCREAMING_SNAKE_CASE , a % b )
def lowerCamelCase ():
print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 367
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__lowercase : Any = [0, 25, 50]
__lowercase : int = [25, 50, 75]
__lowercase : List[str] = fuzz.membership.trimf(X, abca)
__lowercase : Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowercase : List[Any] = np.ones(75)
__lowercase : Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowercase : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowercase : str = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowercase : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowercase : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowercase : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowercase : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowercase : Union[str, Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
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