code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[str] , __A: Tuple , __A: List[str]=13 , __A: Union[str, Any]=7 , __A: Union[str, Any]=6 , __A: Dict=17 , __A: List[str]=23 , __A: Any=11 , __A: str=True , ) -> Any:
_A = parent
_A = batch_size
_A = seq_length
_A = act_dim
_A = state_dim
_A = hidden_size
_A = max_length
_A = is_training
def __A ( self: str ) -> Optional[Any]:
_A = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_A = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_A = floats_tensor((self.batch_size, self.seq_length, 1) )
_A = floats_tensor((self.batch_size, self.seq_length, 1) )
_A = ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
_A = random_attention_mask((self.batch_size, self.seq_length) )
_A = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def __A ( self: Union[str, Any] ) -> Optional[int]:
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def __A ( self: List[Any] , __A: Tuple , __A: int , __A: Any , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[str] , __A: Any , ) -> Any:
_A = DecisionTransformerModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A , __A , __A , __A , __A , __A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def __A ( self: str ) -> Tuple:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (DecisionTransformerModel,) if is_torch_available() else ()
A_ = ()
A_ = {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
A_ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: Any ) -> Optional[int]:
_A = DecisionTransformerModelTester(self )
_A = ConfigTester(self , config_class=__A , hidden_size=37 )
def __A ( self: str ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
@slow
def __A ( self: Optional[int] ) -> str:
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = DecisionTransformerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: List[str] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(__A )] , __A )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self: str ) -> Any:
_A = 2 # number of steps of autoregressive prediction we will perform
_A = 10 # defined by the RL environment, may be normalized
_A = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_A = model.to(__A )
_A = model.config
torch.manual_seed(0 )
_A = torch.randn(1 , 1 , config.state_dim ).to(device=__A , dtype=torch.floataa ) # env.reset()
_A = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] , device=__A )
_A = torch.tensor(__A , device=__A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_A = state
_A = torch.zeros(1 , 0 , config.act_dim , device=__A , dtype=torch.floataa )
_A = torch.zeros(1 , 0 , device=__A , dtype=torch.floataa )
_A = torch.tensor(0 , device=__A , dtype=torch.long ).reshape(1 , 1 )
for step in range(__A ):
_A = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=__A )] , dim=1 )
_A = torch.cat([rewards, torch.zeros(1 , 1 , device=__A )] , dim=1 )
_A = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_A ,_A ,_A = model(
states=__A , actions=__A , rewards=__A , returns_to_go=__A , timesteps=__A , attention_mask=__A , return_dict=__A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
_A ,_A ,_A ,_A = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=__A , dtype=torch.floataa ),
1.0,
False,
{},
)
_A = action_pred[0, -1]
_A = torch.cat([states, state] , dim=1 )
_A = returns_to_go[0, -1] - reward
_A = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_A = torch.cat(
[timesteps, torch.ones((1, 1) , device=__A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[Any] ) -> Union[str, Any]:
_A = {}
def __A ( self: str ) -> None:
print(self.vertex )
for i in self.vertex:
print(__A , ''' -> ''' , ''' -> '''.join([str(__A ) for j in self.vertex[i]] ) )
def __A ( self: Union[str, Any] , __A: int , __A: int ) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__A )
else:
# else make a new vertex
_A = [to_vertex]
def __A ( self: Any ) -> None:
# visited array for storing already visited nodes
_A = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__A , __A )
def __A ( self: List[Any] , __A: int , __A: list ) -> None:
# mark start vertex as visited
_A = True
print(__A , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__A , __A )
if __name__ == "__main__":
__A = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'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:
__A = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
__A = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
__A = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
__A = [
'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
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
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, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: str ) -> Any:
_A = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A , '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A , '''depth_multiplier''' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: Tuple , __A: Optional[int]=13 , __A: Optional[Any]=3 , __A: Optional[Any]=32 , __A: Optional[Any]=0.25 , __A: Optional[Any]=8 , __A: Dict=8 , __A: str=6 , __A: Union[str, Any]=32 , __A: Any=True , __A: int=True , __A: List[Any]=True , __A: Union[str, Any]="relu6" , __A: Dict=12_80 , __A: Dict=0.1 , __A: List[str]=0.02 , __A: List[str]=True , __A: List[Any]=True , __A: Optional[Any]=10 , __A: Union[str, Any]=None , ) -> Any:
_A = parent
_A = batch_size
_A = num_channels
_A = image_size
_A = depth_multiplier
_A = depth_divisible_by
_A = min_depth
_A = expand_ratio
_A = tf_padding
_A = output_stride
_A = first_layer_is_expansion
_A = finegrained_output
_A = hidden_act
_A = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
_A = classifier_dropout_prob
_A = use_labels
_A = is_training
_A = num_labels
_A = initializer_range
_A = scope
def __A ( self: str ) -> str:
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.num_labels )
_A = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_A = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self: str ) -> Optional[Any]:
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __A ( self: List[str] , __A: Union[str, Any] , __A: List[Any] , __A: int , __A: Optional[int] ) -> Optional[Any]:
_A = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Optional[int] , __A: Union[str, Any] , __A: List[str] ) -> Any:
_A = self.num_labels
_A = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
_A = model(__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self: Optional[int] , __A: Tuple , __A: str , __A: Dict , __A: List[Any] ) -> Optional[Any]:
_A = self.num_labels
_A = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
_A = model(__A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
_A = model(__A , labels=__A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __A ( self: str ) -> Optional[Any]:
_A = self.prepare_config_and_inputs()
_A ,_A ,_A ,_A = config_and_inputs
_A = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": MobileNetVaModel,
"image-classification": MobileNetVaForImageClassification,
"image-segmentation": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: Any ) -> Union[str, Any]:
_A = MobileNetVaModelTester(self )
_A = MobileNetVaConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: Dict ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __A ( self: Union[str, Any] ) -> str:
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __A ( self: List[str] ) -> int:
pass
def __A ( self: Any ) -> Union[str, Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
def __A ( self: int ) -> str:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __A ( self: List[Any] ) -> Optional[Any]:
def check_hidden_states_output(__A: Tuple , __A: List[str] , __A: Optional[Any] ):
_A = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_A = model(**self._prepare_for_class(__A , __A ) )
_A = outputs.hidden_states
_A = 16
self.assertEqual(len(__A ) , __A )
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = True
check_hidden_states_output(__A , __A , __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A = True
check_hidden_states_output(__A , __A , __A )
def __A ( self: Any ) -> Dict:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __A ( self: Union[str, Any] ) -> Optional[int]:
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: int ) -> Any:
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __A ( self: Tuple ) -> Union[str, Any]:
_A = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=__A , return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
_A = model(**__A )
# verify the logits
_A = torch.Size((1, 10_01) )
self.assertEqual(outputs.logits.shape , __A )
_A = torch.tensor([0.2_445, -1.1_993, 0.1_905] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __A , atol=1e-4 ) )
@slow
def __A ( self: Union[str, Any] ) -> Tuple:
_A = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
_A = model.to(__A )
_A = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
_A = prepare_img()
_A = image_processor(images=__A , return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
_A = model(**__A )
_A = outputs.logits
# verify the logits
_A = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , __A )
_A = torch.tensor(
[
[[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
[[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
[[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
] , device=__A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __A , atol=1e-4 ) )
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__A = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'output.dense',
'attention.self_mask': 'attn_mask',
'mlp.fc1': 'intermediate.dense',
'mlp.fc2': 'output.dense',
'norm1': 'layernorm_before',
'norm2': 'layernorm_after',
'bn0': 'batch_norm',
}
__A = AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')
def __A ( _lowercase , _lowercase=False ):
'''simple docstring'''
_A ,_A = create_model(
'''HTSAT-tiny''' , '''roberta''' , _lowercase , precision='''fp32''' , device='''cuda:0''' if torch.cuda.is_available() else '''cpu''' , enable_fusion=_lowercase , fusion_type='''aff_2d''' if enable_fusion else None , )
return model, model_cfg
def __A ( _lowercase ):
'''simple docstring'''
_A = {}
_A = R'''.*sequential.(\d+).*'''
_A = R'''.*_projection.(\d+).*'''
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
_A = key.replace(_lowercase , _lowercase )
if re.match(_lowercase , _lowercase ):
# replace sequential layers with list
_A = re.match(_lowercase , _lowercase ).group(1 )
_A = key.replace(f"""sequential.{sequential_layer}.""" , f"""layers.{int(_lowercase )//3}.linear.""" )
elif re.match(_lowercase , _lowercase ):
_A = int(re.match(_lowercase , _lowercase ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
_A = 1 if projecton_layer == 0 else 2
_A = key.replace(f"""_projection.{projecton_layer}.""" , f"""_projection.linear{transformers_projection_layer}.""" )
if "audio" and "qkv" in key:
# split qkv into query key and value
_A = value
_A = mixed_qkv.size(0 ) // 3
_A = mixed_qkv[:qkv_dim]
_A = mixed_qkv[qkv_dim : qkv_dim * 2]
_A = mixed_qkv[qkv_dim * 2 :]
_A = query_layer
_A = key_layer
_A = value_layer
else:
_A = value
return model_state_dict
def __A ( _lowercase , _lowercase , _lowercase , _lowercase=False ):
'''simple docstring'''
_A ,_A = init_clap(_lowercase , enable_fusion=_lowercase )
clap_model.eval()
_A = clap_model.state_dict()
_A = rename_state_dict(_lowercase )
_A = ClapConfig()
_A = enable_fusion
_A = ClapModel(_lowercase )
# ignore the spectrogram embedding layer
model.load_state_dict(_lowercase , strict=_lowercase )
model.save_pretrained(_lowercase )
transformers_config.save_pretrained(_lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
__A = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["torch", "torchsde"]
def __init__( self: str , *__A: str , **__A: Optional[int] ) -> List[Any]:
requires_backends(self , ['''torch''', '''torchsde'''] )
@classmethod
def __A ( cls: int , *__A: List[Any] , **__A: str ) -> Any:
requires_backends(cls , ['''torch''', '''torchsde'''] )
@classmethod
def __A ( cls: Dict , *__A: List[str] , **__A: Dict ) -> Dict:
requires_backends(cls , ['''torch''', '''torchsde'''] )
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = CLIPConfig
A_ = ["CLIPEncoderLayer"]
def __init__( self: Optional[Any] , __A: CLIPConfig ) -> Any:
super().__init__(__A )
_A = CLIPVisionModelWithProjection(config.vision_config )
_A = nn.Linear(config.vision_config.projection_dim , 1 )
_A = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def __A ( self: str , __A: Any , __A: Optional[int] , __A: Tuple=0.5 , __A: List[Any]=0.5 ) -> int:
_A = self.vision_model(__A )[0]
_A = self.p_head(__A )
_A = nsfw_detected.flatten()
_A = nsfw_detected > p_threshold
_A = nsfw_detected.tolist()
if any(__A ):
logger.warning(
'''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, nsfw_detected_ in enumerate(__A ):
if nsfw_detected_:
_A = np.zeros(images[idx].shape )
_A = self.w_head(__A )
_A = watermark_detected.flatten()
_A = watermark_detected > w_threshold
_A = watermark_detected.tolist()
if any(__A ):
logger.warning(
'''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, watermark_detected_ in enumerate(__A ):
if watermark_detected_:
_A = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "new-model"
if is_tf_available():
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = NewModelConfig
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self: Optional[Any] ) -> Union[str, Any]:
_A = '''bert-base-cased'''
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModel.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: Optional[int] ) -> Any:
_A = '''bert-base-cased'''
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForPreTraining.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: Dict ) -> List[str]:
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForCausalLM.from_pretrained(__A )
_A ,_A = TFAutoModelForCausalLM.from_pretrained(__A , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: Tuple ) -> str:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelWithLMHead.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: int ) -> int:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForMaskedLM.from_pretrained(__A )
_A ,_A = TFAutoModelForMaskedLM.from_pretrained(__A , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: str ) -> Optional[Any]:
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForSeqaSeqLM.from_pretrained(__A )
_A ,_A = TFAutoModelForSeqaSeqLM.from_pretrained(__A , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: Union[str, Any] ) -> List[Any]:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForSequenceClassification.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
def __A ( self: Optional[int] ) -> List[Any]:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForQuestionAnswering.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
@slow
@require_tensorflow_probability
def __A ( self: Any ) -> str:
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_A = AutoConfig.from_pretrained(__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
_A = TFAutoModelForTableQuestionAnswering.from_pretrained(__A )
_A ,_A = TFAutoModelForTableQuestionAnswering.from_pretrained(
__A , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertIsInstance(__A , __A )
def __A ( self: List[str] ) -> Dict:
_A = TFAutoModelWithLMHead.from_pretrained(__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_44_10 )
def __A ( self: Union[str, Any] ) -> Optional[int]:
_A = TFAutoModelWithLMHead.from_pretrained(__A )
self.assertIsInstance(__A , __A )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__A ) , 1_44_10 )
def __A ( self: Optional[Any] ) -> Optional[Any]:
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_A = TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny''' )
self.assertIsInstance(__A , __A )
_A = copy.deepcopy(model.config )
_A = ['''FunnelBaseModel''']
_A = TFAutoModel.from_config(__A )
self.assertIsInstance(__A , __A )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__A )
_A = TFAutoModel.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __A ( self: Optional[Any] ) -> int:
try:
AutoConfig.register('''new-model''' , __A )
_A = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(__A ):
auto_class.register(__A , __A )
auto_class.register(__A , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
auto_class.register(__A , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
_A = BertModelTester(self ).get_config()
_A = NewModelConfig(**tiny_config.to_dict() )
_A = auto_class.from_config(__A )
self.assertIsInstance(__A , __A )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__A )
_A = auto_class.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def __A ( self: Optional[Any] ) -> int:
with self.assertRaisesRegex(
__A , '''bert-base is not a local folder and is not a valid model identifier''' ):
_A = TFAutoModel.from_pretrained('''bert-base''' )
def __A ( self: List[str] ) -> List[Any]:
with self.assertRaisesRegex(
__A , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_A = TFAutoModel.from_pretrained(__A , revision='''aaaaaa''' )
def __A ( self: Tuple ) -> Optional[int]:
with self.assertRaisesRegex(
__A , '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin''' , ):
_A = TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def __A ( self: int ) -> List[Any]:
with self.assertRaisesRegex(__A , '''Use `from_pt=True` to load this model''' ):
_A = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
def __A ( self: List[str] ) -> List[str]:
# Make sure we have cached the model.
_A = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
_A = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_A = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
with RequestCounter() as counter:
_A = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "informer"
A_ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self: Union[str, Any] , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "student_t" , __A: str = "nll" , __A: int = 1 , __A: List[int] = None , __A: Optional[Union[str, bool]] = "mean" , __A: int = 0 , __A: int = 0 , __A: int = 0 , __A: int = 0 , __A: Optional[List[int]] = None , __A: Optional[List[int]] = None , __A: int = 64 , __A: int = 32 , __A: int = 32 , __A: int = 2 , __A: int = 2 , __A: int = 2 , __A: int = 2 , __A: bool = True , __A: str = "gelu" , __A: float = 0.05 , __A: float = 0.1 , __A: float = 0.1 , __A: float = 0.1 , __A: float = 0.1 , __A: int = 1_00 , __A: float = 0.02 , __A: List[str]=True , __A: str = "prob" , __A: int = 5 , __A: bool = True , **__A: Optional[Any] , ) -> List[Any]:
# time series specific configuration
_A = prediction_length
_A = context_length or prediction_length
_A = distribution_output
_A = loss
_A = input_size
_A = num_time_features
_A = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
_A = scaling
_A = num_dynamic_real_features
_A = num_static_real_features
_A = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(__A ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
_A = cardinality
else:
_A = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(__A ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
_A = embedding_dimension
else:
_A = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_A = num_parallel_samples
# Transformer architecture configuration
_A = input_size * len(self.lags_sequence ) + self._number_of_features
_A = d_model
_A = encoder_attention_heads
_A = decoder_attention_heads
_A = encoder_ffn_dim
_A = decoder_ffn_dim
_A = encoder_layers
_A = decoder_layers
_A = dropout
_A = attention_dropout
_A = activation_dropout
_A = encoder_layerdrop
_A = decoder_layerdrop
_A = activation_function
_A = init_std
_A = use_cache
# Informer
_A = attention_type
_A = sampling_factor
_A = distil
super().__init__(is_encoder_decoder=__A , **__A )
@property
def __A ( self: Optional[int] ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__A = logging.get_logger(__name__)
__A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
# See all LED models at https://huggingface.co/models?filter=LED
__A = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
__A = {
'allenai/led-base-16384': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def __A ( ):
'''simple docstring'''
_A = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_A = bs[:]
_A = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowercase )
cs.append(2**8 + n )
n += 1
_A = [chr(_lowercase ) for n in cs]
return dict(zip(_lowercase , _lowercase ) )
def __A ( _lowercase ):
'''simple docstring'''
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ["input_ids", "attention_mask"]
def __init__( self: Union[str, Any] , __A: List[str] , __A: str , __A: Any="replace" , __A: str="<s>" , __A: Optional[int]="</s>" , __A: Optional[Any]="</s>" , __A: Dict="<s>" , __A: Any="<unk>" , __A: int="<pad>" , __A: int="<mask>" , __A: Union[str, Any]=False , **__A: Optional[Any] , ) -> int:
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else bos_token
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else eos_token
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else sep_token
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else cls_token
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else unk_token
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
super().__init__(
errors=__A , bos_token=__A , eos_token=__A , unk_token=__A , sep_token=__A , cls_token=__A , pad_token=__A , mask_token=__A , add_prefix_space=__A , **__A , )
with open(__A , encoding='''utf-8''' ) as vocab_handle:
_A = json.load(__A )
_A = {v: k for k, v in self.encoder.items()}
_A = errors # how to handle errors in decoding
_A = bytes_to_unicode()
_A = {v: k for k, v in self.byte_encoder.items()}
with open(__A , encoding='''utf-8''' ) as merges_handle:
_A = merges_handle.read().split('''\n''' )[1:-1]
_A = [tuple(merge.split() ) for merge in bpe_merges]
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = {}
_A = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_A = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def __A ( self: Union[str, Any] ) -> str:
return len(self.encoder )
def __A ( self: List[Any] ) -> Tuple:
return dict(self.encoder , **self.added_tokens_encoder )
def __A ( self: Union[str, Any] , __A: Union[str, Any] ) -> Tuple:
if token in self.cache:
return self.cache[token]
_A = tuple(__A )
_A = get_pairs(__A )
if not pairs:
return token
while True:
_A = min(__A , key=lambda __A : self.bpe_ranks.get(__A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_A ,_A = bigram
_A = []
_A = 0
while i < len(__A ):
try:
_A = word.index(__A , __A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__A )
_A = new_word
if len(__A ) == 1:
break
else:
_A = get_pairs(__A )
_A = ''' '''.join(__A )
_A = word
return word
def __A ( self: Any , __A: int ) -> Dict:
_A = []
for token in re.findall(self.pat , __A ):
_A = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A ).split(''' ''' ) )
return bpe_tokens
def __A ( self: List[str] , __A: str ) -> List[str]:
return self.encoder.get(__A , self.encoder.get(self.unk_token ) )
def __A ( self: Optional[Any] , __A: List[str] ) -> List[Any]:
return self.decoder.get(__A )
def __A ( self: Union[str, Any] , __A: str ) -> Optional[int]:
_A = ''''''.join(__A )
_A = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __A ( self: Union[str, Any] , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_A = os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__A , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__A , ensure_ascii=__A ) + '''\n''' )
_A = 0
with open(__A , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __A : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_A = token_index
writer.write(''' '''.join(__A ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __A ( self: List[Any] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __A ( self: str , __A: List[int] , __A: Optional[List[int]] = None , __A: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
if token_ids_a is None:
return [1] + ([0] * len(__A )) + [1]
return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]
def __A ( self: Optional[Any] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __A ( self: Dict , __A: List[Any] , __A: Any=False , **__A: Optional[Any] ) -> Optional[int]:
_A = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
_A = ''' ''' + text
return (text, kwargs)
def __A ( self: List[Any] , __A: Union[Dict[str, EncodedInput], BatchEncoding] , __A: Optional[int] = None , __A: PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __A: Optional[int] = None , __A: Optional[bool] = None , ) -> dict:
_A = super()._pad(
encoded_inputs=__A , max_length=__A , padding_strategy=__A , pad_to_multiple_of=__A , return_attention_mask=__A , )
# Load from model defaults
if return_attention_mask is None:
_A = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_A = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_A = len(encoded_inputs['''global_attention_mask'''] ) != len(__A )
if needs_to_be_padded:
_A = len(__A ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_A = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
_A = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = IFPipeline
A_ = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
A_ = TEXT_TO_IMAGE_BATCH_PARAMS
A_ = PipelineTesterMixin.required_optional_params - {"latents"}
def __A ( self: List[str] ) -> str:
return self._get_dummy_components()
def __A ( self: Tuple , __A: Any , __A: Tuple=0 ) -> Union[str, Any]:
if str(__A ).startswith('''mps''' ):
_A = torch.manual_seed(__A )
else:
_A = torch.Generator(device=__A ).manual_seed(__A )
_A = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def __A ( self: Any ) -> int:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def __A ( self: str ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __A ( self: int ) -> Optional[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __A ( self: int ) -> Dict:
self._test_save_load_local()
def __A ( self: str ) -> List[Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __A ( self: int ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Union[str, Any] ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Any ) -> Union[str, Any]:
# if
_A = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa )
_A = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''' )
_A ,_A = pipe_a.encode_prompt('''anime turtle''' , device='''cuda''' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_A = None
_A = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_A = IFImgaImgPipeline(**pipe_a.components )
_A = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_A = IFInpaintingPipeline(**pipe_a.components )
_A = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def __A ( self: Union[str, Any] , __A: List[Any] , __A: List[str] , __A: Union[str, Any] , __A: List[str] ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__A , __A )
def __A ( self: Tuple , __A: Optional[Any] , __A: List[Any] , __A: int , __A: Union[str, Any] ) -> List[Any]:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(__A )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__A , __A )
def __A ( self: Tuple , __A: Any , __A: Union[str, Any] , __A: Dict , __A: Any ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(__A )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(__A )
_A = pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__A , __A )
def __A ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
A_ = MODEL_FOR_CAUSAL_LM_MAPPING
A_ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def __A ( self: List[Any] ) -> List[str]:
_A = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_A = text_generator('''This is a test''' , do_sample=__A )
self.assertEqual(
__A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_A = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
__A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_A = text_generator('''This is a test''' , do_sample=__A , num_return_sequences=2 , return_tensors=__A )
self.assertEqual(
__A , [
{'''generated_token_ids''': ANY(__A )},
{'''generated_token_ids''': ANY(__A )},
] , )
_A = text_generator.model.config.eos_token_id
_A = '''<pad>'''
_A = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=__A , num_return_sequences=2 , batch_size=2 , return_tensors=__A , )
self.assertEqual(
__A , [
[
{'''generated_token_ids''': ANY(__A )},
{'''generated_token_ids''': ANY(__A )},
],
[
{'''generated_token_ids''': ANY(__A )},
{'''generated_token_ids''': ANY(__A )},
],
] , )
@require_tf
def __A ( self: int ) -> List[Any]:
_A = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_A = text_generator('''This is a test''' , do_sample=__A )
self.assertEqual(
__A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_A = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=__A )
self.assertEqual(
__A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def __A ( self: Dict , __A: Tuple , __A: Union[str, Any] , __A: List[Any] ) -> Any:
_A = TextGenerationPipeline(model=__A , tokenizer=__A )
return text_generator, ["This is a test", "Another test"]
def __A ( self: Any ) -> Any:
_A = '''Hello I believe in'''
_A = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_A = text_generator(__A )
self.assertEqual(
__A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_A = text_generator(__A , stop_sequence=''' fe''' )
self.assertEqual(__A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def __A ( self: int , __A: List[str] , __A: List[str] ) -> str:
_A = text_generator.model
_A = text_generator.tokenizer
_A = text_generator('''This is a test''' )
self.assertEqual(__A , [{'''generated_text''': ANY(__A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_A = text_generator('''This is a test''' , return_full_text=__A )
self.assertEqual(__A , [{'''generated_text''': ANY(__A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_A = pipeline(task='''text-generation''' , model=__A , tokenizer=__A , return_full_text=__A )
_A = text_generator('''This is a test''' )
self.assertEqual(__A , [{'''generated_text''': ANY(__A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_A = text_generator('''This is a test''' , return_full_text=__A )
self.assertEqual(__A , [{'''generated_text''': ANY(__A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_A = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=__A )
self.assertEqual(
__A , [
[{'''generated_text''': ANY(__A )}, {'''generated_text''': ANY(__A )}],
[{'''generated_text''': ANY(__A )}, {'''generated_text''': ANY(__A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_A = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=__A )
self.assertEqual(
__A , [
[{'''generated_text''': ANY(__A )}, {'''generated_text''': ANY(__A )}],
[{'''generated_text''': ANY(__A )}, {'''generated_text''': ANY(__A )}],
] , )
with self.assertRaises(__A ):
_A = text_generator('''test''' , return_full_text=__A , return_text=__A )
with self.assertRaises(__A ):
_A = text_generator('''test''' , return_full_text=__A , return_tensors=__A )
with self.assertRaises(__A ):
_A = text_generator('''test''' , return_text=__A , return_tensors=__A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_A = text_generator('''''' )
self.assertEqual(__A , [{'''generated_text''': ANY(__A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_A = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_A = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_00_00
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 5_00 , max_new_tokens=20 )
_A = text_generator('''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__A ):
text_generator(
'''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def __A ( self: Union[str, Any] ) -> Union[str, Any]:
import torch
# Classic `model_kwargs`
_A = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def __A ( self: int ) -> List[str]:
import torch
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def __A ( self: Optional[int] ) -> List[Any]:
import torch
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=__A , top_p=0.5 )
def __A ( self: str ) -> int:
_A = '''Hello world'''
_A = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_A = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_A = logging.get_logger('''transformers.generation.utils''' )
_A = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(__A ) as cl:
_A = text_generator(__A , max_length=10 , max_new_tokens=1 )
self.assertIn(__A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__A ) as cl:
_A = text_generator(__A , max_new_tokens=1 )
self.assertNotIn(__A , cl.out )
with CaptureLogger(__A ) as cl:
_A = text_generator(__A , max_length=10 )
self.assertNotIn(__A , cl.out )
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
__A = logging.getLogger()
__A = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: Any , __A: Union[str, Any] ) -> Optional[Any]:
os.makedirs(__A , exist_ok=__A )
_A = {'''source''': '''What is love ?''', '''target''': '''life'''}
_A = {'''train''': 12, '''val''': 2, '''test''': 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
_A = '''\n'''.join([contents[field]] * n_lines[split] )
with open(os.path.join(__A , f"""{split}.{field}""" ) , '''w''' ) as f:
f.write(__A )
def __A ( self: Dict , __A: int , __A: str = "pytorch" ) -> Union[str, Any]:
_A = self.get_auto_remove_tmp_dir()
_A = os.path.join(__A , '''output''' )
_A = os.path.join(__A , '''data''' )
self._create_dummy_data(data_dir=__A )
_A = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append('''--fp16''' )
else:
testargs.append('''--gpus=0''' )
testargs.append('''--distributed_backend=ddp_cpu''' )
testargs.append('''--num_processes=2''' )
_A = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(__A , env=self.get_env() )
_A = os.path.join(__A , '''metrics.json''' )
with open(__A ) as f:
_A = json.load(__A )
return result
@require_torch_gpu
def __A ( self: Any ) -> Any:
_A = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_multi_gpu
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_gpu
@require_ray
def __A ( self: str ) -> Tuple:
_A = self._run_finetune(gpus=1 , distributed_retriever='''ray''' )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_multi_gpu
@require_ray
def __A ( self: List[Any] ) -> Optional[int]:
_A = self._run_finetune(gpus=1 , distributed_retriever='''ray''' )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = 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 __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = AlbertConfig.from_json_file(_lowercase )
print(f"""Building PyTorch model from configuration: {config}""" )
_A = AlbertForPreTraining(_lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_albert(_lowercase , _lowercase , _lowercase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: Optional[Any] ) -> int:
_A = tempfile.mkdtemp()
_A = 8
# DPR tok
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , DPR_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] ) )
# BART tok
_A = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_A = {'''unk_token''': '''<unk>'''}
_A = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(__A , BART_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 __A ( self: int ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def __A ( self: Union[str, Any] ) -> DPRContextEncoderTokenizer:
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def __A ( self: int ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def __A ( self: Dict ) -> str:
shutil.rmtree(self.tmpdirname )
def __A ( self: int ) -> Dict:
_A = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def __A ( self: Optional[Any] ) -> str:
_A = self.get_dummy_dataset()
_A = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
_A = dataset
_A = RagRetriever(
__A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def __A ( self: Dict , __A: bool ) -> int:
_A = self.get_dummy_dataset()
_A = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
_A = os.path.join(self.tmpdirname , '''dataset''' )
_A = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
_A = RagRetriever(
__A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
_A = RagRetriever(
__A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __A ) , )
return retriever
def __A ( self: Optional[Any] ) -> List[Any]:
_A = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
_A = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
_A = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
_A = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(__A , open(__A , '''wb''' ) )
_A = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
_A = RagRetriever(
__A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def __A ( self: Tuple ) -> Dict:
_A = 1
_A = self.get_dummy_canonical_hf_index_retriever()
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A ,_A ,_A = retriever.retrieve(__A , n_docs=__A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __A )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __A ( self: Optional[int] ) -> Dict:
_A = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
_A = self.get_dummy_dataset()
retriever.save_pretrained(__A )
_A = RagRetriever.from_pretrained(__A )
self.assertIsInstance(__A , __A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever.retrieve(__A , n_docs=1 )
self.assertTrue(out is not None )
def __A ( self: Any ) -> List[Any]:
_A = 1
_A = self.get_dummy_custom_hf_index_retriever(from_disk=__A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A ,_A ,_A = retriever.retrieve(__A , n_docs=__A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __A )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __A ( self: str ) -> Dict:
_A = self.get_dummy_custom_hf_index_retriever(from_disk=__A )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__A )
_A = RagRetriever.from_pretrained(__A )
self.assertIsInstance(__A , __A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever.retrieve(__A , n_docs=1 )
self.assertTrue(out is not None )
def __A ( self: str ) -> List[str]:
_A = 1
_A = self.get_dummy_custom_hf_index_retriever(from_disk=__A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A ,_A ,_A = retriever.retrieve(__A , n_docs=__A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , __A )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __A ( self: int ) -> Optional[Any]:
_A = self.get_dummy_custom_hf_index_retriever(from_disk=__A )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__A )
_A = RagRetriever.from_pretrained(__A )
self.assertIsInstance(__A , __A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever.retrieve(__A , n_docs=1 )
self.assertTrue(out is not None )
def __A ( self: int ) -> str:
_A = 1
_A = self.get_dummy_legacy_index_retriever()
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A ,_A ,_A = retriever.retrieve(__A , n_docs=__A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(__A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , __A )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __A ( self: int ) -> List[Any]:
_A = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(__A )
_A = RagRetriever.from_pretrained(__A )
self.assertIsInstance(__A , __A )
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever.retrieve(__A , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def __A ( self: Union[str, Any] ) -> List[Any]:
import torch
_A = 1
_A = self.get_dummy_canonical_hf_index_retriever()
_A = [[5, 7], [10, 11]]
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever(__A , __A , prefix=retriever.config.generator.prefix , n_docs=__A )
_A ,_A ,_A = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__A , __A )
self.assertIsInstance(__A , __A )
self.assertIsInstance(__A , np.ndarray )
_A = retriever(
__A , __A , prefix=retriever.config.generator.prefix , n_docs=__A , return_tensors='''pt''' , )
_A ,_A ,_A ,_A = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(__A , torch.Tensor )
self.assertIsInstance(__A , torch.Tensor )
self.assertIsInstance(__A , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def __A ( self: int ) -> str:
_A = self.get_dpr_ctx_encoder_tokenizer()
_A = 1
_A = self.get_dummy_custom_hf_index_retriever(from_disk=__A )
retriever.set_ctx_encoder_tokenizer(__A )
_A = [[5, 7], [10, 11]]
_A = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
_A = retriever(__A , __A , prefix=retriever.config.generator.prefix , n_docs=__A )
self.assertEqual(
len(__A ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , __A ) # check for doc token related keys in dictionary.
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , __A: Optional[Any] , __A: Optional[int]=7 , __A: List[Any]=3 , __A: Tuple=18 , __A: Dict=30 , __A: List[Any]=4_00 , __A: int=True , __A: str=None , __A: List[str]=True , __A: Dict=None , __A: Optional[int]=True , __A: Optional[int]=[0.5, 0.5, 0.5] , __A: str=[0.5, 0.5, 0.5] , ) -> Dict:
_A = size if size is not None else {'''shortest_edge''': 18}
_A = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
_A = parent
_A = batch_size
_A = num_channels
_A = image_size
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_center_crop
_A = crop_size
_A = do_normalize
_A = image_mean
_A = image_std
def __A ( self: int ) -> List[str]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = LevitImageProcessor if is_vision_available() else None
def __A ( self: Dict ) -> Union[str, Any]:
_A = LevitImageProcessingTester(self )
@property
def __A ( self: Optional[int] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self: Union[str, Any] ) -> Dict:
_A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , '''image_mean''' ) )
self.assertTrue(hasattr(__A , '''image_std''' ) )
self.assertTrue(hasattr(__A , '''do_normalize''' ) )
self.assertTrue(hasattr(__A , '''do_resize''' ) )
self.assertTrue(hasattr(__A , '''do_center_crop''' ) )
self.assertTrue(hasattr(__A , '''size''' ) )
def __A ( self: int ) -> Tuple:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
_A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Optional[int] ) -> Optional[Any]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = 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 = 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 = image_processing(__A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __A ( self: List[str] ) -> Tuple:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = 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 = 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 = image_processing(__A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __A ( self: List[str] ) -> str:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = 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 = 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 = 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'''],
) , )
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig',
],
'tokenization_jukebox': ['JukeboxTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
'JukeboxModel',
'JukeboxPreTrainedModel',
'JukeboxVQVAE',
'JukeboxPrior',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig',
'BlipTextConfig',
'BlipVisionConfig',
],
'processing_blip': ['BlipProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['BlipImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlipModel',
'BlipPreTrainedModel',
'BlipForConditionalGeneration',
'BlipForQuestionAnswering',
'BlipVisionModel',
'BlipTextModel',
'BlipForImageTextRetrieval',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFBlipModel',
'TFBlipPreTrainedModel',
'TFBlipForConditionalGeneration',
'TFBlipForQuestionAnswering',
'TFBlipVisionModel',
'TFBlipTextModel',
'TFBlipForImageTextRetrieval',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[str] , __A: int = 6 ) -> None:
_A = None
_A = None
self.create_linked_list(__A )
def __A ( self: Union[str, Any] , __A: int ) -> None:
_A = Node()
_A = current_node
_A = current_node
_A = current_node
for _ in range(1 , __A ):
_A = Node()
_A = current_node
_A = previous_node
_A = current_node
_A = self.front
_A = previous_node
def __A ( self: int ) -> bool:
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def __A ( self: Dict ) -> Any | None:
self.check_can_perform_operation()
return self.front.data if self.front else None
def __A ( self: Dict , __A: Any ) -> None:
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
_A = self.rear.next
if self.rear:
_A = data
def __A ( self: List[str] ) -> Any:
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
_A = self.front.data
_A = None
return data
_A = self.front
_A = old_front.next
_A = old_front.data
_A = None
return data
def __A ( self: Any ) -> None:
if self.is_empty():
raise Exception('''Empty Queue''' )
def __A ( self: List[str] ) -> None:
if self.rear and self.rear.next == self.front:
raise Exception('''Full Queue''' )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Dict ) -> None:
_A = None
_A = None
_A = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_A = JsonDatasetReader(_lowercase , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_json_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = features.copy() if features else default_expected_features
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = JsonDatasetReader(_lowercase , features=_lowercase , cache_dir=_lowercase ).read()
_check_json_dataset(_lowercase , _lowercase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_A = features.copy() if features else default_expected_features
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = JsonDatasetReader(_lowercase , features=_lowercase , cache_dir=_lowercase ).read()
assert isinstance(_lowercase , _lowercase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_A = features.copy()
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = tmp_path / '''cache'''
_A = JsonDatasetReader(_lowercase , features=_lowercase , cache_dir=_lowercase ).read()
assert isinstance(_lowercase , _lowercase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = JsonDatasetReader(_lowercase , cache_dir=_lowercase , split=_lowercase ).read()
_check_json_dataset(_lowercase , _lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if issubclass(_lowercase , _lowercase ):
_A = jsonl_path
elif issubclass(_lowercase , _lowercase ):
_A = [jsonl_path]
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = JsonDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_json_dataset(_lowercase , _lowercase )
def __A ( _lowercase , _lowercase , _lowercase=("train",) ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase )
for split in splits:
_A = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_A = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowercase , keep_in_memory=_lowercase ).read()
_check_json_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = features.copy() if features else default_expected_features
_A = (
Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_A = JsonDatasetReader({'''train''': jsonl_path} , features=_lowercase , cache_dir=_lowercase ).read()
_check_json_datasetdict(_lowercase , _lowercase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if split:
_A = {split: jsonl_path}
else:
_A = '''train'''
_A = {'''train''': jsonl_path, '''test''': jsonl_path}
_A = tmp_path / '''cache'''
_A = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_A = JsonDatasetReader(_lowercase , cache_dir=_lowercase ).read()
_check_json_datasetdict(_lowercase , _lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __A ( _lowercase ):
'''simple docstring'''
return json.load(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
return [json.loads(_lowercase ) for line in buffer]
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def __A ( self: Optional[Any] , __A: Tuple , __A: Any , __A: Optional[int] ) -> List[str]:
with io.BytesIO() as buffer:
JsonDatasetWriter(__A , __A , lines=__A ).write()
buffer.seek(0 )
_A = load_json_function(__A )
assert isinstance(__A , __A )
assert isinstance(exported_content[0] , __A )
assert len(__A ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: List[Any] , __A: Union[str, Any] , __A: Optional[int] , __A: List[Any] ) -> Union[str, Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(__A , __A , lines=__A , orient=__A ).write()
buffer.seek(0 )
_A = load_json(__A )
assert isinstance(__A , __A )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__A , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__A ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def __A ( self: int , __A: Union[str, Any] , __A: Union[str, Any] , __A: Optional[Any] ) -> Tuple:
with io.BytesIO() as buffer:
JsonDatasetWriter(__A , __A , lines=__A , num_proc=2 ).write()
buffer.seek(0 )
_A = load_json_function(__A )
assert isinstance(__A , __A )
assert isinstance(exported_content[0] , __A )
assert len(__A ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def __A ( self: Tuple , __A: int , __A: List[str] , __A: List[Any] , __A: List[Any] , __A: List[Any] ) -> Union[str, Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(__A , __A , lines=__A , orient=__A , num_proc=2 ).write()
buffer.seek(0 )
_A = load_json(__A )
assert isinstance(__A , __A )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__A , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__A ) == 10
def __A ( self: Tuple , __A: Optional[int] ) -> Optional[int]:
with pytest.raises(__A ):
with io.BytesIO() as buffer:
JsonDatasetWriter(__A , __A , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def __A ( self: Dict , __A: Tuple , __A: Dict , __A: Tuple , __A: Optional[Any] , __A: int ) -> Optional[Any]:
_A = tmp_path_factory.mktemp('''data''' ) / f"""test.json.{extension}"""
_A = str(shared_datadir / f"""test_file.json.{extension}""" )
JsonDatasetWriter(__A , __A , compression=__A ).write()
with fsspec.open(__A , '''rb''' , compression='''infer''' ) as f:
_A = f.read()
with fsspec.open(__A , '''rb''' , compression='''infer''' ) as f:
_A = f.read()
assert exported_content == original_content
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
import qiskit
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = qiskit.Aer.get_backend('''aer_simulator''' )
_A = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
_A = qiskit.execute(_lowercase , _lowercase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(_lowercase )
if __name__ == "__main__":
__A = half_adder(1, 1)
print(f'Half Adder Output Qubit Counts: {counts}')
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["image_processor", "tokenizer"]
A_ = "BlipImageProcessor"
A_ = ("BertTokenizer", "BertTokenizerFast")
def __init__( self: str , __A: List[Any] , __A: int ) -> Optional[int]:
_A = False
super().__init__(__A , __A )
_A = self.image_processor
def __call__( self: Any , __A: ImageInput = None , __A: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __A: bool = True , __A: Union[bool, str, PaddingStrategy] = False , __A: Union[bool, str, TruncationStrategy] = None , __A: Optional[int] = None , __A: int = 0 , __A: Optional[int] = None , __A: Optional[bool] = None , __A: bool = False , __A: bool = False , __A: bool = False , __A: bool = False , __A: bool = False , __A: bool = True , __A: Optional[Union[str, TensorType]] = None , **__A: int , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
_A = self.tokenizer
_A = self.tokenizer(
text=__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , stride=__A , pad_to_multiple_of=__A , return_attention_mask=__A , return_overflowing_tokens=__A , return_special_tokens_mask=__A , return_offsets_mapping=__A , return_token_type_ids=__A , return_length=__A , verbose=__A , return_tensors=__A , **__A , )
return text_encoding
# add pixel_values
_A = self.image_processor(__A , return_tensors=__A )
if text is not None:
_A = self.tokenizer(
text=__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , stride=__A , pad_to_multiple_of=__A , return_attention_mask=__A , return_overflowing_tokens=__A , return_special_tokens_mask=__A , return_offsets_mapping=__A , return_token_type_ids=__A , return_length=__A , verbose=__A , return_tensors=__A , **__A , )
else:
_A = None
if text_encoding is not None:
encoding_image_processor.update(__A )
return encoding_image_processor
def __A ( self: Optional[int] , *__A: Optional[int] , **__A: Optional[Any] ) -> int:
return self.tokenizer.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Any , **__A: List[str] ) -> Union[str, Any]:
return self.tokenizer.decode(*__A , **__A )
@property
def __A ( self: Tuple ) -> Tuple:
_A = self.tokenizer.model_input_names
_A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
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 SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[int] , __A: Optional[int] , __A: Optional[Any]=7 , __A: Any=3 , __A: Dict=18 , __A: List[Any]=30 , __A: Union[str, Any]=4_00 , __A: Tuple=True , __A: Dict=None , __A: Optional[int]=True , ) -> Optional[int]:
_A = size if size is not None else {'''height''': 18, '''width''': 18}
_A = parent
_A = batch_size
_A = num_channels
_A = image_size
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = apply_ocr
def __A ( self: List[Any] ) -> Optional[Any]:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __A ( self: Any ) -> Optional[int]:
_A = LayoutLMvaImageProcessingTester(self )
@property
def __A ( self: Optional[int] ) -> List[str]:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self: Optional[Any] ) -> List[str]:
_A = 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 __A ( self: Optional[int] ) -> Optional[int]:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def __A ( self: List[str] ) -> Union[str, Any]:
pass
def __A ( self: Optional[Any] ) -> Tuple:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = 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 = 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 = 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 __A ( self: Optional[int] ) -> str:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = 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 = 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 = 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 __A ( self: Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = 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 = 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 = 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 __A ( self: str ) -> Optional[Any]:
# with apply_OCR = True
_A = LayoutLMvaImageProcessor()
from datasets import load_dataset
_A = load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
_A = Image.open(ds[0]['''file'''] ).convert('''RGB''' )
_A = image_processing(__A , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
_A = [['''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 = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __A )
self.assertListEqual(encoding.boxes , __A )
# with apply_OCR = False
_A = LayoutLMvaImageProcessor(apply_ocr=__A )
_A = image_processing(__A , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: int ) -> None:
_A = data
_A = None
_A = None
def __A ( _lowercase ): # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def __A ( _lowercase ):
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def __A ( _lowercase ):
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def __A ( ): # Main function for testing.
'''simple docstring'''
_A = Node(1 )
_A = Node(2 )
_A = Node(3 )
_A = Node(4 )
_A = Node(5 )
_A = Node(6 )
_A = Node(7 )
_A = Node(8 )
_A = Node(9 )
print(is_full_binary_tree(_lowercase ) )
print(depth_of_tree(_lowercase ) )
print('''Tree is: ''' )
display(_lowercase )
if __name__ == "__main__":
main()
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = BigBirdConfig.from_json_file(_lowercase )
print(f"""Building PyTorch model from configuration: {config}""" )
if is_trivia_qa:
_A = BigBirdForQuestionAnswering(_lowercase )
else:
_A = BigBirdForPreTraining(_lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(_lowercase , _lowercase , is_trivia_qa=_lowercase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--big_bird_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
)
__A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__A = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
for attribute in key.split('''.''' ):
_A = getattr(_lowercase , _lowercase )
if weight_type is not None:
_A = getattr(_lowercase , _lowercase ).shape
else:
_A = 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 = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
_A = None
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_lowercase , _lowercase , _lowercase , _lowercase , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
elif name.split('''.''' )[0] == "proj":
_A = fairseq_model.proj
_A = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_lowercase )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _lowercase )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "bias" in name:
_A = '''bias'''
elif "weight" in name:
_A = '''weight'''
else:
_A = None
set_recursively(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
continue
if not is_used:
unused_weights.append(_lowercase )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = int(items[1] )
if type_id == 0:
if "bias" in name:
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 = 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 = 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 = 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 = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A ,_A = emb.weight.shape
_A = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
_A = emb.weight.data
return lin_layer
def __A ( _lowercase ):
'''simple docstring'''
with open(_lowercase , '''r''' , encoding='''utf-8''' ) as f:
_A = f.readlines()
_A = [line.split(''' ''' )[0] for line in lines]
_A = len(_lowercase )
_A = {
'''<s>''': 0,
'''<pad>''': 1,
'''</s>''': 2,
'''<unk>''': 3,
}
vocab_dict.update(dict(zip(_lowercase , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
_A = WavaVecaConfig.from_pretrained(_lowercase )
_A = SpeechaTextaConfig.from_pretrained(
_lowercase , vocab_size=_lowercase , decoder_layers=_lowercase , do_stable_layer_norm=_lowercase )
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_lowercase , return_attention_mask=_lowercase , )
_A ,_A ,_A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
_A = model[0].eval()
# set weights for wav2vec2 encoder
_A = WavaVecaModel(_lowercase )
_A = recursively_load_weights_wavaveca(model.encoder , _lowercase )
_A = SpeechaTextaForCausalLM(_lowercase )
_A ,_A = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=_lowercase )
# set output linear layer
unexpected_keys.remove('''embed_out''' )
_A = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
_A = SpeechEncoderDecoderModel(encoder=_lowercase , decoder=_lowercase )
_A = False
# add projection layer
_A = nn.Parameter(projection_layer.weight )
_A = nn.Parameter(projection_layer.bias )
_A = create_vocab_dict(_lowercase )
with open(os.path.join(_lowercase , '''vocab.json''' ) , '''w''' ) as fp:
json.dump(_lowercase , _lowercase )
_A = SpeechaTextaTokenizer(os.path.join(_lowercase , '''vocab.json''' ) )
tokenizer.save_pretrained(_lowercase )
_A = hf_wavavec.config.to_dict()
_A = tokenizer.pad_token_id
_A = tokenizer.bos_token_id
_A = tokenizer.eos_token_id
_A = '''speech_to_text_2'''
_A = '''wav2vec2'''
_A = SpeechEncoderDecoderConfig.from_dict(_lowercase )
hf_wavavec.save_pretrained(_lowercase )
feature_extractor.save_pretrained(_lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument(
'--encoder_config_path',
default='facebook/wav2vec2-large-lv60',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/s2t-small-mustc-en-fr-st',
type=str,
help='Path to hf decoder s2t checkpoint config',
)
parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')
__A = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: Tuple , __A: Optional[Any]=13 , __A: List[Any]=32 , __A: str=2 , __A: Optional[int]=3 , __A: Dict=16 , __A: Tuple=[1, 2, 1] , __A: str=[2, 2, 4] , __A: List[str]=2 , __A: Tuple=2.0 , __A: List[str]=True , __A: List[Any]=0.0 , __A: List[Any]=0.0 , __A: int=0.1 , __A: str="gelu" , __A: Optional[Any]=False , __A: str=True , __A: List[str]=0.02 , __A: Dict=1e-5 , __A: Union[str, Any]=True , __A: str=None , __A: List[Any]=True , __A: Optional[Any]=10 , __A: Dict=8 , __A: Optional[int]=["stage1", "stage2", "stage3"] , __A: List[Any]=[1, 2, 3] , ) -> Tuple:
_A = parent
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = embed_dim
_A = depths
_A = num_heads
_A = window_size
_A = mlp_ratio
_A = qkv_bias
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = drop_path_rate
_A = hidden_act
_A = use_absolute_embeddings
_A = patch_norm
_A = layer_norm_eps
_A = initializer_range
_A = is_training
_A = scope
_A = use_labels
_A = type_sequence_label_size
_A = encoder_stride
_A = out_features
_A = out_indices
def __A ( self: List[Any] ) -> Tuple:
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = self.get_config()
return config, pixel_values, labels
def __A ( self: str ) -> Dict:
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def __A ( self: Optional[int] , __A: str , __A: Any , __A: Dict ) -> str:
_A = MaskFormerSwinModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A )
_A = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_A = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __A ( self: Optional[Any] , __A: Dict , __A: Any , __A: List[str] ) -> List[Any]:
_A = MaskFormerSwinBackbone(config=__A )
model.to(__A )
model.eval()
_A = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(__A ):
_A = ['''stem''']
_A = MaskFormerSwinBackbone(config=__A )
def __A ( self: List[str] ) -> Dict:
_A = self.prepare_config_and_inputs()
_A ,_A ,_A = config_and_inputs
_A = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
A_ = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
A_ = False
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: Optional[int] ) -> Optional[Any]:
_A = MaskFormerSwinModelTester(self )
_A = ConfigTester(self , config_class=__A , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
''' `nn.DataParallel`'''
) )
def __A ( self: Optional[int] ) -> List[Any]:
pass
def __A ( self: str ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __A ( self: int ) -> Optional[Any]:
return
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __A ( self: Dict ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
@unittest.skip('''Swin does not use inputs_embeds''' )
def __A ( self: Dict ) -> Any:
pass
@unittest.skip('''Swin does not support feedforward chunking''' )
def __A ( self: Tuple ) -> List[str]:
pass
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A , nn.Linear ) )
def __A ( self: Tuple ) -> Optional[int]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions''' )
def __A ( self: Any ) -> List[Any]:
pass
@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone''' )
def __A ( self: Union[str, Any] ) -> Union[str, Any]:
pass
def __A ( self: Optional[int] , __A: int , __A: Union[str, Any] , __A: List[Any] , __A: Tuple ) -> Any:
_A = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_A = model(**self._prepare_for_class(__A , __A ) )
_A = outputs.hidden_states
_A = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ) , __A )
# Swin has a different seq_length
_A = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_A = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __A ( self: Any ) -> Union[str, Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
_A = True
self.check_hidden_states_output(__A , __A , __A , __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A = True
self.check_hidden_states_output(__A , __A , __A , __A )
def __A ( self: int ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = 3
_A = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_A = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_A = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_A = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
_A = True
self.check_hidden_states_output(__A , __A , __A , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A = True
self.check_hidden_states_output(__A , __A , __A , (padded_height, padded_width) )
@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints''' )
def __A ( self: List[str] ) -> Union[str, Any]:
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def __A ( self: Tuple ) -> Dict:
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def __A ( self: Dict ) -> str:
pass
def __A ( self: str ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(__A: int ):
_A = 0
return t
def check_equivalence(__A: List[Any] , __A: Optional[Any] , __A: str , __A: List[Any]={} ):
with torch.no_grad():
_A = model(**__A , return_dict=__A , **__A )
_A = model(**__A , return_dict=__A , **__A ).to_tuple()
def recursive_check(__A: Tuple , __A: Any ):
if isinstance(__A , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__A , __A ):
recursive_check(__A , __A )
elif isinstance(__A , __A ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(__A , __A )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(__A ) , set_nan_tensor_to_zero(__A ) , atol=1e-5 ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(__A ).any()} and `inf`: {torch.isinf(__A )}. Dict has"""
f""" `nan`: {torch.isnan(__A ).any()} and `inf`: {torch.isinf(__A )}."""
) , )
recursive_check(__A , __A )
for model_class in self.all_model_classes:
_A = model_class(__A )
model.to(__A )
model.eval()
_A = self._prepare_for_class(__A , __A )
_A = self._prepare_for_class(__A , __A )
check_equivalence(__A , __A , __A )
_A = self._prepare_for_class(__A , __A , return_labels=__A )
_A = self._prepare_for_class(__A , __A , return_labels=__A )
check_equivalence(__A , __A , __A )
_A = self._prepare_for_class(__A , __A )
_A = self._prepare_for_class(__A , __A )
check_equivalence(__A , __A , __A , {'''output_hidden_states''': True} )
_A = self._prepare_for_class(__A , __A , return_labels=__A )
_A = self._prepare_for_class(__A , __A , return_labels=__A )
check_equivalence(__A , __A , __A , {'''output_hidden_states''': True} )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase , snake_case ):
"""simple docstring"""
A_ = (MaskFormerSwinBackbone,) if is_torch_available() else ()
A_ = MaskFormerSwinConfig
def __A ( self: Union[str, Any] ) -> int:
_A = MaskFormerSwinModelTester(self )
def __A ( self: Tuple ) -> Any:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = inputs_dict['''pixel_values'''].shape[0]
for backbone_class in self.all_model_classes:
_A = backbone_class(__A )
backbone.to(__A )
backbone.eval()
_A = backbone(**__A )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , __A )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
_A = backbone(**__A , output_hidden_states=__A )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
_A ,_A ,_A = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
_A = backbone(**__A , output_attentions=__A )
self.assertIsNotNone(outputs.attentions )
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
import argparse
import copy
def __A ( _lowercase ):
'''simple docstring'''
_A = {}
with open(_lowercase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_A = []
_list.append([line.split()[1], line.split()[2]] )
_A = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_A = []
_list.append([line.split()[0], line.split()[2]] )
_A = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
with open(_lowercase ) as f:
_A = f.read(1 )
_A = start_node
_A = []
_A = start_node
_A = 0
while visiting not in first_solution:
_A = 1_00_00
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowercase ) and k[0] not in first_solution:
_A = k[1]
_A = k[0]
first_solution.append(_lowercase )
_A = distance_of_first_solution + int(_lowercase )
_A = best_node
first_solution.append(_lowercase )
_A = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_A = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_00_00
)
return first_solution, distance_of_first_solution
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = []
for n in solution[1:-1]:
_A = solution.index(_lowercase )
for kn in solution[1:-1]:
_A = solution.index(_lowercase )
if n == kn:
continue
_A = copy.deepcopy(_lowercase )
_A = kn
_A = n
_A = 0
for k in _tmp[:-1]:
_A = _tmp[_tmp.index(_lowercase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_A = distance + int(i[1] )
_tmp.append(_lowercase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_A = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowercase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = 1
_A = first_solution
_A = []
_A = distance_of_first_solution
_A = solution
while count <= iters:
_A = find_neighborhood(_lowercase , _lowercase )
_A = 0
_A = neighborhood[index_of_best_solution]
_A = len(_lowercase ) - 1
_A = False
while not found:
_A = 0
while i < len(_lowercase ):
if best_solution[i] != solution[i]:
_A = best_solution[i]
_A = solution[i]
break
_A = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_A = True
_A = best_solution[:-1]
_A = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_A = cost
_A = solution
else:
_A = index_of_best_solution + 1
_A = neighborhood[index_of_best_solution]
if len(_lowercase ) >= size:
tabu_list.pop(0 )
_A = count + 1
return best_solution_ever, best_cost
def __A ( _lowercase=None ):
'''simple docstring'''
_A = generate_neighbours(args.File )
_A ,_A = generate_first_solution(
args.File , _lowercase )
_A ,_A = tabu_search(
_lowercase , _lowercase , _lowercase , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __A ( _lowercase , _lowercase , _lowercase = "x" , _lowercase = 10**-10 , _lowercase = 1 , ):
'''simple docstring'''
_A = symbols(_lowercase )
_A = lambdify(_lowercase , _lowercase )
_A = lambdify(_lowercase , diff(_lowercase , _lowercase ) )
_A = starting_point
while True:
if diff_function(_lowercase ) != 0:
_A = prev_guess - multiplicity * func(_lowercase ) / diff_function(
_lowercase )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
_A = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def __A ( ):
'''simple docstring'''
_A = torch.nn.Linear(2 , 4 )
_A = torch.optim.AdamW(model.parameters() , lr=1.0 )
_A = torch.optim.lr_scheduler.OneCycleLR(_lowercase , max_lr=0.01 , steps_per_epoch=2 , epochs=1 )
_A = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
_A = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def __A ( _lowercase ):
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def __A ( _lowercase ):
'''simple docstring'''
_A = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@require_cuda
def __A ( self: int ) -> List[str]:
_A = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(__A ):
_A = Accelerator(cpu=__A )
def __A ( self: Tuple ) -> List[str]:
_A = Accelerator()
_A = GradientState()
assert state.num_steps == 1
_A = 4
assert state.num_steps == 4
assert state.sync_gradients is True
_A = False
assert state.sync_gradients is False
GradientState._reset_state()
def __A ( self: int ) -> Union[str, Any]:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = accelerator.prepare(__A , __A , __A , __A , __A )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def __A ( self: Optional[int] ) -> Any:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
accelerator.prepare(__A , __A , __A , __A , __A )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def __A ( self: List[str] ) -> List[Any]:
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__A: int , **__A: Any ):
pass
with patch('''torch.cuda.set_device''' , __A ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
_A = Accelerator()
self.assertEqual(str(accelerator.state.device ) , '''cuda:64''' )
def __A ( self: Any ) -> Dict:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
accelerator.prepare(__A , __A , __A , __A , __A )
_A = get_signature(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__A )
# make sure random weights don't match
load_random_weights(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) > 1e-3 )
# make sure loaded weights match
accelerator.load_state(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) < 1e-3 )
def __A ( self: List[str] ) -> Dict:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
accelerator.prepare(__A , __A , __A , __A , __A )
_A = get_signature(__A )
# saving hook
def save_config(__A: Optional[int] , __A: List[Any] , __A: Tuple ):
_A = {'''class_name''': models[0].__class__.__name__}
with open(os.path.join(__A , '''data.json''' ) , '''w''' ) as f:
json.dump(__A , __A )
# loading hook
def load_config(__A: List[Any] , __A: List[Any] ):
with open(os.path.join(__A , '''data.json''' ) , '''r''' ) as f:
_A = json.load(__A )
_A = config['''class_name''']
_A = accelerator.register_save_state_pre_hook(__A )
_A = accelerator.register_load_state_pre_hook(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__A )
# make sure random weights don't match with hooks
load_random_weights(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) > 1e-3 )
# random class name to verify correct one is loaded
_A = '''random'''
# make sure loaded weights match with hooks
accelerator.load_state(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) < 1e-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__A )
# make sure random weights don't match with hooks removed
load_random_weights(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) > 1e-3 )
# random class name to verify correct one is loaded
_A = '''random'''
# make sure loaded weights match with hooks removed
accelerator.load_state(__A )
self.assertTrue(abs(model_signature - get_signature(__A ) ) < 1e-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def __A ( self: int ) -> Dict:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
_A = None
# This should work
_A ,_A ,_A ,_A ,_A ,_A = accelerator.prepare(
__A , __A , __A , __A , __A , __A )
self.assertTrue(dummy_obj is None )
def __A ( self: Union[str, Any] ) -> Dict:
_A = Accelerator()
_A ,_A ,_A ,_A ,_A = create_components()
_A = [1, 2, 3]
# This should work
_A ,_A ,_A ,_A ,_A ,_A = accelerator.prepare(
__A , __A , __A , __A , __A , __A )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Dummy object should have `_is_accelerate_prepared` set to `True`''' , )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Model is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(__A , '''_is_accelerate_prepared''' , __A ) , __A , '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`''' , )
@slow
@require_bnb
def __A ( self: Union[str, Any] ) -> List[str]:
from transformers import AutoModelForCausalLM
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=__A , device_map={'''''': 0} , )
_A = Accelerator()
# This should work
_A = accelerator.prepare(__A )
@slow
@require_bnb
def __A ( self: Optional[Any] ) -> Dict:
from transformers import AutoModelForCausalLM
_A = Accelerator()
with init_empty_weights():
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
model.tie_weights()
_A = infer_auto_device_map(__A )
_A = '''cpu'''
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , device_map=__A , load_in_abit=__A , llm_inta_enable_fpaa_cpu_offload=__A )
# This should not work and get value error
with self.assertRaises(__A ):
_A = accelerator.prepare(__A )
@slow
@require_bnb
@require_multi_gpu
def __A ( self: Any ) -> int:
from transformers import AutoModelForCausalLM
_A = {'''distributed_type''': DistributedType.MULTI_GPU}
with init_empty_weights():
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
model.tie_weights()
_A = infer_auto_device_map(__A )
_A = 1
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=__A , device_map=__A , )
_A = Accelerator()
# This should not work and get value error
with self.assertRaises(__A ):
_A = accelerator.prepare(__A )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def __A ( self: Tuple ) -> Union[str, Any]:
from transformers import AutoModelForCausalLM
with init_empty_weights():
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
_A = infer_auto_device_map(__A )
_A = 1
_A = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=__A , device_map=__A , )
_A = Accelerator()
# This should work
_A = accelerator.prepare(__A )
@require_cuda
def __A ( self: Tuple ) -> Optional[int]:
_A = torch.nn.Linear(10 , 10 )
_A = torch.optim.SGD(model.parameters() , lr=0.01 )
_A = Accelerator(cpu=__A )
_A = accelerator.prepare(__A )
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import math
import tensorflow as tf
from packaging import version
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
_A = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
_A = tf.cast(math.pi , x.dtype )
_A = tf.cast(0.04_47_15 , x.dtype )
_A = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(_lowercase , 3 )) ))
return x * cdf
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
return x * tf.tanh(tf.math.softplus(_lowercase ) )
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
_A = tf.cast(0.04_47_15 , x.dtype )
_A = tf.cast(0.79_78_84_56_08 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
_A = tf.cast(1.7_02 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __A ( _lowercase ):
'''simple docstring'''
return tf.clip_by_value(_gelu(_lowercase ) , -10 , 10 )
def __A ( _lowercase , _lowercase=-1 ):
'''simple docstring'''
_A ,_A = tf.split(_lowercase , 2 , axis=_lowercase )
return a * tf.math.sigmoid(_lowercase )
if version.parse(tf.version.VERSION) >= version.parse('2.4'):
def __A ( _lowercase ):
'''simple docstring'''
return tf.keras.activations.gelu(_lowercase , approximate=_lowercase )
__A = tf.keras.activations.gelu
__A = approximate_gelu_wrap
else:
__A = _gelu
__A = _gelu_new
__A = {
'gelu': gelu,
'gelu_10': gelu_aa,
'gelu_fast': gelu_fast,
'gelu_new': gelu_new,
'glu': glu,
'mish': mish,
'quick_gelu': quick_gelu,
'relu': tf.keras.activations.relu,
'sigmoid': tf.keras.activations.sigmoid,
'silu': tf.keras.activations.swish,
'swish': tf.keras.activations.swish,
'tanh': tf.keras.activations.tanh,
}
def __A ( _lowercase ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )
_A = ''''''
while len(_lowercase ) % 3 != 0:
_A = '''0''' + bin_string
_A = [
bin_string[index : index + 3]
for index in range(len(_lowercase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
_A = 0
for index, val in enumerate(_lowercase ):
oct_val += int(2 ** (2 - index) * int(_lowercase ) )
oct_string += str(_lowercase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["image_processor", "feature_extractor"]
A_ = "TvltImageProcessor"
A_ = "TvltFeatureExtractor"
def __init__( self: List[str] , __A: List[Any] , __A: List[str] ) -> List[str]:
super().__init__(image_processor=__A , feature_extractor=__A )
_A = image_processor
_A = feature_extractor
def __call__( self: List[str] , __A: Any=None , __A: Any=None , __A: Optional[int]=None , __A: str=None , __A: List[Any]=False , __A: Tuple=False , *__A: List[str] , **__A: Optional[Any] , ) -> Optional[Any]:
if images is None and audio is None:
raise ValueError('''You need to specify either an `images` or `audio` input to process.''' )
_A = None
if images is not None:
_A = self.image_processor(__A , mask_pixel=__A , *__A , **__A )
if images_mixed is not None:
_A = self.image_processor(__A , is_mixed=__A , *__A , **__A )
if audio is not None:
_A = self.feature_extractor(
__A , *__A , sampling_rate=__A , mask_audio=__A , **__A )
_A = {}
if audio is not None:
output_dict.update(__A )
if images is not None:
output_dict.update(__A )
if images_mixed_dict is not None:
output_dict.update(__A )
return output_dict
@property
def __A ( self: Optional[Any] ) -> int:
_A = self.image_processor.model_input_names
_A = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = 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 __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Any , *__A: Union[str, Any] , **__A: Any ) -> Optional[int]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Dict , *__A: str , **__A: List[Any] ) -> Dict:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Optional[Any] , *__A: List[Any] , **__A: int ) -> Any:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Dict , *__A: int , **__A: Any ) -> Any:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Tuple , *__A: int , **__A: Any ) -> str:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Any , *__A: int , **__A: Optional[int] ) -> Any:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[Any] , *__A: Union[str, Any] , **__A: Dict ) -> Optional[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Union[str, Any] , *__A: Dict , **__A: Optional[int] ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Any , *__A: Optional[Any] , **__A: Any ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Union[str, Any] , *__A: str , **__A: Tuple ) -> int:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Union[str, Any] , *__A: Optional[Any] , **__A: List[Any] ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: str , *__A: Tuple , **__A: List[Any] ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Union[str, Any] , *__A: List[str] , **__A: Dict ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: List[str] , *__A: Any , **__A: int ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Union[str, Any] , *__A: Union[str, Any] , **__A: Any ) -> int:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[str] , *__A: Tuple , **__A: int ) -> Any:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Dict , *__A: Optional[Any] , **__A: List[str] ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Any , *__A: Dict , **__A: List[Any] ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[Any] , *__A: List[str] , **__A: Dict ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: Any , **__A: Optional[Any] ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Union[str, Any] , *__A: Optional[int] , **__A: Tuple ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Union[str, Any] , *__A: Any , **__A: List[str] ) -> Optional[int]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: int , **__A: int ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Dict , *__A: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Dict , *__A: List[Any] , **__A: Any ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: str , *__A: int , **__A: Optional[Any] ) -> str:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Tuple , *__A: List[str] , **__A: Optional[int] ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[Any] , *__A: Optional[int] , **__A: Union[str, Any] ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: List[Any] , *__A: Any , **__A: List[Any] ) -> str:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: List[Any] , **__A: int ) -> Any:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[str] , *__A: Tuple , **__A: Optional[Any] ) -> Any:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: Union[str, Any] , *__A: str , **__A: Dict ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: Optional[int] , *__A: Optional[Any] , **__A: List[Any] ) -> str:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: List[Any] , *__A: Any , **__A: Any ) -> Optional[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: Tuple , **__A: str ) -> Dict:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: List[str] , *__A: int , **__A: Tuple ) -> Dict:
requires_backends(cls , ['''flax'''] )
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Tuple , *__A: str , **__A: Union[str, Any] ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: List[Any] , **__A: List[str] ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def __A ( cls: int , *__A: str , **__A: str ) -> str:
requires_backends(cls , ['''flax'''] )
| 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "efficientformer"
def __init__( self: Dict , __A: List[int] = [3, 2, 6, 4] , __A: List[int] = [48, 96, 2_24, 4_48] , __A: List[bool] = [True, True, True, True] , __A: int = 4_48 , __A: int = 32 , __A: int = 4 , __A: int = 7 , __A: int = 5 , __A: int = 8 , __A: int = 4 , __A: float = 0.0 , __A: int = 16 , __A: int = 3 , __A: int = 3 , __A: int = 3 , __A: int = 2 , __A: int = 1 , __A: float = 0.0 , __A: int = 1 , __A: bool = True , __A: bool = True , __A: float = 1e-5 , __A: str = "gelu" , __A: float = 0.02 , __A: float = 1e-12 , __A: int = 2_24 , __A: float = 1e-05 , **__A: int , ) -> None:
super().__init__(**__A )
_A = hidden_act
_A = hidden_dropout_prob
_A = hidden_sizes
_A = num_hidden_layers
_A = num_attention_heads
_A = initializer_range
_A = layer_norm_eps
_A = patch_size
_A = num_channels
_A = depths
_A = mlp_expansion_ratio
_A = downsamples
_A = dim
_A = key_dim
_A = attention_ratio
_A = resolution
_A = pool_size
_A = downsample_patch_size
_A = downsample_stride
_A = downsample_pad
_A = drop_path_rate
_A = num_metaad_blocks
_A = distillation
_A = use_layer_scale
_A = layer_scale_init_value
_A = image_size
_A = batch_norm_eps
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = None
if token is not None:
_A = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}
_A = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
_A = requests.get(_lowercase , headers=_lowercase ).json()
_A = {}
try:
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
_A = math.ceil((result['''total_count'''] - 1_00) / 1_00 )
for i in range(_lowercase ):
_A = requests.get(url + f"""&page={i + 2}""" , headers=_lowercase ).json()
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return job_links
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = None
if token is not None:
_A = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}
_A = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
_A = requests.get(_lowercase , headers=_lowercase ).json()
_A = {}
try:
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
_A = math.ceil((result['''total_count'''] - 1_00) / 1_00 )
for i in range(_lowercase ):
_A = requests.get(url + f"""&page={i + 2}""" , headers=_lowercase ).json()
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
return artifacts
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = None
if token is not None:
_A = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}
_A = requests.get(_lowercase , headers=_lowercase , allow_redirects=_lowercase )
_A = result.headers['''Location''']
_A = requests.get(_lowercase , allow_redirects=_lowercase )
_A = os.path.join(_lowercase , f"""{artifact_name}.zip""" )
with open(_lowercase , '''wb''' ) as fp:
fp.write(response.content )
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = []
_A = []
_A = None
with zipfile.ZipFile(_lowercase ) as z:
for filename in z.namelist():
if not os.path.isdir(_lowercase ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_lowercase ) as f:
for line in f:
_A = line.decode('''UTF-8''' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
_A = line[: line.index(''': ''' )]
_A = line[line.index(''': ''' ) + len(''': ''' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('''FAILED ''' ):
# `test` is the test method that failed
_A = line[len('''FAILED ''' ) :]
failed_tests.append(_lowercase )
elif filename == "job_name.txt":
_A = line
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
f"""`errors` and `failed_tests` should have the same number of elements. Got {len(_lowercase )} for `errors` """
f"""and {len(_lowercase )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
''' problem.''' )
_A = None
if job_name and job_links:
_A = job_links.get(_lowercase , _lowercase )
# A list with elements of the form (line of error, error, failed test)
_A = [x + [y] + [job_link] for x, y in zip(_lowercase , _lowercase )]
return result
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = []
_A = [os.path.join(_lowercase , _lowercase ) for p in os.listdir(_lowercase ) if p.endswith('''.zip''' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_lowercase , job_links=_lowercase ) )
return errors
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = Counter()
counter.update([x[1] for x in logs] )
_A = counter.most_common()
_A = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
_A = {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}
_A = dict(sorted(r.items() , key=lambda _lowercase : item[1]["count"] , reverse=_lowercase ) )
return r
def __A ( _lowercase ):
'''simple docstring'''
_A = test.split('''::''' )[0]
if test.startswith('''tests/models/''' ):
_A = test.split('''/''' )[2]
else:
_A = None
return test
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = [(x[0], x[1], get_model(x[2] )) for x in logs]
_A = [x for x in logs if x[2] is not None]
_A = {x[2] for x in logs}
_A = {}
for test in tests:
_A = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
_A = counter.most_common()
_A = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
_A = sum(error_counts.values() )
if n_errors > 0:
_A = {'''count''': n_errors, '''errors''': error_counts}
_A = dict(sorted(r.items() , key=lambda _lowercase : item[1]["count"] , reverse=_lowercase ) )
return r
def __A ( _lowercase ):
'''simple docstring'''
_A = '''| no. | error | status |'''
_A = '''|-:|:-|:-|'''
_A = [header, sep]
for error in reduced_by_error:
_A = reduced_by_error[error]['''count''']
_A = f"""| {count} | {error[:1_00]} | |"""
lines.append(_lowercase )
return "\n".join(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = '''| model | no. of errors | major error | count |'''
_A = '''|-:|-:|-:|-:|'''
_A = [header, sep]
for model in reduced_by_model:
_A = reduced_by_model[model]['''count''']
_A ,_A = list(reduced_by_model[model]['''errors'''].items() )[0]
_A = f"""| {model} | {count} | {error[:60]} | {_count} |"""
lines.append(_lowercase )
return "\n".join(_lowercase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
__A = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
__A = get_job_links(args.workflow_run_id, token=args.token)
__A = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
__A = k.find(' / ')
__A = k[index + len(' / ') :]
__A = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
__A = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
__A = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
__A = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
__A = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
__A = reduce_by_error(errors)
__A = reduce_by_model(errors)
__A = make_github_table(reduced_by_error)
__A = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__A = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Tuple , __A: str , __A: Dict=7 , __A: Tuple=3 , __A: Dict=18 , __A: Dict=30 , __A: Tuple=4_00 , __A: Optional[int]=None , __A: List[Any]=True , __A: int=True , __A: Optional[int]=None , ) -> str:
_A = size if size is not None else {'''height''': 20, '''width''': 20}
_A = parent
_A = batch_size
_A = num_channels
_A = image_size
_A = min_resolution
_A = max_resolution
_A = size
_A = do_normalize
_A = do_convert_rgb
_A = [5_12, 10_24, 20_48, 40_96]
_A = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
def __A ( self: Tuple ) -> Optional[Any]:
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __A ( self: str ) -> List[str]:
_A = '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
_A = Image.open(requests.get(__A , stream=__A ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." , )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = PixaStructImageProcessor if is_vision_available() else None
def __A ( self: Optional[int] ) -> Any:
_A = PixaStructImageProcessingTester(self )
@property
def __A ( self: List[Any] ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , '''do_normalize''' ) )
self.assertTrue(hasattr(__A , '''do_convert_rgb''' ) )
def __A ( self: Any ) -> Union[str, Any]:
_A = self.image_processor_tester.prepare_dummy_image()
_A = self.image_processing_class(**self.image_processor_dict )
_A = 20_48
_A = image_processor(__A , return_tensors='''pt''' , max_patches=__A )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0_606 ) , atol=1e-3 , rtol=1e-3 ) )
def __A ( self: Optional[int] ) -> List[str]:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = 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 = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_A = image_processor(
__A , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self: Any ) -> Tuple:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = 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 = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
_A = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(__A ):
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A ).flattened_patches
_A = '''Hello'''
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A , header_text=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_A = image_processor(
__A , return_tensors='''pt''' , max_patches=__A , header_text=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self: Optional[int] ) -> Any:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
_A = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_A = image_processor(
__A , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self: Tuple ) -> str:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = 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 = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_A = image_processor(
__A , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." , )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = PixaStructImageProcessor if is_vision_available() else None
def __A ( self: Optional[int] ) -> List[str]:
_A = PixaStructImageProcessingTester(self , num_channels=4 )
_A = 3
@property
def __A ( self: List[Any] ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self: str ) -> Dict:
_A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , '''do_normalize''' ) )
self.assertTrue(hasattr(__A , '''do_convert_rgb''' ) )
def __A ( self: List[Any] ) -> str:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = 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 = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_A = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_A = image_processor(
__A , return_tensors='''pt''' , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "Salesforce/blip-image-captioning-base"
A_ = (
"This is a tool that generates a description of an image. It takes an input named `image` which should be the "
"image to caption, and returns a text that contains the description in English."
)
A_ = "image_captioner"
A_ = AutoModelForVisionaSeq
A_ = ["image"]
A_ = ["text"]
def __init__( self: str , *__A: str , **__A: Optional[Any] ) -> List[Any]:
requires_backends(self , ['''vision'''] )
super().__init__(*__A , **__A )
def __A ( self: Union[str, Any] , __A: "Image" ) -> int:
return self.pre_processor(images=__A , return_tensors='''pt''' )
def __A ( self: List[str] , __A: List[Any] ) -> Dict:
return self.model.generate(**__A )
def __A ( self: List[Any] , __A: Optional[int] ) -> List[str]:
return self.pre_processor.batch_decode(__A , skip_special_tokens=__A )[0].strip()
| 62 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['VivitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'VivitModel',
'VivitPreTrainedModel',
'VivitForVideoClassification',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: Any ) -> Optional[Any]:
_A = data
_A = None
def __repr__( self: int ) -> str:
return f"""Node({self.data})"""
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int ) -> Any:
_A = None
def __iter__( self: List[str] ) -> Any:
_A = self.head
while node:
yield node.data
_A = node.next
def __len__( self: int ) -> int:
return sum(1 for _ in self )
def __repr__( self: Optional[int] ) -> str:
return "->".join([str(__A ) for item in self] )
def __getitem__( self: int , __A: int ) -> Any:
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: Tuple , __A: int , __A: Any ) -> None:
if not 0 <= index < len(self ):
raise ValueError('''list index out of range.''' )
_A = self.head
for _ in range(__A ):
_A = current.next
_A = data
def __A ( self: int , __A: Any ) -> None:
self.insert_nth(len(self ) , __A )
def __A ( self: List[str] , __A: Any ) -> None:
self.insert_nth(0 , __A )
def __A ( self: str , __A: int , __A: Any ) -> None:
if not 0 <= index <= len(self ):
raise IndexError('''list index out of range''' )
_A = Node(__A )
if self.head is None:
_A = new_node
elif index == 0:
_A = self.head # link new_node to head
_A = new_node
else:
_A = self.head
for _ in range(index - 1 ):
_A = temp.next
_A = temp.next
_A = new_node
def __A ( self: Union[str, Any] ) -> None: # print every node data
print(self )
def __A ( self: Dict ) -> Any:
return self.delete_nth(0 )
def __A ( self: Tuple ) -> Any: # delete from tail
return self.delete_nth(len(self ) - 1 )
def __A ( self: Optional[Any] , __A: int = 0 ) -> Any:
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('''List index out of range.''' )
_A = self.head # default first node
if index == 0:
_A = self.head.next
else:
_A = self.head
for _ in range(index - 1 ):
_A = temp.next
_A = temp.next
_A = temp.next.next
return delete_node.data
def __A ( self: Any ) -> bool:
return self.head is None
def __A ( self: Any ) -> None:
_A = None
_A = self.head
while current:
# Store the current node's next node.
_A = current.next
# Make the current node's next point backwards
_A = prev
# Make the previous node be the current node
_A = current
# Make the current node the next node (to progress iteration)
_A = next_node
# Return prev in order to put the head at the end
_A = prev
def __A ( ):
'''simple docstring'''
_A = LinkedList()
assert linked_list.is_empty() is True
assert str(_lowercase ) == ""
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(_lowercase ) == i
linked_list.insert_nth(_lowercase , i + 1 )
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_lowercase ) == "->".join(str(_lowercase ) 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(_lowercase ) == 9
assert str(_lowercase ) == "->".join(str(_lowercase ) 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 = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8 , 1 ) )
def __A ( ):
'''simple docstring'''
_A = [
-9,
1_00,
Node(77_34_51_12 ),
'''dlrow olleH''',
7,
55_55,
0,
-1_92.5_55_55,
'''Hello, world!''',
77.9,
Node(10 ),
None,
None,
12.20,
]
_A = LinkedList()
for i in test_input:
linked_list.insert_tail(_lowercase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_lowercase ) == "-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 = linked_list.delete_head()
assert result == -9
assert (
str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
_A = linked_list.delete_tail()
assert result == 12.2
assert (
str(_lowercase ) == "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 = linked_list.delete_nth(10 )
assert result is None
assert (
str(_lowercase ) == "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(_lowercase )
== "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(_lowercase )
assert (
str(_lowercase )
== "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(_lowercase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def __A ( ):
'''simple docstring'''
from doctest import testmod
testmod()
_A = 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(_lowercase )
print('''\nReading/changing Node data using indexing:''' )
print(f"""Element at Position 1: {linked_list[1]}""" )
_A = input('''Enter New Value: ''' ).strip()
print('''New list:''' )
print(_lowercase )
print(f"""length of linked_list is : {len(_lowercase )}""" )
if __name__ == "__main__":
main()
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
__A = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
__A = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
__A = 'UperNetConfig'
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: Tuple , __A: int , __A: int , __A: Union[int, Tuple[int, int]] , __A: Union[int, Tuple[int, int], str] = 0 , __A: bool = False , __A: Union[int, Tuple[int, int]] = 1 , ) -> None:
super().__init__()
_A = nn.Convad(
in_channels=__A , out_channels=__A , kernel_size=__A , padding=__A , bias=__A , dilation=__A , )
_A = nn.BatchNormad(__A )
_A = nn.ReLU()
def __A ( self: List[str] , __A: torch.Tensor ) -> torch.Tensor:
_A = self.conv(__A )
_A = self.batch_norm(__A )
_A = self.activation(__A )
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: List[str] , __A: int , __A: int , __A: int ) -> None:
super().__init__()
_A = [
nn.AdaptiveAvgPoolad(__A ),
UperNetConvModule(__A , __A , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(__A ) , __A )
def __A ( self: int , __A: torch.Tensor ) -> torch.Tensor:
_A = input
for layer in self.layers:
_A = layer(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: int , __A: Tuple[int, ...] , __A: int , __A: int , __A: bool ) -> None:
super().__init__()
_A = pool_scales
_A = align_corners
_A = in_channels
_A = channels
_A = []
for i, pool_scale in enumerate(__A ):
_A = UperNetPyramidPoolingBlock(pool_scale=__A , in_channels=__A , channels=__A )
self.blocks.append(__A )
self.add_module(str(__A ) , __A )
def __A ( self: List[Any] , __A: torch.Tensor ) -> List[torch.Tensor]:
_A = []
for ppm in self.blocks:
_A = ppm(__A )
_A = nn.functional.interpolate(
__A , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(__A )
return ppm_outs
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: Optional[Any] , __A: int , __A: Optional[int] ) -> Optional[int]:
super().__init__()
_A = config
_A = config.pool_scales # e.g. (1, 2, 3, 6)
_A = in_channels
_A = config.hidden_size
_A = False
_A = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
_A = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
_A = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
_A = nn.ModuleList()
_A = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
_A = UperNetConvModule(__A , self.channels , kernel_size=1 )
_A = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(__A )
self.fpn_convs.append(__A )
_A = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def __A ( self: List[str] ) -> List[str]:
self.apply(self._init_weights )
def __A ( self: List[str] , __A: Union[str, Any] ) -> Optional[int]:
if isinstance(__A , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __A ( self: Dict , __A: Tuple ) -> Any:
_A = inputs[-1]
_A = [x]
psp_outs.extend(self.psp_modules(__A ) )
_A = torch.cat(__A , dim=1 )
_A = self.bottleneck(__A )
return output
def __A ( self: Dict , __A: torch.Tensor ) -> torch.Tensor:
# build laterals
_A = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(__A ) )
# build top-down path
_A = len(__A )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_A = laterals[i - 1].shape[2:]
_A = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=__A , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
_A = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_A = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
_A = torch.cat(__A , dim=1 )
_A = self.fpn_bottleneck(__A )
_A = self.classifier(__A )
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: str , __A: str , __A: int = 2 , __A: int = 3 , __A: Union[int, Tuple[int, int]] = 1 ) -> None:
super().__init__()
_A = config
_A = config.auxiliary_in_channels
_A = config.auxiliary_channels
_A = config.auxiliary_num_convs
_A = config.auxiliary_concat_input
_A = in_index
_A = (kernel_size // 2) * dilation
_A = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=__A , padding=__A , dilation=__A ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=__A , padding=__A , dilation=__A ) )
if self.num_convs == 0:
_A = nn.Identity()
else:
_A = nn.Sequential(*__A )
if self.concat_input:
_A = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=__A , padding=kernel_size // 2 )
_A = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def __A ( self: Tuple ) -> Optional[Any]:
self.apply(self._init_weights )
def __A ( self: Tuple , __A: List[Any] ) -> str:
if isinstance(__A , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __A ( self: List[Any] , __A: torch.Tensor ) -> torch.Tensor:
# just take the relevant feature maps
_A = encoder_hidden_states[self.in_index]
_A = self.convs(__A )
if self.concat_input:
_A = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
_A = self.classifier(__A )
return output
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = UperNetConfig
A_ = "pixel_values"
A_ = True
def __A ( self: List[Any] , __A: Optional[int] ) -> str:
if isinstance(__A , __A ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def __A ( self: Any ) -> int:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def __A ( self: int , __A: Tuple , __A: Dict=False ) -> Optional[int]:
if isinstance(__A , __A ):
_A = value
__A = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): 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'
__A = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for 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(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , __A: str ) -> List[Any]:
super().__init__(__A )
_A = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
_A = UperNetHead(__A , in_channels=self.backbone.channels )
_A = UperNetFCNHead(__A ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=__A , config_class=_CONFIG_FOR_DOC )
def __A ( self: List[Any] , __A: Optional[torch.Tensor] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , __A: Optional[torch.Tensor] = None , __A: Optional[bool] = None , ) -> Union[tuple, SemanticSegmenterOutput]:
_A = return_dict if return_dict is not None else self.config.use_return_dict
_A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_A = output_attentions if output_attentions is not None else self.config.output_attentions
_A = self.backbone.forward_with_filtered_kwargs(
__A , output_hidden_states=__A , output_attentions=__A )
_A = outputs.feature_maps
_A = self.decode_head(__A )
_A = nn.functional.interpolate(__A , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__A )
_A = None
if self.auxiliary_head is not None:
_A = self.auxiliary_head(__A )
_A = nn.functional.interpolate(
__A , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__A )
_A = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
_A = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
_A = loss_fct(__A , __A )
_A = loss_fct(__A , __A )
_A = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
_A = (logits,) + outputs[1:]
else:
_A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=__A , logits=__A , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A = datasets.load_iris()
__A = np.array(data['data'])
__A = np.array(data['target'])
__A = data['target_names']
__A , __A , __A , __A = train_test_split(X, y)
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return np.linalg.norm(np.array(_lowercase ) - np.array(_lowercase ) )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=5 ):
'''simple docstring'''
_A = zip(_lowercase , _lowercase )
# List of distances of all points from the point to be classified
_A = []
for data_point in data:
_A = euclidean_distance(data_point[0] , _lowercase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
_A = [i[1] for i in sorted(_lowercase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
_A = Counter(_lowercase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = 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 __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
from __future__ import annotations
__A = 1.6021e-19 # units = C
def __A ( _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __A ( _lowercase ):
'''simple docstring'''
_A = os.path.join(args.tf_model_dir , '''parameters.json''' )
_A = json.loads(open(_lowercase ).read() )
if not params:
raise ValueError(
f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith('''.pt''' ):
_A = args.output + '''.pt'''
_A = OrderedDict()
with tf.device('''/CPU:0''' ):
_A = tf.train.load_checkpoint(args.tf_model_dir )
_A = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_A = reader.get_tensor(_lowercase ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
_A = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
_A = 8
_A = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/moe''' ):
_A = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
_A = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/softmlp/kernel''' ):
_A = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
_A = key_name[-9:-7]
for i in range(16 ):
_A = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
_A = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/mlp''' ):
_A = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
_A = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/p1/bias''' ):
_A = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/p2/kernel''' ):
_A = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/p2/bias''' ):
_A = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/ln''' ):
_A = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
_A = '''model.blocks.%d.feed_forward.norm.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/g''' ):
_A = '''model.blocks.%d.feed_forward.norm.weight''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/att''' ):
_A = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
_A = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_A = state[:, 0, :, :]
_A = state[:, 1, :, :]
_A = state[:, 2, :, :]
_A = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
_A = torch.tensor(_lowercase )
_A = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
_A = torch.tensor(_lowercase )
_A = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/o/kernel''' ):
_A = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
_A = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/an''' ):
_A = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
_A = '''model.blocks.%d.self_attn.norm.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif key_name.endswith('''/g''' ):
_A = '''model.blocks.%d.self_attn.norm.weight''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
_A = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
_A = '''model.%s.weight''' % nlayer
_A = vnp.copy() # same in embedded
_A = torch.tensor(_lowercase )
if key_name.startswith('''model/wte''' ):
_A = '''lm_head.weight'''
_A = vnp.copy() # same in embedded
_A = torch.tensor(_lowercase )
elif key_name.startswith('''model/wob''' ):
_A = '''final_logits_bias'''
_A = vnp.copy() # same in embedded
_A = state.reshape((1, -1) )
_A = torch.tensor(_lowercase )
elif key_name == "model/dense/kernel":
_A = '''model.last_project.weight'''
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(_lowercase )
elif key_name == "model/dense_1/bias":
_A = '''model.last_project.bias'''
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(_lowercase )
torch.save(_lowercase , args.output )
if __name__ == "__main__":
__A = argparse.ArgumentParser(
description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
__A = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
def __A ( _lowercase = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
_A = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
_A = 1
_A = 2
while i * i <= n:
while n % i == 0:
_A = i
n //= i
i += 1
if n > 1:
_A = n
return int(_lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__A = trt.Logger(trt.Logger.WARNING)
__A = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__A = logging.getLogger(__name__)
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__A = parser.parse_args()
if args.tokenizer_name:
__A = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__A = args.per_device_eval_batch_size
__A = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__A = True
__A = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__A = 'temp_engine/bert-fp16.engine'
if args.inta:
__A = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__A = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__A = [network.get_input(i) for i in range(network.num_inputs)]
__A = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__A = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__A = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__A = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
_A = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
_A = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , _lowercase )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , _lowercase )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , _lowercase )
# start time
_A = time.time()
# Run inference
context.execute_async(
bindings=[int(_lowercase ) for d_inp in d_inputs] + [int(_lowercase ), int(_lowercase )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(_lowercase , _lowercase , _lowercase )
cuda.memcpy_dtoh_async(_lowercase , _lowercase , _lowercase )
# Synchronize the stream and take time
stream.synchronize()
# end time
_A = time.time()
_A = end_time - start_time
_A = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__A = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__A = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__A = raw_datasets['validation'].column_names
__A = 'question' if 'question' in column_names else column_names[0]
__A = 'context' if 'context' in column_names else column_names[1]
__A = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__A = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
__A = min(args.max_seq_length, tokenizer.model_max_length)
def __A ( _lowercase ):
'''simple docstring'''
_A = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
_A = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=_lowercase , stride=args.doc_stride , return_overflowing_tokens=_lowercase , return_offsets_mapping=_lowercase , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
_A = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
_A = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
_A = tokenized_examples.sequence_ids(_lowercase )
_A = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
_A = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
_A = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
__A = raw_datasets['validation']
# Validation Feature Creation
__A = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__A = default_data_collator
__A = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__A = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def __A ( _lowercase , _lowercase , _lowercase , _lowercase="eval" ):
'''simple docstring'''
_A = postprocess_qa_predictions(
examples=_lowercase , features=_lowercase , predictions=_lowercase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=_lowercase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
_A = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
_A = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
_A = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=_lowercase , label_ids=_lowercase )
__A = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def __A ( _lowercase ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(_lowercase ) ) * engine.get_binding_dtype(_lowercase ).itemsize
# Allocate device memory for inputs and outputs.
__A = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__A = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__A = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__A = cuda.mem_alloc(h_outputa.nbytes)
__A = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__A = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
__A = 0.0
__A = 0
__A = timeit.default_timer()
__A = None
for step, batch in enumerate(eval_dataloader):
__A , __A = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__A , __A = outputs
__A = torch.tensor(start_logits)
__A = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__A = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__A = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__A = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__A = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__A = nested_truncate(all_preds, len(eval_dataset))
__A = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1000))
logger.info('Total Number of Inference = %d', niter)
__A = post_processing_function(eval_examples, eval_dataset, all_preds)
__A = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
__A = re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
__A = 10
__A = 256
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) < MIN_NUM_TOKENS:
return None
_A = MinHash(num_perm=_lowercase )
for token in set(_lowercase ):
min_hash.update(token.encode() )
return min_hash
def __A ( _lowercase ):
'''simple docstring'''
return {t for t in NON_ALPHA.split(_lowercase ) if len(t.strip() ) > 0}
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[str] , *,
__A: float = 0.85 , ) -> str:
_A = duplication_jaccard_threshold
_A = NUM_PERM
_A = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_A = defaultdict(__A )
def __A ( self: Union[str, Any] , __A: Tuple , __A: MinHash ) -> None:
_A = self._index.query(__A )
if code_key in self._index.keys:
print(f"""Duplicate key {code_key}""" )
return
self._index.insert(__A , __A )
if len(__A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__A )
def __A ( self: Optional[int] ) -> List[List[Dict]]:
_A = []
for base, duplicates in self._duplicate_clusters.items():
_A = [base] + list(__A )
# reformat the cluster to be a list of dict
_A = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__A )
return duplicate_clusters
def __A ( self: Dict , __A: Any ) -> None:
_A = self.get_duplicate_clusters()
with open(__A , '''w''' ) as f:
json.dump(__A , __A )
def __A ( _lowercase ):
'''simple docstring'''
_A ,_A = element
_A = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def __A ( _lowercase ):
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(_lowercase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = DuplicationIndex(duplication_jaccard_threshold=_lowercase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_lowercase ) ) , max_queue_size=1_00 ) ):
di.add(_lowercase , _lowercase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = get_tokens(_lowercase )
_A = get_tokens(_lowercase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
__A = None
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = []
for elementa in cluster:
_A = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_A = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(_lowercase , _lowercase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_A = 1
extremes.append(_lowercase )
return extremes
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global _shared_dataset
_A = dataset
_A = []
_A = partial(_find_cluster_extremes_shared , jaccard_threshold=_lowercase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
_lowercase , _lowercase , ) , total=len(_lowercase ) , ):
extremes_list.append(_lowercase )
return extremes_list
def __A ( _lowercase , _lowercase = 0.85 ):
'''simple docstring'''
_A = make_duplicate_clusters(_lowercase , _lowercase )
_A = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_A = {}
_A = find_extremes(_lowercase , _lowercase , _lowercase )
for extremes in extremes_clusters:
for element in extremes:
_A = element
_A = duplicate_indices - set(extreme_dict.keys() )
_A = dataset.filter(lambda _lowercase , _lowercase : idx not in remove_indices , with_indices=_lowercase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_A = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_A = extreme_dict[element['''base_index''']]['''copies''']
print(f"""Original dataset size: {len(_lowercase )}""" )
print(f"""Number of duplicate clusters: {len(_lowercase )}""" )
print(f"""Files in duplicate cluster: {len(_lowercase )}""" )
print(f"""Unique files in duplicate cluster: {len(_lowercase )}""" )
print(f"""Filtered dataset size: {len(_lowercase )}""" )
return ds_filter, duplicate_clusters
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , _lowercase , _lowercase , _lowercase )
move_disk(_lowercase , _lowercase )
move_tower(height - 1 , _lowercase , _lowercase , _lowercase )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
print('''moving disk from''' , _lowercase , '''to''' , _lowercase )
def __A ( ):
'''simple docstring'''
_A = int(input('''Height of hanoi: ''' ).strip() )
move_tower(_lowercase , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main()
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = LayoutLMTokenizer
A_ = LayoutLMTokenizerFast
A_ = True
A_ = True
def __A ( self: Any ) -> List[str]:
super().setUp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __A ( self: str , **__A: Any ) -> Optional[Any]:
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: Dict , __A: Optional[int] ) -> List[str]:
_A = '''UNwant\u00E9d,running'''
_A = '''unwanted, running'''
return input_text, output_text
def __A ( self: Tuple ) -> Optional[Any]:
_A = self.tokenizer_class(self.vocab_file )
_A = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [7, 4, 5, 10, 8, 9] )
def __A ( self: Dict ) -> List[Any]:
pass
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = 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 __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__A = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
__A = {'allegro/herbert-base-cased': 514}
__A = {}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_INIT_CONFIGURATION
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = HerbertTokenizer
def __init__( self: List[str] , __A: Dict=None , __A: List[Any]=None , __A: Dict=None , __A: Tuple="<s>" , __A: Optional[int]="<unk>" , __A: Dict="<pad>" , __A: Optional[Any]="<mask>" , __A: Optional[Any]="</s>" , **__A: Optional[int] , ) -> str:
super().__init__(
__A , __A , tokenizer_file=__A , cls_token=__A , unk_token=__A , pad_token=__A , mask_token=__A , sep_token=__A , **__A , )
def __A ( self: List[Any] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.cls_token_id]
_A = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __A ( self: Optional[Any] , __A: List[int] , __A: Optional[List[int]] = None , __A: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
if token_ids_a is None:
return [1] + ([0] * len(__A )) + [1]
return [1] + ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1]
def __A ( self: Union[str, Any] , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self: str , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
_A = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__A = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ErnieForCausalLM',
'ErnieForMaskedLM',
'ErnieForMultipleChoice',
'ErnieForNextSentencePrediction',
'ErnieForPreTraining',
'ErnieForQuestionAnswering',
'ErnieForSequenceClassification',
'ErnieForTokenClassification',
'ErnieModel',
'ErniePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: str ) -> Optional[int]:
_A = tf.convert_to_tensor(
[
[
8.2_220_991, # 3rd highest value; idx. 0
-0.5_620_044,
5.23_229_752,
4.0_386_393,
-6.8_798_378,
-0.54_785_802,
-3.2_012_153,
2.92_777_176,
1.88_171_953,
7.35_341_276, # 5th highest value; idx. 9
8.43_207_833, # 2nd highest value; idx. 10
-9.85_711_836,
-5.96_209_236,
-1.13_039_161,
-7.1_115_294,
-0.8_369_633,
-5.3_186_408,
7.06_427_407,
0.81_369_344,
-0.82_023_817,
-5.9_179_796,
0.58_813_443,
-6.99_778_438,
4.71_551_189,
-0.18_771_637,
7.44_020_759, # 4th highest value; idx. 25
9.38_450_987, # 1st highest value; idx. 26
2.12_662_941,
-9.32_562_038,
2.35_652_522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58_425_518,
4.53_139_238,
-5.57_510_464,
-6.28_030_699,
-7.19_529_503,
-4.02_122_551,
1.39_337_037,
-6.06_707_057,
1.59_480_517,
-9.643_119,
0.03_907_799,
0.67_231_762,
-8.88_206_726,
6.27_115_922, # 4th highest value; idx. 13
2.28_520_723,
4.82_767_506,
4.30_421_368,
8.8_275_313, # 2nd highest value; idx. 17
5.44_029_958, # 5th highest value; idx. 18
-4.4_735_794,
7.38_579_536, # 3rd highest value; idx. 20
-2.91_051_663,
2.61_946_077,
-2.5_674_762,
-9.48_959_302,
-4.02_922_645,
-1.35_416_918,
9.67_702_323, # 1st highest value; idx. 27
-5.89_478_553,
1.85_370_467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
_A = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
_A = tf.convert_to_tensor(
[8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023] , dtype=tf.floataa , ) # expected non filtered values as noted above
_A = tf_top_k_top_p_filtering(__A , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
_A = output[output != -float('''inf''' )]
_A = tf.cast(
tf.where(tf.not_equal(__A , tf.constant(-float('''inf''' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(__A , __A , rtol=1e-12 )
tf.debugging.assert_equal(__A , __A )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , snake_case ):
"""simple docstring"""
if is_tf_available():
A_ = {
"AutoModelForCausalLM": TFAutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeqaSeq,
"AutoModelForSeq2SeqLM": TFAutoModelForSeqaSeqLM,
"AutoModelForVision2Seq": TFAutoModelForVisionaSeq,
"LogitsProcessorList": TFLogitsProcessorList,
"MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
"create_tensor_fn": tf.convert_to_tensor,
"floats_tensor": floats_tensor,
"return_tensors": "tf",
}
@slow
def __A ( self: int ) -> List[Any]:
# TF-only test: tf.saved_model export
_A = TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_A = 2
_A = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self: Tuple , __A: Optional[int] ) -> Dict:
super(__A , self ).__init__()
_A = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='''input_ids''' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='''attention_mask''' ),
) , jit_compile=__A , )
def __A ( self: Optional[Any] , __A: str , __A: int ) -> Optional[int]:
_A = self.model.generate(
input_ids=__A , attention_mask=__A , max_new_tokens=__A , return_dict_in_generate=__A , )
return {"sequences": outputs["sequences"]}
_A = [[2, 0], [1_02, 1_03]]
_A = [[1, 0], [1, 1]]
_A = DummyModel(model=__A )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__A , __A , signatures={'''serving_default''': dummy_model.serving} )
_A = tf.saved_model.load(__A ).signatures['''serving_default''']
for batch_size in range(1 , len(__A ) + 1 ):
_A = {
'''input_ids''': tf.constant(dummy_input_ids[:batch_size] ),
'''attention_mask''': tf.constant(dummy_attention_masks[:batch_size] ),
}
_A = serving_func(**__A )['''sequences''']
_A = test_model.generate(**__A , max_new_tokens=__A )
tf.debugging.assert_equal(__A , __A )
@slow
def __A ( self: List[Any] ) -> List[Any]:
# TF-only test: tf.saved_model export
_A = TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_A = 1
_A = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self: Tuple , __A: Union[str, Any] ) -> str:
super(__A , self ).__init__()
_A = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='''input_ids''' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='''attention_mask''' ),
) , jit_compile=__A , )
def __A ( self: int , __A: Dict , __A: Any ) -> str:
_A = self.model.generate(
input_ids=__A , attention_mask=__A , max_new_tokens=__A , return_dict_in_generate=__A , )
return {"sequences": outputs["sequences"]}
_A = [[2], [1_02, 1_03]]
_A = [[1], [1, 1]]
_A = DummyModel(model=__A )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__A , __A , signatures={'''serving_default''': dummy_model.serving} )
_A = tf.saved_model.load(__A ).signatures['''serving_default''']
for input_row in range(len(__A ) ):
_A = {
'''input_ids''': tf.constant([dummy_input_ids[input_row]] ),
'''attention_mask''': tf.constant([dummy_attention_masks[input_row]] ),
}
_A = serving_func(**__A )['''sequences''']
_A = test_model.generate(**__A , max_new_tokens=__A )
tf.debugging.assert_equal(__A , __A )
@slow
@require_tensorflow_text
def __A ( self: Optional[Any] ) -> int:
# TF-only test: tf.saved_model export
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='''google/flan-t5-small''' , filename='''spiece.model''' , local_dir=__A )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self: int ) -> int:
super().__init__()
_A = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__A , '''spiece.model''' ) , '''rb''' ).read() )
_A = TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
def __A ( self: Dict , __A: Dict , *__A: Any , **__A: int ) -> Any:
_A = self.tokenizer.tokenize(__A )
_A ,_A = text.pad_model_inputs(
__A , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
_A = self.model.generate(input_ids=__A , attention_mask=__A )
return self.tokenizer.detokenize(__A )
_A = CompleteSentenceTransformer()
_A = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='''inputs''' )
_A = complete_model(__A )
_A = tf.keras.Model(__A , __A )
keras_model.save(__A )
def __A ( self: Dict ) -> List[Any]:
# Has PT equivalent: this test relies on random sampling
_A = {
'''do_sample''': True,
'''num_beams''': 1,
'''top_p''': 0.7,
'''top_k''': 10,
'''temperature''': 0.7,
}
_A = 14
_A = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_A = '''Hello, my dog is cute and'''
_A = tokenizer(__A , return_tensors='''tf''' )
_A = TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_A = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(''':/CPU:0''' ):
tf.random.set_seed(0 )
_A = model.generate(**__A , eos_token_id=__A , **__A )
self.assertTrue(expectation == len(generated_tokens[0] ) )
_A = [6_38, 1_98]
with tf.device(''':/CPU:0''' ):
tf.random.set_seed(0 )
_A = model.generate(**__A , eos_token_id=__A , **__A )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __A ( self: str ) -> str:
# Has PT equivalent: ample use of framework-specific code
_A = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart''' )
_A = '''Hugging Face is a technology company based in New York and Paris.'''
_A = bart_tokenizer(__A , return_tensors='''tf''' ).input_ids
_A = TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart''' )
_A = bart_model.generate(__A ).numpy()
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Any=None , **__A: Optional[int] ) -> Optional[Any]:
return super().call(__A , **__A )
_A = FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart''' )
_A = bart_model.generate(__A , foo='''bar''' ).numpy()
self.assertTrue(np.array_equal(__A , __A ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def __A ( self: List[Any] , __A: Optional[int] , **__A: Union[str, Any] ) -> List[str]:
return super().call(__A , **__A )
_A = FakeEncoder(bart_model.config , bart_model.model.shared )
_A = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
_A = bart_model.generate(__A ).numpy()
with self.assertRaises(__A ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__A , foo='''bar''' )
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = FunnelTokenizer
A_ = FunnelTokenizerFast
A_ = True
A_ = True
def __A ( self: Tuple ) -> int:
super().setUp()
_A = [
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __A ( self: List[str] , **__A: Optional[Any] ) -> int:
return FunnelTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: List[Any] , **__A: List[Any] ) -> str:
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **__A )
def __A ( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
_A = '''UNwant\u00E9d,running'''
_A = '''unwanted, running'''
return input_text, output_text
def __A ( self: Dict ) -> Optional[int]:
_A = self.tokenizer_class(self.vocab_file )
_A = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [7, 4, 5, 10, 8, 9] )
def __A ( self: str ) -> Dict:
_A = self.get_tokenizers(do_lower_case=__A )
for tokenizer in tokenizers:
_A = tokenizer('''UNwant\u00E9d,running''' )
_A = len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len )
_A = tokenizer('''UNwant\u00E9d,running''' , '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len + [1] * sentence_len )
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, 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_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: Any , __A: Union[str, Any]=13 , __A: Union[str, Any]=30 , __A: int=2 , __A: Optional[Any]=3 , __A: Any=True , __A: Any=True , __A: Any=32 , __A: Tuple=5 , __A: int=4 , __A: Optional[Any]=37 , __A: Optional[int]="gelu" , __A: str=0.1 , __A: Any=0.1 , __A: Optional[int]=10 , __A: Any=0.02 , __A: Any=3 , __A: Union[str, Any]=None , __A: int=2 , ) -> str:
_A = parent
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = is_training
_A = use_labels
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = type_sequence_label_size
_A = initializer_range
_A = scope
_A = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
_A = (image_size // patch_size) ** 2
_A = num_patches + 2
def __A ( self: Dict ) -> Optional[Any]:
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = self.get_config()
return config, pixel_values, labels
def __A ( self: Optional[int] ) -> Dict:
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__A , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __A ( self: int , __A: Union[str, Any] , __A: int , __A: Any ) -> List[Any]:
_A = DeiTModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self: Optional[Any] , __A: Any , __A: int , __A: List[str] ) -> Any:
_A = DeiTForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_A = model(__A )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_A = 1
_A = DeiTForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(__A )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __A ( self: Dict , __A: Optional[int] , __A: Dict , __A: str ) -> Union[str, Any]:
_A = self.type_sequence_label_size
_A = DeiTForImageClassification(__A )
model.to(__A )
model.eval()
_A = model(__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_A = 1
_A = DeiTForImageClassification(__A )
model.to(__A )
model.eval()
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self: str ) -> str:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": DeiTModel,
"image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
def __A ( self: List[Any] ) -> Optional[int]:
_A = DeiTModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A , hidden_size=37 )
def __A ( self: Tuple ) -> Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason='''DeiT does not use inputs_embeds''' )
def __A ( self: Optional[int] ) -> Union[str, Any]:
pass
def __A ( self: Tuple ) -> str:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A , nn.Linear ) )
def __A ( self: List[str] ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
def __A ( self: Dict ) -> Dict:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __A ( self: Optional[int] ) -> Tuple:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def __A ( self: Dict ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __A ( self: Tuple , __A: Optional[Any] , __A: Optional[int] , __A: Tuple=False ) -> List[Any]:
_A = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __A ( self: Tuple ) -> Optional[int]:
if not self.model_tester.is_training:
return
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__A )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
_A = model_class(__A )
model.to(__A )
model.train()
_A = self._prepare_for_class(__A , __A , return_labels=__A )
_A = model(**__A ).loss
loss.backward()
def __A ( self: Optional[Any] ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
_A = False
_A = True
for model_class in self.all_model_classes:
if model_class in get_values(__A ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
_A = model_class(__A )
model.gradient_checkpointing_enable()
model.to(__A )
model.train()
_A = self._prepare_for_class(__A , __A , return_labels=__A )
_A = model(**__A ).loss
loss.backward()
def __A ( self: List[Any] ) -> str:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = [
{'''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 ),
*get_values(__A ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
_A = problem_type['''title''']
_A = problem_type['''num_labels''']
_A = model_class(__A )
model.to(__A )
model.train()
_A = self._prepare_for_class(__A , __A , return_labels=__A )
if problem_type["num_labels"] > 1:
_A = inputs['''labels'''].unsqueeze(1 ).repeat(1 , problem_type['''num_labels'''] )
_A = 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 = 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 __A ( self: Optional[Any] ) -> Union[str, Any]:
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = DeiTModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Any ) -> Optional[int]:
return (
DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
if is_vision_available()
else None
)
@slow
def __A ( self: Dict ) -> Any:
_A = DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' ).to(
__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=__A , return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
_A = model(**__A )
# verify the logits
_A = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __A )
_A = torch.tensor([-1.0_266, 0.1_912, -1.2_861] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __A , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __A ( self: Optional[int] ) -> List[Any]:
_A = DeiTModel.from_pretrained(
'''facebook/deit-base-distilled-patch16-224''' , torch_dtype=torch.floataa , device_map='''auto''' )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=__A , return_tensors='''pt''' )
_A = inputs.pixel_values.to(__A )
# forward pass to make sure inference works in fp16
with torch.no_grad():
_A = model(__A )
| 62 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "altclip_text_model"
def __init__( self: List[str] , __A: Union[str, Any]=25_00_02 , __A: List[str]=10_24 , __A: int=24 , __A: int=16 , __A: Dict=40_96 , __A: List[Any]="gelu" , __A: List[str]=0.1 , __A: List[str]=0.1 , __A: int=5_14 , __A: Optional[Any]=1 , __A: int=0.02 , __A: Tuple=0.02 , __A: Optional[int]=1e-05 , __A: Any=1 , __A: Optional[int]=0 , __A: Union[str, Any]=2 , __A: Dict="absolute" , __A: Any=True , __A: Tuple=7_68 , **__A: Optional[int] , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = initializer_factor
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = project_dim
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "altclip_vision_model"
def __init__( self: Any , __A: Union[str, Any]=7_68 , __A: List[Any]=30_72 , __A: str=5_12 , __A: Optional[int]=12 , __A: Optional[int]=12 , __A: Optional[Any]=3 , __A: Tuple=2_24 , __A: Dict=32 , __A: Tuple="quick_gelu" , __A: List[str]=1e-5 , __A: Tuple=0.0 , __A: Tuple=0.02 , __A: str=1.0 , **__A: str , ) -> int:
super().__init__(**__A )
_A = hidden_size
_A = intermediate_size
_A = projection_dim
_A = num_hidden_layers
_A = num_attention_heads
_A = num_channels
_A = patch_size
_A = image_size
_A = initializer_range
_A = initializer_factor
_A = attention_dropout
_A = layer_norm_eps
_A = hidden_act
@classmethod
def __A ( cls: List[str] , __A: Union[str, os.PathLike] , **__A: Optional[int] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_A ,_A = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get('''model_type''' ) == "altclip":
_A = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "altclip"
A_ = True
def __init__( self: Optional[int] , __A: Tuple=None , __A: Union[str, Any]=None , __A: Dict=7_68 , __A: Optional[Any]=2.6_592 , **__A: Dict ) -> Optional[int]:
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
_A = kwargs.pop('''text_config_dict''' , __A )
_A = kwargs.pop('''vision_config_dict''' , __A )
super().__init__(**__A )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
_A = {}
# This is the complete result when using `text_config_dict`.
_A = AltCLIPTextConfig(**__A ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
_A = (
f"""`{key}` is found in both `text_config_dict` and `text_config` but with different values. """
f"""The value `text_config_dict[\"{key}\"]` will be used instead."""
)
# If inferred from default argument values (just to be super careful)
else:
_A = (
f"""`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The """
f"""value `text_config[\"{key}\"]` will be overriden."""
)
logger.warning(__A )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
_A = {}
# This is the complete result when using `vision_config_dict`.
_A = AltCLIPVisionConfig(**__A ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
_A = {
str(__A ): value for key, value in _vision_config_dict['''id2label'''].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
_A = (
f"""`{key}` is found in both `vision_config_dict` and `vision_config` but with different """
f"""values. The value `vision_config_dict[\"{key}\"]` will be used instead."""
)
# If inferred from default argument values (just to be super careful)
else:
_A = (
f"""`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. """
f"""The value `vision_config[\"{key}\"]` will be overriden."""
)
logger.warning(__A )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
_A = {}
logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''' )
if vision_config is None:
_A = {}
logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''' )
_A = AltCLIPTextConfig(**__A )
_A = AltCLIPVisionConfig(**__A )
_A = projection_dim
_A = logit_scale_init_value
_A = 1.0
@classmethod
def __A ( cls: str , __A: AltCLIPTextConfig , __A: AltCLIPVisionConfig , **__A: Dict ) -> str:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def __A ( self: str ) -> Dict:
_A = copy.deepcopy(self.__dict__ )
_A = self.text_config.to_dict()
_A = self.vision_config.to_dict()
_A = self.__class__.model_type
return output
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "deta"
A_ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self: int , __A: List[str]=None , __A: Optional[int]=9_00 , __A: Dict=20_48 , __A: Union[str, Any]=6 , __A: Union[str, Any]=20_48 , __A: Tuple=8 , __A: Optional[int]=6 , __A: str=10_24 , __A: Optional[Any]=8 , __A: Union[str, Any]=0.0 , __A: Optional[Any]=True , __A: List[Any]="relu" , __A: List[str]=2_56 , __A: Any=0.1 , __A: str=0.0 , __A: List[str]=0.0 , __A: List[Any]=0.02 , __A: List[str]=1.0 , __A: Any=True , __A: Dict=False , __A: List[str]="sine" , __A: Optional[Any]=5 , __A: Union[str, Any]=4 , __A: List[str]=4 , __A: Optional[Any]=True , __A: List[str]=3_00 , __A: List[Any]=True , __A: str=True , __A: Optional[Any]=1 , __A: Dict=5 , __A: int=2 , __A: List[Any]=1 , __A: Any=1 , __A: Tuple=5 , __A: List[Any]=2 , __A: Any=0.1 , __A: Any=0.25 , **__A: int , ) -> Any:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
_A = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(__A , __A ):
_A = backbone_config.pop('''model_type''' )
_A = CONFIG_MAPPING[backbone_model_type]
_A = config_class.from_dict(__A )
_A = backbone_config
_A = num_queries
_A = max_position_embeddings
_A = d_model
_A = encoder_ffn_dim
_A = encoder_layers
_A = encoder_attention_heads
_A = decoder_ffn_dim
_A = decoder_layers
_A = decoder_attention_heads
_A = dropout
_A = attention_dropout
_A = activation_dropout
_A = activation_function
_A = init_std
_A = init_xavier_std
_A = encoder_layerdrop
_A = auxiliary_loss
_A = position_embedding_type
# deformable attributes
_A = num_feature_levels
_A = encoder_n_points
_A = decoder_n_points
_A = two_stage
_A = two_stage_num_proposals
_A = with_box_refine
_A = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
_A = class_cost
_A = bbox_cost
_A = giou_cost
# Loss coefficients
_A = mask_loss_coefficient
_A = dice_loss_coefficient
_A = bbox_loss_coefficient
_A = giou_loss_coefficient
_A = eos_coefficient
_A = focal_alpha
super().__init__(is_encoder_decoder=__A , **__A )
@property
def __A ( self: List[Any] ) -> int:
return self.encoder_attention_heads
@property
def __A ( self: Optional[Any] ) -> int:
return self.d_model
def __A ( self: Union[str, Any] ) -> Optional[int]:
_A = copy.deepcopy(self.__dict__ )
_A = self.backbone_config.to_dict()
_A = self.__class__.model_type
return output
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__A = (
'https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'
)
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
def __A ( ):
'''simple docstring'''
_A = '''https://pypi.org/pypi/diffusers/json'''
_A = json.loads(request.urlopen(_lowercase ).read() )['''releases'''].keys()
return sorted(_lowercase , key=lambda _lowercase : version.Version(_lowercase ) )
def __A ( ):
'''simple docstring'''
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(_lowercase )
os.makedirs(_lowercase , exist_ok=_lowercase )
_A = Path(_lowercase ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def __A ( _lowercase ):
'''simple docstring'''
init_hf_modules()
_A = Path(_lowercase ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(_lowercase , exist_ok=_lowercase )
_A = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def __A ( _lowercase ):
'''simple docstring'''
with open(_lowercase , '''r''' , encoding='''utf-8''' ) as f:
_A = f.read()
# Imports of the form `import .xxx`
_A = re.findall('''^\s*import\s+\.(\S+)\s*$''' , _lowercase , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , _lowercase , flags=re.MULTILINE )
# Unique-ify
return list(set(_lowercase ) )
def __A ( _lowercase ):
'''simple docstring'''
_A = False
_A = [module_file]
_A = []
# Let's recurse through all relative imports
while not no_change:
_A = []
for f in files_to_check:
new_imports.extend(get_relative_imports(_lowercase ) )
_A = Path(_lowercase ).parent
_A = [str(module_path / m ) for m in new_imports]
_A = [f for f in new_import_files if f not in all_relative_imports]
_A = [f"""{f}.py""" for f in new_import_files]
_A = len(_lowercase ) == 0
all_relative_imports.extend(_lowercase )
return all_relative_imports
def __A ( _lowercase ):
'''simple docstring'''
with open(_lowercase , '''r''' , encoding='''utf-8''' ) as f:
_A = f.read()
# Imports of the form `import xxx`
_A = re.findall('''^\s*import\s+(\S+)\s*$''' , _lowercase , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , _lowercase , flags=re.MULTILINE )
# Only keep the top-level module
_A = [imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
_A = list(set(_lowercase ) )
_A = []
for imp in imports:
try:
importlib.import_module(_lowercase )
except ImportError:
missing_packages.append(_lowercase )
if len(_lowercase ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
f"""{", ".join(_lowercase )}. Run `pip install {" ".join(_lowercase )}`""" )
return get_relative_imports(_lowercase )
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = module_path.replace(os.path.sep , '''.''' )
_A = importlib.import_module(_lowercase )
if class_name is None:
return find_pipeline_class(_lowercase )
return getattr(_lowercase , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
from ..pipelines import DiffusionPipeline
_A = dict(inspect.getmembers(_lowercase , inspect.isclass ) )
_A = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , _lowercase )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
f""" {loaded_module}.""" )
_A = cls
return pipeline_class
def __A ( _lowercase , _lowercase , _lowercase = None , _lowercase = False , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
_A = str(_lowercase )
_A = os.path.join(_lowercase , _lowercase )
if os.path.isfile(_lowercase ):
_A = module_file_or_url
_A = '''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
_A = get_diffusers_versions()
# cut ".dev0"
_A = '''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
_A = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(f"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
_A = f"""v{revision}"""
elif revision == "main":
_A = revision
else:
raise ValueError(
f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
f""" {", ".join(available_versions + ["main"] )}.""" )
# community pipeline on GitHub
_A = COMMUNITY_PIPELINES_URL.format(revision=_lowercase , pipeline=_lowercase )
try:
_A = cached_download(
_lowercase , cache_dir=_lowercase , force_download=_lowercase , proxies=_lowercase , resume_download=_lowercase , local_files_only=_lowercase , use_auth_token=_lowercase , )
_A = '''git'''
_A = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
_A = hf_hub_download(
_lowercase , _lowercase , cache_dir=_lowercase , force_download=_lowercase , proxies=_lowercase , resume_download=_lowercase , local_files_only=_lowercase , use_auth_token=_lowercase , )
_A = os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
_A = check_imports(_lowercase )
# Now we move the module inside our cached dynamic modules.
_A = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(_lowercase )
_A = Path(_lowercase ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(_lowercase , submodule_path / module_file )
for module_needed in modules_needed:
_A = f"""{module_needed}.py"""
shutil.copy(os.path.join(_lowercase , _lowercase ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(_lowercase , _lowercase ):
_A = use_auth_token
elif use_auth_token is True:
_A = HfFolder.get_token()
else:
_A = None
_A = model_info(_lowercase , revision=_lowercase , token=_lowercase ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
_A = submodule_path / commit_hash
_A = full_submodule + os.path.sep + commit_hash
create_dynamic_module(_lowercase )
if not (submodule_path / module_file).exists():
shutil.copy(_lowercase , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
_lowercase , f"""{module_needed}.py""" , cache_dir=_lowercase , force_download=_lowercase , resume_download=_lowercase , proxies=_lowercase , use_auth_token=_lowercase , revision=_lowercase , local_files_only=_lowercase , )
return os.path.join(_lowercase , _lowercase )
def __A ( _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = False , **_lowercase , ):
'''simple docstring'''
_A = get_cached_module_file(
_lowercase , _lowercase , cache_dir=_lowercase , force_download=_lowercase , resume_download=_lowercase , proxies=_lowercase , use_auth_token=_lowercase , revision=_lowercase , local_files_only=_lowercase , )
return get_class_in_module(_lowercase , final_module.replace('''.py''' , '''''' ) )
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__A = get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Dict , __A: Optional[str] = None ) -> List[Any]:
_A = (
os.path.join(__A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_A = Extractor
def __A ( self: Union[str, Any] , __A: str ) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_A = os.path.abspath(__A )
return os.path.join(self.extract_dir , hash_url_to_filename(__A ) )
def __A ( self: str , __A: str , __A: bool ) -> bool:
return force_extract or (
not os.path.isfile(__A ) and not (os.path.isdir(__A ) and os.listdir(__A ))
)
def __A ( self: List[str] , __A: str , __A: bool = False ) -> str:
_A = self.extractor.infer_extractor_format(__A )
if not extractor_format:
return input_path
_A = self._get_output_path(__A )
if self._do_extract(__A , __A ):
self.extractor.extract(__A , __A , __A )
return output_path
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@classmethod
@abstractmethod
def __A ( cls: str , __A: Union[Path, str] , **__A: Any ) -> bool:
...
@staticmethod
@abstractmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
...
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = []
@staticmethod
def __A ( __A: Union[Path, str] , __A: int ) -> List[Any]:
with open(__A , '''rb''' ) as f:
return f.read(__A )
@classmethod
def __A ( cls: Any , __A: Union[Path, str] , __A: bytes = b"" ) -> bool:
if not magic_number:
_A = max(len(__A ) for cls_magic_number in cls.magic_numbers )
try:
_A = cls.read_magic_number(__A , __A )
except OSError:
return False
return any(magic_number.startswith(__A ) for cls_magic_number in cls.magic_numbers )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@classmethod
def __A ( cls: Union[str, Any] , __A: Union[Path, str] , **__A: str ) -> bool:
return tarfile.is_tarfile(__A )
@staticmethod
def __A ( __A: Optional[Any] , __A: Any ) -> str:
def resolved(__A: str ) -> str:
return os.path.realpath(os.path.abspath(__A ) )
def badpath(__A: str , __A: str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__A , __A ) ).startswith(__A )
def badlink(__A: Optional[int] , __A: str ) -> bool:
# Links are interpreted relative to the directory containing the link
_A = resolved(os.path.join(__A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=__A )
_A = resolved(__A )
for finfo in members:
if badpath(finfo.name , __A ):
logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(__A , __A ):
logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(__A , __A ):
logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
os.makedirs(__A , exist_ok=__A )
_A = tarfile.open(__A )
tar_file.extractall(__A , members=TarExtractor.safemembers(__A , __A ) )
tar_file.close()
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\x1F\x8B"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
with gzip.open(__A , '''rb''' ) as gzip_file:
with open(__A , '''wb''' ) as extracted_file:
shutil.copyfileobj(__A , __A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [
B"PK\x03\x04",
B"PK\x05\x06", # empty archive
B"PK\x07\x08", # spanned archive
]
@classmethod
def __A ( cls: List[str] , __A: Union[Path, str] , __A: bytes = b"" ) -> bool:
if super().is_extractable(__A , magic_number=__A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__A , '''rb''' ) as fp:
_A = _EndRecData(__A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_A = fp.read(__A ) # CD is where we expect it to be
if len(__A ) == sizeCentralDir:
_A = struct.unpack(__A , __A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
os.makedirs(__A , exist_ok=__A )
with zipfile.ZipFile(__A , '''r''' ) as zip_file:
zip_file.extractall(__A )
zip_file.close()
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
with lzma.open(__A ) as compressed_file:
with open(__A , '''wb''' ) as extracted_file:
shutil.copyfileobj(__A , __A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"Rar!\x1a\x07\x00", B"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError('''Please pip install rarfile''' )
import rarfile
os.makedirs(__A , exist_ok=__A )
_A = rarfile.RarFile(__A )
rf.extractall(__A )
rf.close()
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\x28\xb5\x2F\xFD"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('''Please pip install zstandard''' )
import zstandard as zstd
_A = zstd.ZstdDecompressor()
with open(__A , '''rb''' ) as ifh, open(__A , '''wb''' ) as ofh:
dctx.copy_stream(__A , __A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\x42\x5A\x68"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
with bza.open(__A , '''rb''' ) as compressed_file:
with open(__A , '''wb''' ) as extracted_file:
shutil.copyfileobj(__A , __A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError('''Please pip install py7zr''' )
import pyazr
os.makedirs(__A , exist_ok=__A )
with pyazr.SevenZipFile(__A , '''r''' ) as archive:
archive.extractall(__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = [B"\x04\x22\x4D\x18"]
@staticmethod
def __A ( __A: Union[Path, str] , __A: Union[Path, str] ) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError('''Please pip install lz4''' )
import lza.frame
with lza.frame.open(__A , '''rb''' ) as compressed_file:
with open(__A , '''wb''' ) as extracted_file:
shutil.copyfileobj(__A , __A )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def __A ( cls: Any ) -> List[Any]:
return max(
len(__A )
for extractor in cls.extractors.values()
if issubclass(__A , __A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def __A ( __A: Union[Path, str] , __A: int ) -> Tuple:
try:
return MagicNumberBaseExtractor.read_magic_number(__A , magic_number_length=__A )
except OSError:
return b""
@classmethod
def __A ( cls: int , __A: Union[Path, str] , __A: bool = False ) -> bool:
warnings.warn(
'''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'infer_extractor_format\' instead.''' , category=__A , )
_A = cls.infer_extractor_format(__A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def __A ( cls: Any , __A: Union[Path, str] ) -> str: # <Added version="2.4.0"/>
_A = cls._get_magic_number_max_length()
_A = cls._read_magic_number(__A , __A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__A , magic_number=__A ):
return extractor_format
@classmethod
def __A ( cls: Any , __A: Union[Path, str] , __A: Union[Path, str] , __A: Optional[str] = None , __A: Optional[BaseExtractor] = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(__A ) , exist_ok=__A )
# Prevent parallel extractions
_A = str(Path(__A ).with_suffix('''.lock''' ) )
with FileLock(__A ):
shutil.rmtree(__A , ignore_errors=__A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__A , __A ): # passed as positional arg
warnings.warn(
'''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'extractor_format\' instead.''' , category=__A , )
_A = extractor if extractor != '''deprecated''' else extractor_format
else:
_A = cls.extractors[extractor_format]
return extractor.extract(__A , __A )
else:
warnings.warn(
'''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
'''exception in 3.0.0.''' , category=__A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__A ):
return extractor.extract(__A , __A )
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = "ssube/stable-diffusion-x4-upscaler-onnx"
def __A ( self: Union[str, Any] , __A: str=0 ) -> int:
_A = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(__A ) )
_A = torch.manual_seed(__A )
_A = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def __A ( self: int ) -> Optional[Any]:
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs()
_A = pipe(**__A ).images
_A = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def __A ( self: List[Any] ) -> Optional[Any]:
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_A = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs()
_A = pipe(**__A ).images
_A = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __A ( self: Tuple ) -> Any:
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs()
_A = pipe(**__A ).images
_A = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __A ( self: List[Any] ) -> Optional[Any]:
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_A = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs()
_A = pipe(**__A ).images
_A = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __A ( self: List[Any] ) -> Dict:
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_A = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs()
_A = pipe(**__A ).images
_A = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __A ( self: List[Any] ) -> Union[str, Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __A ( self: str ) -> str:
_A = ort.SessionOptions()
_A = False
return options
def __A ( self: List[Any] ) -> Optional[int]:
_A = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
_A = init_image.resize((1_28, 1_28) )
# using the PNDM scheduler by default
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
_A = '''A fantasy landscape, trending on artstation'''
_A = torch.manual_seed(0 )
_A = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type='''np''' , )
_A = output.images
_A = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_A = np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __A ( self: Tuple ) -> Tuple:
_A = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
_A = init_image.resize((1_28, 1_28) )
_A = LMSDiscreteScheduler.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , subfolder='''scheduler''' )
_A = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
_A = '''A fantasy landscape, trending on artstation'''
_A = torch.manual_seed(0 )
_A = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type='''np''' , )
_A = output.images
_A = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_A = np.array(
[0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A = logging.get_logger(__name__)
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowercase , _lowercase , _lowercase=0 , _lowercase=None ):
_A = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_A = math.floor(val / multiple ) * multiple
if x < min_val:
_A = math.ceil(val / multiple ) * multiple
return x
_A = (output_size, output_size) if isinstance(_lowercase , _lowercase ) else output_size
_A ,_A = get_image_size(_lowercase )
_A ,_A = output_size
# determine new height and width
_A = output_height / input_height
_A = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_A = scale_width
else:
# fit height
_A = scale_height
_A = constraint_to_multiple_of(scale_height * input_height , multiple=_lowercase )
_A = constraint_to_multiple_of(scale_width * input_width , multiple=_lowercase )
return (new_height, new_width)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["pixel_values"]
def __init__( self: int , __A: bool = True , __A: Dict[str, int] = None , __A: PILImageResampling = PILImageResampling.BILINEAR , __A: bool = False , __A: int = 1 , __A: bool = True , __A: Union[int, float] = 1 / 2_55 , __A: bool = True , __A: Optional[Union[float, List[float]]] = None , __A: Optional[Union[float, List[float]]] = None , **__A: Optional[int] , ) -> None:
super().__init__(**__A )
_A = size if size is not None else {'''height''': 3_84, '''width''': 3_84}
_A = get_size_dict(__A )
_A = do_resize
_A = size
_A = keep_aspect_ratio
_A = ensure_multiple_of
_A = resample
_A = do_rescale
_A = rescale_factor
_A = do_normalize
_A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __A ( self: Dict , __A: np.ndarray , __A: Dict[str, int] , __A: bool = False , __A: int = 1 , __A: PILImageResampling = PILImageResampling.BICUBIC , __A: Optional[Union[str, ChannelDimension]] = None , **__A: Union[str, Any] , ) -> np.ndarray:
_A = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_A = get_resize_output_image_size(
__A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=__A , multiple=__A , )
return resize(__A , size=__A , resample=__A , data_format=__A , **__A )
def __A ( self: Optional[int] , __A: np.ndarray , __A: Union[int, float] , __A: Optional[Union[str, ChannelDimension]] = None , **__A: List[str] , ) -> Optional[int]:
return rescale(__A , scale=__A , data_format=__A , **__A )
def __A ( self: Optional[int] , __A: np.ndarray , __A: Union[float, List[float]] , __A: Union[float, List[float]] , __A: Optional[Union[str, ChannelDimension]] = None , **__A: Optional[Any] , ) -> np.ndarray:
return normalize(__A , mean=__A , std=__A , data_format=__A , **__A )
def __A ( self: Any , __A: ImageInput , __A: bool = None , __A: int = None , __A: bool = None , __A: int = None , __A: PILImageResampling = None , __A: bool = None , __A: float = None , __A: bool = None , __A: Optional[Union[float, List[float]]] = None , __A: Optional[Union[float, List[float]]] = None , __A: Optional[Union[str, TensorType]] = None , __A: ChannelDimension = ChannelDimension.FIRST , **__A: Tuple , ) -> PIL.Image.Image:
_A = do_resize if do_resize is not None else self.do_resize
_A = size if size is not None else self.size
_A = get_size_dict(__A )
_A = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_A = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_A = resample if resample is not None else self.resample
_A = do_rescale if do_rescale is not None else self.do_rescale
_A = rescale_factor if rescale_factor is not None else self.rescale_factor
_A = do_normalize if do_normalize is not None else self.do_normalize
_A = image_mean if image_mean is not None else self.image_mean
_A = image_std if image_std is not None else self.image_std
_A = make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_A = [to_numpy_array(__A ) for image in images]
if do_resize:
_A = [self.resize(image=__A , size=__A , resample=__A ) for image in images]
if do_rescale:
_A = [self.rescale(image=__A , scale=__A ) for image in images]
if do_normalize:
_A = [self.normalize(image=__A , mean=__A , std=__A ) for image in images]
_A = [to_channel_dimension_format(__A , __A ) for image in images]
_A = {'''pixel_values''': images}
return BatchFeature(data=__A , tensor_type=__A )
def __A ( self: Union[str, Any] , __A: int , __A: List[Tuple] = None ) -> str:
_A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__A ) != len(__A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(__A ):
_A = target_sizes.numpy()
_A = []
for idx in range(len(__A ) ):
_A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=__A )
_A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__A )
else:
_A = logits.argmax(dim=1 )
_A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''only integers accepted as input''' )
else:
_A = str(abs(_lowercase ) )
_A = [list(_lowercase ) for char in range(len(_lowercase ) )]
for index in range(len(_lowercase ) ):
num_transpositions[index].pop(_lowercase )
return max(
int(''''''.join(list(_lowercase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('doctest').testmod()
| 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = [1]
for i in range(2 , _lowercase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_A = []
_A = list(range(_lowercase ) )
# Find permutation
while factorials:
_A = factorials.pop()
_A ,_A = divmod(_lowercase , _lowercase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
for ch in input_str:
_A = ord(_lowercase )
_A = pow(2 , _lowercase )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase , _lowercase="attention" ):
'''simple docstring'''
_A = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
_A = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
_A = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
_A = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def __A ( _lowercase , _lowercase , _lowercase , _lowercase=False ):
'''simple docstring'''
if split_mlp_wi:
_A = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
_A = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
_A = (wi_a, wi_a)
else:
_A = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
_A = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def __A ( _lowercase , *, _lowercase , _lowercase ):
'''simple docstring'''
_A = traverse_util.flatten_dict(variables['''target'''] )
_A = {'''/'''.join(_lowercase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_A = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , _lowercase )
_A = collections.OrderedDict()
# Shared embeddings.
_A = old['''token_embedder/embedding''']
# Encoder.
for i in range(_lowercase ):
# Block i, layer 0 (Self Attention).
_A = tax_layer_norm_lookup(_lowercase , _lowercase , '''encoder''' , '''pre_attention_layer_norm''' )
_A ,_A ,_A ,_A = tax_attention_lookup(_lowercase , _lowercase , '''encoder''' , '''attention''' )
_A = layer_norm
_A = k.T
_A = o.T
_A = q.T
_A = v.T
# Block i, layer 1 (MLP).
_A = tax_layer_norm_lookup(_lowercase , _lowercase , '''encoder''' , '''pre_mlp_layer_norm''' )
_A ,_A = tax_mlp_lookup(_lowercase , _lowercase , '''encoder''' , _lowercase )
_A = layer_norm
if split_mlp_wi:
_A = wi[0].T
_A = wi[1].T
else:
_A = wi.T
_A = wo.T
_A = old[
'''encoder/relpos_bias/rel_embedding'''
].T
_A = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(_lowercase ):
# Block i, layer 0 (Self Attention).
_A = tax_layer_norm_lookup(_lowercase , _lowercase , '''decoder''' , '''pre_self_attention_layer_norm''' )
_A ,_A ,_A ,_A = tax_attention_lookup(_lowercase , _lowercase , '''decoder''' , '''self_attention''' )
_A = layer_norm
_A = k.T
_A = o.T
_A = q.T
_A = v.T
# Block i, layer 1 (Cross Attention).
_A = tax_layer_norm_lookup(_lowercase , _lowercase , '''decoder''' , '''pre_cross_attention_layer_norm''' )
_A ,_A ,_A ,_A = tax_attention_lookup(_lowercase , _lowercase , '''decoder''' , '''encoder_decoder_attention''' )
_A = layer_norm
_A = k.T
_A = o.T
_A = q.T
_A = v.T
# Block i, layer 2 (MLP).
_A = tax_layer_norm_lookup(_lowercase , _lowercase , '''decoder''' , '''pre_mlp_layer_norm''' )
_A ,_A = tax_mlp_lookup(_lowercase , _lowercase , '''decoder''' , _lowercase )
_A = layer_norm
if split_mlp_wi:
_A = wi[0].T
_A = wi[1].T
else:
_A = wi.T
_A = wo.T
_A = old['''decoder/decoder_norm/scale''']
_A = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_A = old['''decoder/logits_dense/kernel'''].T
return new
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_A = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_A = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_A = state_dict['''shared.weight''']
return state_dict
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = checkpoints.load_tax_checkpoint(_lowercase )
_A = convert_tax_to_pytorch(_lowercase , num_layers=config.num_layers , is_encoder_only=_lowercase )
_A = make_state_dict(_lowercase , _lowercase )
model.load_state_dict(_lowercase , strict=_lowercase )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase = False ):
'''simple docstring'''
_A = TaConfig.from_json_file(_lowercase )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_A = TaEncoderModel(_lowercase )
else:
_A = TaForConditionalGeneration(_lowercase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(_lowercase , _lowercase , _lowercase , _lowercase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_lowercase )
# Verify that we can load the checkpoint.
model.from_pretrained(_lowercase )
print('''Done''' )
if __name__ == "__main__":
__A = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__A = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = LEDConfig
A_ = {}
A_ = "gelu"
def __init__( self: Union[str, Any] , __A: Dict , __A: Optional[Any]=13 , __A: Optional[int]=7 , __A: str=True , __A: Union[str, Any]=False , __A: List[Any]=99 , __A: Tuple=32 , __A: str=2 , __A: Optional[Any]=4 , __A: Optional[int]=37 , __A: str=0.1 , __A: Tuple=0.1 , __A: List[Any]=20 , __A: Optional[Any]=2 , __A: Union[str, Any]=1 , __A: Optional[int]=0 , __A: Optional[Any]=4 , ) -> List[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = eos_token_id
_A = pad_token_id
_A = bos_token_id
_A = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
_A = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
_A = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __A ( self: Dict ) -> Any:
_A = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A = tf.concat([input_ids, eos_tensor] , axis=1 )
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
_A = prepare_led_inputs_dict(__A , __A , __A )
_A = tf.concat(
[tf.zeros_like(__A )[:, :-1], tf.ones_like(__A )[:, -1:]] , axis=-1 , )
_A = global_attention_mask
return config, inputs_dict
def __A ( self: int , __A: Dict , __A: Optional[int] ) -> List[Any]:
_A = TFLEDModel(config=__A ).get_decoder()
_A = inputs_dict['''input_ids''']
_A = input_ids[:1, :]
_A = inputs_dict['''attention_mask'''][:1, :]
_A = 1
# first forward pass
_A = model(__A , attention_mask=__A , use_cache=__A )
_A ,_A = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_A = ids_tensor((self.batch_size, 3) , config.vocab_size )
_A = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_A = tf.concat([input_ids, next_tokens] , axis=-1 )
_A = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_A = model(__A , attention_mask=__A )[0]
_A = model(__A , attention_mask=__A , past_key_values=__A )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_A = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_A = output_from_no_past[:, -3:, random_slice_idx]
_A = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__A , __A , rtol=1e-3 )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ):
'''simple docstring'''
if attention_mask is None:
_A = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_A = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_A = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
A_ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
A_ = (
{
"conversational": TFLEDForConditionalGeneration,
"feature-extraction": TFLEDModel,
"summarization": TFLEDForConditionalGeneration,
"text2text-generation": TFLEDForConditionalGeneration,
"translation": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
A_ = True
A_ = False
A_ = False
A_ = False
def __A ( self: Any ) -> List[Any]:
_A = TFLEDModelTester(self )
_A = ConfigTester(self , config_class=__A )
def __A ( self: int ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __A ( self: List[str] ) -> int:
_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__A )
def __A ( self: Optional[int] ) -> Optional[int]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
_A = tf.zeros_like(inputs_dict['''attention_mask'''] )
_A = 2
_A = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
_A = True
_A = self.model_tester.seq_length
_A = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__A: str ):
_A = outputs.decoder_attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__A: Tuple ):
_A = [t.numpy() for t in outputs.encoder_attentions]
_A = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
_A = True
_A = False
_A = False
_A = model_class(__A )
_A = model(self._prepare_for_class(__A , __A ) )
_A = len(__A )
self.assertEqual(config.output_hidden_states , __A )
check_encoder_attentions_output(__A )
if self.is_encoder_decoder:
_A = model_class(__A )
_A = model(self._prepare_for_class(__A , __A ) )
self.assertEqual(config.output_hidden_states , __A )
check_decoder_attentions_output(__A )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_A = True
_A = model_class(__A )
_A = model(self._prepare_for_class(__A , __A ) )
self.assertEqual(config.output_hidden_states , __A )
check_encoder_attentions_output(__A )
# Check attention is always last and order is fine
_A = True
_A = True
_A = model_class(__A )
_A = model(self._prepare_for_class(__A , __A ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__A ) )
self.assertEqual(model.config.output_hidden_states , __A )
check_encoder_attentions_output(__A )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def __A ( self: Optional[int] ) -> List[str]:
pass
def __A ( self: Dict ) -> Optional[Any]:
# TODO: Head-masking not yet implement
pass
def __A ( _lowercase ):
'''simple docstring'''
return tf.constant(_lowercase , dtype=tf.intaa )
__A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Optional[int] ) -> Any:
_A = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
_A = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
_A = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
_A = prepare_led_inputs_dict(model.config , __A , __A )
_A = model(**__A )[0]
_A = (1, 10_24, 7_68)
self.assertEqual(output.shape , __A )
# change to expected output here
_A = tf.convert_to_tensor(
[[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __A , atol=1e-3 )
def __A ( self: Any ) -> Union[str, Any]:
_A = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
_A = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
_A = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
_A = prepare_led_inputs_dict(model.config , __A , __A )
_A = model(**__A )[0]
_A = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __A )
# change to expected output here
_A = tf.convert_to_tensor(
[[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __A , atol=1e-3 , rtol=1e-3 )
| 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
_A = m # noqa: E741
return r
def __A ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
_A = [0] * len(_lowercase )
_A = 1
_A = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
_A = v[i]
elif v[i] > tail[length - 1]:
_A = v[i]
length += 1
else:
_A = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: int , __A: int , __A: float = 0 ) -> None:
_A ,_A = row, column
_A = [[default_value for c in range(__A )] for r in range(__A )]
def __str__( self: int ) -> str:
_A = f"""Matrix consist of {self.row} rows and {self.column} columns\n"""
# Make string identifier
_A = 0
for row_vector in self.array:
for obj in row_vector:
_A = max(__A , len(str(__A ) ) )
_A = f"""%{max_element_length}s"""
# Make string and return
def single_line(__A: list[float] ) -> str:
nonlocal string_format_identifier
_A = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(__A ) for row_vector in self.array )
return s
def __repr__( self: List[str] ) -> str:
return str(self )
def __A ( self: str , __A: tuple[int, int] ) -> bool:
if not (isinstance(__A , (list, tuple) ) and len(__A ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self: int , __A: tuple[int, int] ) -> Any:
assert self.validate_indicies(__A )
return self.array[loc[0]][loc[1]]
def __setitem__( self: Any , __A: tuple[int, int] , __A: float ) -> None:
assert self.validate_indicies(__A )
_A = value
def __add__( self: int , __A: Matrix ) -> Matrix:
assert isinstance(__A , __A )
assert self.row == another.row and self.column == another.column
# Add
_A = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
_A = self[r, c] + another[r, c]
return result
def __neg__( self: Dict ) -> Matrix:
_A = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
_A = -self[r, c]
return result
def __sub__( self: List[Any] , __A: Matrix ) -> Matrix:
return self + (-another)
def __mul__( self: List[str] , __A: int | float | Matrix ) -> Matrix:
if isinstance(__A , (int, float) ): # Scalar multiplication
_A = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
_A = self[r, c] * another
return result
elif isinstance(__A , __A ): # Matrix multiplication
assert self.column == another.row
_A = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
_A = f"""Unsupported type given for another ({type(__A )})"""
raise TypeError(__A )
def __A ( self: int ) -> Matrix:
_A = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
_A = self[r, c]
return result
def __A ( self: List[str] , __A: Matrix , __A: Matrix ) -> Any:
assert isinstance(__A , __A ) and isinstance(__A , __A )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
_A = v.transpose()
_A = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def __A ( ):
'''simple docstring'''
_A = Matrix(3 , 3 , 0 )
for i in range(3 ):
_A = 1
print(f"""a^(-1) is {ainv}""" )
# u, v
_A = Matrix(3 , 1 , 0 )
_A ,_A ,_A = 1, 2, -3
_A = Matrix(3 , 1 , 0 )
_A ,_A ,_A = 4, -2, 5
print(f"""u is {u}""" )
print(f"""v is {v}""" )
print(f"""uv^T is {u * v.transpose()}""" )
# Sherman Morrison
print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowercase , _lowercase )}""" )
def __A ( ):
'''simple docstring'''
import doctest
doctest.testmod()
testa()
| 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__A = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "sequence-classification"
def __init__( self: str , __A: Union[str, Any] ) -> List[str]:
if type(__A ) == dict:
_A = Namespace(**__A )
_A = glue_output_modes[hparams.task]
_A = glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def __A ( self: Optional[Any] , **__A: Union[str, Any] ) -> Optional[int]:
return self.model(**__A )
def __A ( self: Any , __A: Union[str, Any] , __A: int ) -> Optional[Any]:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A = outputs[0]
_A = self.trainer.lr_schedulers[0]['''scheduler''']
_A = {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def __A ( self: List[str] ) -> Dict:
_A = self.hparams
_A = processors[args.task]()
_A = processor.get_labels()
for mode in ["train", "dev"]:
_A = self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , __A )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
_A = (
processor.get_dev_examples(args.data_dir )
if mode == '''dev'''
else processor.get_train_examples(args.data_dir )
)
_A = convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('''Saving features into cached file %s''' , __A )
torch.save(__A , __A )
def __A ( self: List[str] , __A: str , __A: int , __A: bool = False ) -> DataLoader:
_A = '''dev''' if mode == '''test''' else mode
_A = self._feature_file(__A )
logger.info('''Loading features from cached file %s''' , __A )
_A = torch.load(__A )
_A = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_A = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_A = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_A = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_A = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def __A ( self: List[str] , __A: str , __A: Tuple ) -> str:
_A = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_A = batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None
_A = self(**__A )
_A ,_A = outputs[:2]
_A = logits.detach().cpu().numpy()
_A = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __A ( self: str , __A: Dict ) -> tuple:
_A = torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
_A = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_A = np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_A = np.squeeze(__A )
_A = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = [[] for _ in range(out_label_ids.shape[0] )]
_A = {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_A = dict(results.items() )
_A = results
return ret, preds_list, out_label_list
def __A ( self: Any , __A: list ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __A ( self: int , __A: Union[str, Any] ) -> dict:
_A ,_A ,_A = self._eval_end(__A )
_A = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __A ( __A: Optional[Any] , __A: Optional[Any] ) -> Optional[Any]:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--task''' , default='''''' , type=__A , required=__A , help='''The GLUE task to run''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=__A , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser()
add_generic_args(_lowercase , os.getcwd() )
_A = GLUETransformer.add_model_specific_args(_lowercase , os.getcwd() )
_A = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_A = os.path.join(
'''./results''' , f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
_A = GLUETransformer(_lowercase )
_A = generic_train(_lowercase , _lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_A = sorted(glob.glob(os.path.join(args.output_dir , '''checkpoint-epoch=*.ckpt''' ) , recursive=_lowercase ) )
_A = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_lowercase )
if __name__ == "__main__":
main()
| 62 | 1 |
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
#
########################################################################
__A = 16
__A = 32
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = 16 ):
'''simple docstring'''
_A = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_A = DatasetDict(
{
'''train''': dataset['''train'''].select(_lowercase ),
'''validation''': dataset['''train'''].select(_lowercase ),
'''test''': dataset['''validation'''],
} )
def tokenize_function(_lowercase ):
# max_length=None => use the model max length (it's actually the default)
_A = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_lowercase , max_length=_lowercase )
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 = datasets.map(
_lowercase , batched=_lowercase , 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 = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_A = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_A = 16
elif accelerator.mixed_precision != "no":
_A = 8
else:
_A = None
return tokenizer.pad(
_lowercase , padding='''longest''' , max_length=_lowercase , pad_to_multiple_of=_lowercase , return_tensors='''pt''' , )
# Instantiate dataloaders.
_A = DataLoader(
tokenized_datasets['''train'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
_A = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
_A = DataLoader(
tokenized_datasets['''test'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
return train_dataloader, eval_dataloader, test_dataloader
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = []
# Download the dataset
_A = load_dataset('''glue''' , '''mrpc''' )
# Create our splits
_A = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_A = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_A = config['''lr''']
_A = int(config['''num_epochs'''] )
_A = int(config['''seed'''] )
_A = int(config['''batch_size'''] )
_A = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_A = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_A = batch_size // MAX_GPU_BATCH_SIZE
_A = MAX_GPU_BATCH_SIZE
set_seed(_lowercase )
# New Code #
# Create our folds:
_A = kfold.split(np.zeros(datasets['''train'''].num_rows ) , datasets['''train''']['''label'''] )
_A = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(_lowercase ):
_A ,_A ,_A = get_fold_dataloaders(
_lowercase , _lowercase , _lowercase , _lowercase , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_A = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_lowercase )
# 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 = model.to(accelerator.device )
# Instantiate optimizer
_A = AdamW(params=model.parameters() , lr=_lowercase )
# Instantiate scheduler
_A = get_linear_schedule_with_warmup(
optimizer=_lowercase , num_warmup_steps=1_00 , num_training_steps=(len(_lowercase ) * 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 = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# Now we train the model
for epoch in range(_lowercase ):
model.train()
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_A = model(**_lowercase )
_A = outputs.loss
_A = loss / gradient_accumulation_steps
accelerator.backward(_lowercase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_A = model(**_lowercase )
_A = outputs.logits.argmax(dim=-1 )
_A ,_A = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_lowercase , references=_lowercase , )
_A = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , _lowercase )
# New Code #
# We also run predictions on the test set at the very end
_A = []
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_A = model(**_lowercase )
_A = outputs.logits
_A ,_A = 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(_lowercase , 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 = torch.cat(_lowercase , dim=0 )
_A = torch.stack(_lowercase , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_A = metric.compute(predictions=_lowercase , references=_lowercase )
accelerator.print('''Average test metrics from all folds:''' , _lowercase )
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_lowercase , default=_lowercase , 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=_lowercase , default=3 , help='''The number of splits to perform across the dataset''' )
_A = parser.parse_args()
_A = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_lowercase , _lowercase )
if __name__ == "__main__":
main()
| 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).text , '''html.parser''' )
_A = soup.find_all('''td''' , attrs='''titleColumn''' )
_A = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowercase , _lowercase )
}
def __A ( _lowercase = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_A = get_imdb_top_aaa_movies()
with open(_lowercase , '''w''' , newline='''''' ) as out_file:
_A = csv.writer(_lowercase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "xlm-prophetnet"
A_ = ["past_key_values"]
A_ = {
"num_attention_heads": "num_encoder_attention_heads",
}
def __init__( self: Optional[int] , __A: Optional[float] = 0.1 , __A: Optional[Union[str, Callable]] = "gelu" , __A: Optional[int] = 3_05_22 , __A: Optional[int] = 10_24 , __A: Optional[int] = 40_96 , __A: Optional[int] = 12 , __A: Optional[int] = 16 , __A: Optional[int] = 40_96 , __A: Optional[int] = 12 , __A: Optional[int] = 16 , __A: Optional[float] = 0.1 , __A: Optional[float] = 0.1 , __A: Optional[int] = 5_12 , __A: Optional[float] = 0.02 , __A: Optional[bool] = True , __A: Optional[bool] = True , __A: Optional[int] = 0 , __A: Optional[int] = 2 , __A: Optional[int] = 32 , __A: Optional[int] = 1_28 , __A: Optional[bool] = False , __A: Optional[float] = 0.0 , __A: Optional[bool] = True , __A: Optional[int] = 0 , __A: Optional[int] = 1 , __A: Optional[int] = 2 , **__A: Tuple , ) -> List[Any]:
_A = vocab_size
_A = hidden_size
_A = encoder_ffn_dim
_A = num_encoder_layers
_A = num_encoder_attention_heads
_A = decoder_ffn_dim
_A = num_decoder_layers
_A = num_decoder_attention_heads
_A = max_position_embeddings
_A = init_std # Normal(0, this parameter)
_A = activation_function
# parameters for xlmprophetnet
_A = ngram
_A = num_buckets
_A = relative_max_distance
_A = disable_ngram_loss
_A = eps
# 3 Types of Dropout
_A = attention_dropout
_A = activation_dropout
_A = dropout
_A = use_cache
super().__init__(
pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , is_encoder_decoder=__A , add_cross_attention=__A , decoder_start_token_id=__A , **__A , )
@property
def __A ( self: Union[str, Any] ) -> int:
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def __A ( self: Tuple , __A: Dict ) -> int:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BlenderbotSmallTokenizer
A_ = False
def __A ( self: List[str] ) -> int:
super().setUp()
_A = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_A = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = 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 __A ( self: str , **__A: Optional[Any] ) -> Dict:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: str , __A: List[str] ) -> int:
_A = '''adapt act apte'''
_A = '''adapt act apte'''
return input_text, output_text
def __A ( self: Union[str, Any] ) -> Any:
_A = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_A = '''adapt act apte'''
_A = ['''adapt''', '''act''', '''ap@@''', '''te''']
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_A = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_A = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
def __A ( self: Any ) -> List[str]:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
_A = '''I am a small frog.'''
_A = tok([src_text] , padding=__A , truncation=__A )['''input_ids''']
_A = tok.batch_decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def __A ( self: Any ) -> int:
_A = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_A = '''I am a small frog .'''
_A = '''.'''
_A = tok(__A )['''input_ids''']
_A = tok(__A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 62 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["image_processor", "tokenizer"]
A_ = "FlavaImageProcessor"
A_ = ("BertTokenizer", "BertTokenizerFast")
def __init__( self: str , __A: Dict=None , __A: List[str]=None , **__A: Tuple ) -> str:
_A = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __A , )
_A = kwargs.pop('''feature_extractor''' )
_A = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__A , __A )
_A = self.image_processor
def __call__( self: Any , __A: Optional[ImageInput] = None , __A: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , __A: bool = True , __A: Union[bool, str, PaddingStrategy] = False , __A: Union[bool, str, TruncationStrategy] = False , __A: Optional[int] = None , __A: int = 0 , __A: Optional[int] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , __A: Optional[bool] = None , __A: bool = False , __A: bool = False , __A: bool = False , __A: bool = False , __A: bool = True , __A: Optional[Union[str, TensorType]] = None , **__A: List[Any] , ) -> Any:
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 = self.tokenizer(
text=__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , stride=__A , pad_to_multiple_of=__A , return_token_type_ids=__A , return_attention_mask=__A , return_overflowing_tokens=__A , return_special_tokens_mask=__A , return_offsets_mapping=__A , return_length=__A , verbose=__A , return_tensors=__A , **__A , )
if images is not None:
_A = self.image_processor(
__A , return_image_mask=__A , return_codebook_pixels=__A , return_tensors=__A , **__A , )
if text is not None and images is not None:
encoding.update(__A )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def __A ( self: int , *__A: Dict , **__A: Union[str, Any] ) -> str:
return self.tokenizer.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Optional[Any] , **__A: Tuple ) -> Union[str, Any]:
return self.tokenizer.decode(*__A , **__A )
@property
def __A ( self: Union[str, Any] ) -> str:
_A = self.tokenizer.model_input_names
_A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self: Optional[int] ) -> Optional[int]:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __A , )
return self.image_processor_class
@property
def __A ( self: Optional[int] ) -> Union[str, Any]:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __A , )
return self.image_processor
| 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "roberta"
def __init__( self: Dict , __A: int=5_02_65 , __A: Union[str, Any]=7_68 , __A: Union[str, Any]=12 , __A: str=12 , __A: int=30_72 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Optional[int]=5_12 , __A: Union[str, Any]=2 , __A: str=0.02 , __A: str=1e-12 , __A: Any=1 , __A: str=0 , __A: Any=2 , __A: Optional[int]="absolute" , __A: Optional[Any]=True , __A: Union[str, Any]=None , **__A: List[str] , ) -> Dict:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@property
def __A ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 62 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "align_text_model"
def __init__( self: Optional[Any] , __A: Optional[int]=3_05_22 , __A: str=7_68 , __A: Any=12 , __A: List[Any]=12 , __A: List[str]=30_72 , __A: Union[str, Any]="gelu" , __A: Optional[int]=0.1 , __A: Dict=0.1 , __A: Optional[Any]=5_12 , __A: Optional[Any]=2 , __A: Optional[int]=0.02 , __A: Optional[int]=1e-12 , __A: Optional[Any]=0 , __A: Any="absolute" , __A: Union[str, Any]=True , **__A: Optional[Any] , ) -> Union[str, Any]:
super().__init__(**__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = pad_token_id
@classmethod
def __A ( cls: Tuple , __A: Union[str, os.PathLike] , **__A: int ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_A ,_A = cls.get_config_dict(__A , **__A )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
_A = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "align_vision_model"
def __init__( self: Optional[int] , __A: int = 3 , __A: int = 6_00 , __A: float = 2.0 , __A: float = 3.1 , __A: int = 8 , __A: List[int] = [3, 3, 5, 3, 5, 5, 3] , __A: List[int] = [32, 16, 24, 40, 80, 1_12, 1_92] , __A: List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20] , __A: List[int] = [] , __A: List[int] = [1, 2, 2, 2, 1, 2, 1] , __A: List[int] = [1, 2, 2, 3, 3, 4, 1] , __A: List[int] = [1, 6, 6, 6, 6, 6, 6] , __A: float = 0.25 , __A: str = "swish" , __A: int = 25_60 , __A: str = "mean" , __A: float = 0.02 , __A: float = 0.001 , __A: float = 0.99 , __A: float = 0.2 , **__A: str , ) -> str:
super().__init__(**__A )
_A = num_channels
_A = image_size
_A = width_coefficient
_A = depth_coefficient
_A = depth_divisor
_A = kernel_sizes
_A = in_channels
_A = out_channels
_A = depthwise_padding
_A = strides
_A = num_block_repeats
_A = expand_ratios
_A = squeeze_expansion_ratio
_A = hidden_act
_A = hidden_dim
_A = pooling_type
_A = initializer_range
_A = batch_norm_eps
_A = batch_norm_momentum
_A = drop_connect_rate
_A = sum(__A ) * 4
@classmethod
def __A ( cls: List[Any] , __A: Union[str, os.PathLike] , **__A: Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_A ,_A = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
_A = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "align"
A_ = True
def __init__( self: int , __A: Dict=None , __A: str=None , __A: Optional[Any]=6_40 , __A: str=1.0 , __A: Union[str, Any]=0.02 , **__A: Union[str, Any] , ) -> str:
super().__init__(**__A )
if text_config is None:
_A = {}
logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''' )
if vision_config is None:
_A = {}
logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''' )
_A = AlignTextConfig(**__A )
_A = AlignVisionConfig(**__A )
_A = projection_dim
_A = temperature_init_value
_A = initializer_range
@classmethod
def __A ( cls: Dict , __A: AlignTextConfig , __A: AlignVisionConfig , **__A: Optional[Any] ) -> Union[str, Any]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def __A ( self: List[Any] ) -> str:
_A = copy.deepcopy(self.__dict__ )
_A = self.text_config.to_dict()
_A = self.vision_config.to_dict()
_A = self.__class__.model_type
return output
| 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: str , __A: List[Any]=None , __A: Union[str, Any]=None , __A: List[Any]=None , **__A: int ) -> List[Any]:
super().__init__(*__A , **__A )
_A = eval_examples
_A = post_process_function
_A = quant_trainer_args
_A = 1_28 # default number of calibration samples
def __A ( self: Union[str, Any] , __A: List[Any]=None ) -> Optional[Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_A = calib_dataset if calib_dataset is not None else self.calib_dataset
_A = self._remove_unused_columns(__A , description='''Calibration''' )
return DataLoader(
__A , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__A , )
def __A ( self: List[Any] , __A: Any=None ) -> Optional[int]:
_A = self.train_dataset if calib_dataset is None else calib_dataset
_A = self.get_calib_dataloader(__A )
_A = self.model
quant_trainer.configure_model(__A , self.quant_trainer_args , calib=__A )
model.eval()
quant_trainer.enable_calibration(__A )
logger.info('''***** Running calibration *****''' )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(__A ):
# Prediction step
_A ,_A ,_A = self.prediction_step(__A , __A , prediction_loss_only=__A )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__A , self.quant_trainer_args )
_A = model
def __A ( self: Any , __A: Dict=None , __A: Tuple=None , __A: List[Any]=None , __A: str = "eval" ) -> int:
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(__A )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_A = self.post_process_function(__A , __A , output.predictions )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
self.log(__A )
else:
_A = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , __A )
return metrics
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: int , __A: List[Any]=None , __A: str = "test" ) -> Union[str, Any]:
_A = self.get_test_dataloader(__A )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_A = eval_loop(
__A , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__A , )
finally:
_A = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(__A , __A , output.predictions , '''predict''' )
_A = self.compute_metrics(__A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_A = metrics.pop(__A )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__A )
def __A ( self: Tuple , __A: Optional[Any]="./" ) -> List[str]:
_A = self.eval_dataset
_A = self.get_eval_dataloader(__A )
_A = next(iter(__A ) )
# saving device - to make it consistent
_A = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_A = tuple(v.to(__A ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_A = True
_A = self.model.to(__A )
model.eval()
model.float()
_A = model.module if hasattr(__A , '''module''' ) else model
quant_trainer.configure_model(__A , self.quant_trainer_args )
_A = os.path.join(__A , '''model.onnx''' )
logger.info(f"""exporting model to {output_model_file}""" )
_A = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__A , __A , __A , export_params=__A , opset_version=13 , do_constant_folding=__A , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__A , )
logger.info('''onnx export finished''' )
| 62 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '▁'
__A = {'vocab_file': 'sentencepiece.bpe.model'}
__A = {
'vocab_file': {
'facebook/mbart-large-50-one-to-many-mmt': (
'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
),
}
}
__A = {
'facebook/mbart-large-50-one-to-many-mmt': 1024,
}
# fmt: off
__A = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = ["input_ids", "attention_mask"]
A_ = []
A_ = []
def __init__( self: List[str] , __A: Dict , __A: Dict=None , __A: List[str]=None , __A: List[Any]="</s>" , __A: Any="</s>" , __A: Optional[Any]="<s>" , __A: List[str]="<unk>" , __A: Optional[Any]="<pad>" , __A: Tuple="<mask>" , __A: Optional[Dict[str, Any]] = None , **__A: Union[str, Any] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
_A = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=__A , tgt_lang=__A , eos_token=__A , unk_token=__A , sep_token=__A , cls_token=__A , pad_token=__A , mask_token=__A , sp_model_kwargs=self.sp_model_kwargs , **__A , )
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__A ) )
_A = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_A = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_A = 1
_A = len(self.sp_model )
_A = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A )
}
_A = {v: k for k, v in self.lang_code_to_id.items()}
_A = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_A = src_lang if src_lang is not None else '''en_XX'''
_A = self.lang_code_to_id[self._src_lang]
_A = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __A ( self: str ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __A ( self: Any ) -> str:
return self._src_lang
@src_lang.setter
def __A ( self: Dict , __A: str ) -> None:
_A = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self: str ) -> Dict:
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self: int , __A: Dict ) -> None:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __A ( self: List[Any] ) -> Dict:
_A = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __A ( self: Tuple , __A: str ) -> List[str]:
return self.sp_model.encode(__A , out_type=__A )
def __A ( self: List[str] , __A: str ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_A = self.sp_model.PieceToId(__A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __A ( self: Tuple , __A: int ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __A ( self: List[Any] , __A: str ) -> Tuple:
_A = []
_A = ''''''
_A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__A ) + token
_A = True
_A = []
else:
current_sub_tokens.append(__A )
_A = False
out_string += self.sp_model.decode(__A )
return out_string.strip()
def __A ( self: Dict , __A: str , __A: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_A = os.path.join(
__A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
def __A ( self: Optional[int] , __A: List[int] , __A: Optional[List[int]] = None , __A: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
_A = [1] * len(self.prefix_tokens )
_A = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__A )) + suffix_ones
return prefix_ones + ([0] * len(__A )) + ([0] * len(__A )) + suffix_ones
def __A ( self: Tuple , __A: List[int] , __A: Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __A ( self: Tuple , __A: List[str] , __A: str , __A: Optional[str] , __A: Optional[str] , **__A: Any ) -> Optional[int]:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_A = src_lang
_A = self(__A , add_special_tokens=__A , return_tensors=__A , **__A )
_A = self.convert_tokens_to_ids(__A )
_A = tgt_lang_id
return inputs
def __A ( self: Optional[int] , __A: List[str] , __A: str = "en_XX" , __A: Optional[List[str]] = None , __A: str = "ro_RO" , **__A: Optional[Any] , ) -> BatchEncoding:
_A = src_lang
_A = tgt_lang
return super().prepare_seqaseq_batch(__A , __A , **__A )
def __A ( self: List[str] ) -> int:
return self.set_src_lang_special_tokens(self.src_lang )
def __A ( self: Tuple ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __A ( self: Dict , __A: str ) -> None:
_A = self.lang_code_to_id[src_lang]
_A = [self.cur_lang_code_id]
_A = [self.eos_token_id]
def __A ( self: List[str] , __A: str ) -> None:
_A = self.lang_code_to_id[tgt_lang]
_A = [self.cur_lang_code_id]
_A = [self.eos_token_id]
| 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[Any] , __A: Any , __A: List[str]=13 , __A: Tuple=7 , __A: Any=True , __A: Union[str, Any]=True , __A: str=True , __A: List[str]=True , __A: Any=99 , __A: str=64 , __A: str=32 , __A: Tuple=5 , __A: Dict=4 , __A: Optional[int]=37 , __A: List[str]="gelu" , __A: Dict=0.1 , __A: Optional[int]=0.1 , __A: Optional[Any]=5_12 , __A: List[Any]=16 , __A: Any=2 , __A: List[str]=0.02 , __A: Union[str, Any]=3 , __A: Dict=4 , __A: Optional[Any]=None , ) -> str:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = embedding_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
def __A ( self: List[str] ) -> Optional[int]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self: List[str] ) -> Optional[Any]:
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__A , initializer_range=self.initializer_range , )
def __A ( self: Optional[int] , __A: int , __A: Dict , __A: Tuple , __A: List[Any] , __A: int , __A: Optional[Any] , __A: List[Any] ) -> Union[str, Any]:
_A = MegatronBertModel(config=__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A )
_A = model(__A , token_type_ids=__A )
_A = model(__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 __A ( self: int , __A: Dict , __A: Optional[int] , __A: Dict , __A: Union[str, Any] , __A: int , __A: int , __A: List[str] ) -> Optional[int]:
_A = MegatronBertForMaskedLM(config=__A )
model.to(__A )
model.eval()
_A = 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 __A ( self: Any , __A: Optional[Any] , __A: Optional[int] , __A: int , __A: Union[str, Any] , __A: Optional[int] , __A: Optional[int] , __A: Optional[Any] ) -> int:
_A = MegatronBertForCausalLM(config=__A )
model.to(__A )
model.eval()
_A = 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 __A ( self: str , __A: Tuple , __A: List[str] , __A: Tuple , __A: List[str] , __A: Union[str, Any] , __A: Tuple , __A: List[Any] ) -> str:
_A = MegatronBertForNextSentencePrediction(config=__A )
model.to(__A )
model.eval()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __A ( self: List[Any] , __A: Optional[int] , __A: Union[str, Any] , __A: Any , __A: Any , __A: int , __A: Optional[int] , __A: Any ) -> List[str]:
_A = MegatronBertForPreTraining(config=__A )
model.to(__A )
model.eval()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , next_sentence_label=__A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __A ( self: Optional[Any] , __A: List[str] , __A: List[str] , __A: Tuple , __A: Dict , __A: Optional[Any] , __A: Dict , __A: Tuple ) -> Union[str, Any]:
_A = MegatronBertForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
_A = 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 __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: Optional[int] , __A: List[str] , __A: List[str] , __A: Dict , __A: Optional[Any] ) -> Union[str, Any]:
_A = self.num_labels
_A = MegatronBertForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self: Union[str, Any] , __A: List[Any] , __A: Tuple , __A: Optional[int] , __A: List[str] , __A: Any , __A: str , __A: int ) -> Union[str, Any]:
_A = self.num_labels
_A = MegatronBertForTokenClassification(config=__A )
model.to(__A )
model.eval()
_A = 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 __A ( self: str , __A: Optional[int] , __A: List[str] , __A: List[str] , __A: Dict , __A: int , __A: str , __A: str ) -> Dict:
_A = self.num_choices
_A = MegatronBertForMultipleChoice(config=__A )
model.to(__A )
model.eval()
_A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self: List[Any] ) -> Any:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
# test_resize_embeddings = False
A_ = False
def __A ( self: List[Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Any=False ) -> Dict:
_A = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class in get_values(__A ):
_A = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__A )
_A = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def __A ( self: List[str] ) -> str:
_A = MegatronBertModelTester(self )
_A = ConfigTester(self , config_class=__A , hidden_size=37 )
def __A ( self: int ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: List[str] ) -> List[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__A )
def __A ( self: int ) -> Union[str, Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A )
def __A ( self: Tuple ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A )
def __A ( self: List[Any] ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A )
def __A ( self: Union[str, Any] ) -> Optional[int]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A )
def __A ( self: Optional[Any] ) -> List[str]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A )
def __A ( self: Union[str, Any] ) -> Optional[int]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A )
def __A ( self: int ) -> Dict:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A )
def __A ( _lowercase ):
'''simple docstring'''
return torch.tensor(
_lowercase , dtype=torch.long , device=_lowercase , )
__A = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip('''Model is not available.''' )
def __A ( self: List[str] ) -> Dict:
_A = '''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
_A = os.path.join(os.environ['''MYDIR'''] , __A )
_A = MegatronBertModel.from_pretrained(__A )
model.to(__A )
model.half()
_A = _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
_A = model(__A )[0]
_A = torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __A )
_A = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3 ):
for jj in range(3 ):
_A = output[0, ii, jj]
_A = expected[3 * ii + jj]
_A = '''ii={} jj={} a={} b={}'''.format(__A , __A , __A , __A )
self.assertTrue(math.isclose(__A , __A , rel_tol=__A , abs_tol=__A ) , msg=__A )
| 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def __A ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(f"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(f"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(f"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 62 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: Any , __A: List[Any]=14 , __A: Dict=7 , __A: List[str]=True , __A: Tuple=True , __A: Union[str, Any]=True , __A: List[Any]=True , __A: Optional[int]=True , __A: Tuple=99 , __A: Optional[Any]=32 , __A: List[str]=5 , __A: Dict=4 , __A: str=37 , __A: Dict="gelu" , __A: List[str]=0.1 , __A: str=0.1 , __A: Any=5_12 , __A: Union[str, Any]=16 , __A: List[Any]=2 , __A: Tuple=0.02 , __A: Tuple=3 , __A: Union[str, Any]=4 , __A: Any=None , ) -> Optional[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_token_type_ids
_A = use_input_mask
_A = use_labels
_A = use_mc_token_ids
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = self.vocab_size - 1
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
if self.use_mc_token_ids:
_A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
_A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __A ( self: Optional[int] ) -> List[Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __A ( self: Union[str, Any] , __A: Union[str, Any] , __A: Dict , __A: Optional[int] , __A: List[str] , __A: List[str] , *__A: Optional[int] ) -> Optional[Any]:
_A = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
_A = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __A ( self: Optional[Any] , __A: List[str] , __A: Dict , __A: List[Any] , __A: List[Any] , __A: Any , *__A: Any ) -> str:
_A = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.prepare_config_and_inputs()
(
(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,(
_A
) ,
) = config_and_inputs
_A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def __A ( self: List[str] , __A: Dict , __A: Dict , __A: Tuple , __A: List[Any] , *__A: Optional[int] ) -> Any:
_A = self.num_labels
_A = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A_ = (CTRLLMHeadModel,) if is_torch_available() else ()
A_ = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ = True
A_ = False
A_ = False
def __A ( self: Any , __A: List[Any] , __A: int , __A: Optional[Any] , __A: Optional[int] , __A: List[Any] ) -> List[str]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __A ( self: Any ) -> Union[str, Any]:
_A = CTRLModelTester(self )
_A = ConfigTester(self , config_class=__A , n_embd=37 )
def __A ( self: Optional[int] ) -> List[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __A ( self: Dict ) -> Any:
self.config_tester.run_common_tests()
def __A ( self: str ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __A ( self: List[str] ) -> Any:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: Optional[Any] ) -> int:
pass
@slow
def __A ( self: Tuple ) -> Dict:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def __A ( self: Any ) -> Union[str, Any]:
pass
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: int ) -> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __A ( self: Any ) -> Any:
_A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(__A )
_A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__A ) # Legal the president is
_A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_A = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , __A: Dict , __A: List[str]=13 , __A: Any=3 , __A: List[Any]=2_24 , __A: List[str]=30 , __A: List[str]=4_00 , __A: Optional[int]=True , __A: Optional[Any]=None , __A: Union[str, Any]=True , __A: List[str]=[0.5, 0.5, 0.5] , __A: Optional[Any]=[0.5, 0.5, 0.5] , ) -> Optional[int]:
_A = size if size is not None else {'''height''': 18, '''width''': 18}
_A = parent
_A = batch_size
_A = num_channels
_A = image_size
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
def __A ( self: Any ) -> Optional[int]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = ViTImageProcessor if is_vision_available() else None
def __A ( self: List[Any] ) -> Optional[int]:
_A = EfficientFormerImageProcessorTester(self )
@property
def __A ( self: int ) -> Tuple:
return self.image_proc_tester.prepare_image_processor_dict()
def __A ( self: Union[str, Any] ) -> str:
_A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , '''image_mean''' ) )
self.assertTrue(hasattr(__A , '''image_std''' ) )
self.assertTrue(hasattr(__A , '''do_normalize''' ) )
self.assertTrue(hasattr(__A , '''do_resize''' ) )
self.assertTrue(hasattr(__A , '''size''' ) )
def __A ( self: List[Any] ) -> int:
pass
def __A ( self: Optional[Any] ) -> Tuple:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_proc_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_A = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
_A = image_processor(__A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __A ( self: List[Any] ) -> Optional[int]:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_proc_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
# Test not batched input
_A = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
_A = image_processor(__A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __A ( self: List[Any] ) -> Any:
# Initialize image_processor
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_proc_tester , equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input
_A = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
_A = image_processor(__A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
| 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowercase , _lowercase , _lowercase )
order.append(_lowercase )
return order
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowercase , _lowercase , _lowercase )
return component
def __A ( _lowercase ):
'''simple docstring'''
_A = len(_lowercase ) * [False]
_A = {vert: [] for vert in range(len(_lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowercase )
_A = []
for i, was_visited in enumerate(_lowercase ):
if not was_visited:
order += topology_sort(_lowercase , _lowercase , _lowercase )
_A = []
_A = len(_lowercase ) * [False]
for i in range(len(_lowercase ) ):
_A = order[len(_lowercase ) - i - 1]
if not visited[vert]:
_A = find_components(_lowercase , _lowercase , _lowercase )
components_list.append(_lowercase )
return components_list
| 62 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BertJapaneseTokenizer
A_ = False
A_ = True
def __A ( self: int ) -> int:
super().setUp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''こんにちは''',
'''こん''',
'''にちは''',
'''ばんは''',
'''##こん''',
'''##にちは''',
'''##ばんは''',
'''世界''',
'''##世界''',
'''、''',
'''##、''',
'''。''',
'''##。''',
]
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __A ( self: Dict , __A: List[Any] ) -> List[str]:
_A = '''こんにちは、世界。 \nこんばんは、世界。'''
_A = '''こんにちは 、 世界 。 こんばんは 、 世界 。'''
return input_text, output_text
def __A ( self: List[str] , __A: str ) -> Dict:
_A ,_A = self.get_input_output_texts(__A )
_A = tokenizer.encode(__A , add_special_tokens=__A )
_A = tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
return text, ids
def __A ( self: Union[str, Any] ) -> List[str]:
pass # TODO add if relevant
def __A ( self: List[str] ) -> List[str]:
pass # TODO add if relevant
def __A ( self: str ) -> List[Any]:
pass # TODO add if relevant
def __A ( self: int ) -> Optional[Any]:
_A = self.tokenizer_class(self.vocab_file )
_A = tokenizer.tokenize('''こんにちは、世界。\nこんばんは、世界。''' )
self.assertListEqual(__A , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def __A ( self: Tuple ) -> Union[str, Any]:
_A = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''mecab''' )
self.assertIsNotNone(__A )
_A = '''こんにちは、世界。\nこんばんは、世界。'''
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_A = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(__A , '''wb''' ) as handle:
pickle.dump(__A , __A )
with open(__A , '''rb''' ) as handle:
_A = pickle.load(__A )
_A = tokenizer_new.tokenize(__A )
self.assertListEqual(__A , __A )
def __A ( self: Any ) -> List[str]:
_A = MecabTokenizer(mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __A ( self: Optional[int] ) -> Union[str, Any]:
try:
_A = MecabTokenizer(mecab_dic='''unidic_lite''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __A ( self: int ) -> Any:
try:
_A = MecabTokenizer(mecab_dic='''unidic''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __A ( self: Optional[int] ) -> int:
_A = MecabTokenizer(do_lower_case=__A , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iphone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __A ( self: Optional[Any] ) -> Any:
try:
_A = MecabTokenizer(
do_lower_case=__A , normalize_text=__A , mecab_option='''-d /usr/local/lib/mecab/dic/jumandic''' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
def __A ( self: List[Any] ) -> List[Any]:
_A = MecabTokenizer(normalize_text=__A , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。'''] , )
@require_sudachi
def __A ( self: int ) -> Any:
_A = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''sudachi''' )
self.assertIsNotNone(__A )
_A = '''こんにちは、世界。\nこんばんは、世界。'''
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_A = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(__A , '''wb''' ) as handle:
pickle.dump(__A , __A )
with open(__A , '''rb''' ) as handle:
_A = pickle.load(__A )
_A = tokenizer_new.tokenize(__A )
self.assertListEqual(__A , __A )
@require_sudachi
def __A ( self: Union[str, Any] ) -> Dict:
_A = SudachiTokenizer(sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __A ( self: Optional[Any] ) -> Union[str, Any]:
_A = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''A''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国''', '''人''', '''参政''', '''権'''] )
@require_sudachi
def __A ( self: str ) -> Optional[int]:
_A = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''B''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人''', '''参政権'''] )
@require_sudachi
def __A ( self: Tuple ) -> Any:
_A = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''C''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人参政権'''] )
@require_sudachi
def __A ( self: Optional[int] ) -> int:
_A = SudachiTokenizer(do_lower_case=__A , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __A ( self: Tuple ) -> List[Any]:
_A = SudachiTokenizer(normalize_text=__A , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''ｱｯﾌﾟﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', '''\u3000''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __A ( self: Optional[int] ) -> List[Any]:
_A = SudachiTokenizer(trim_whitespace=__A , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
@require_jumanpp
def __A ( self: str ) -> Optional[int]:
_A = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''jumanpp''' )
self.assertIsNotNone(__A )
_A = '''こんにちは、世界。\nこんばんは、世界。'''
_A = tokenizer.tokenize(__A )
self.assertListEqual(__A , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_A = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(__A , '''wb''' ) as handle:
pickle.dump(__A , __A )
with open(__A , '''rb''' ) as handle:
_A = pickle.load(__A )
_A = tokenizer_new.tokenize(__A )
self.assertListEqual(__A , __A )
@require_jumanpp
def __A ( self: List[str] ) -> Optional[Any]:
_A = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __A ( self: Optional[Any] ) -> Dict:
_A = JumanppTokenizer(do_lower_case=__A )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __A ( self: List[Any] ) -> Optional[Any]:
_A = JumanppTokenizer(normalize_text=__A )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱ''', '''ｯ''', '''ﾌ''', '''ﾟ''', '''ﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __A ( self: str ) -> Union[str, Any]:
_A = JumanppTokenizer(trim_whitespace=__A )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れた''', '''。'''] , )
@require_jumanpp
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('''ありがとうございますm(_ _)ｍ見つけるのが大変です。''' ) , ['''ありがとう''', '''ございます''', '''m(_ _)m''', '''見つける''', '''の''', '''が''', '''大変です''', '''。'''] , )
def __A ( self: Optional[Any] ) -> Union[str, Any]:
_A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こんにちは''', '''こん''', '''にちは''', '''ばんは''', '''##こん''', '''##にちは''', '''##ばんは''']
_A = {}
for i, token in enumerate(__A ):
_A = i
_A = WordpieceTokenizer(vocab=__A , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こんにちは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは''' ) , ['''こん''', '''##ばんは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは こんばんにちは こんにちは''' ) , ['''こん''', '''##ばんは''', '''[UNK]''', '''こんにちは'''] )
def __A ( self: Union[str, Any] ) -> str:
_A = BertJapaneseTokenizer.from_pretrained('''nlp-waseda/roberta-base-japanese-with-auto-jumanpp''' )
_A = tokenizer.subword_tokenizer
_A = subword_tokenizer.tokenize('''国境 の 長い トンネル を 抜ける と 雪国 であった 。''' )
self.assertListEqual(__A , ['''▁国境''', '''▁の''', '''▁長い''', '''▁トンネル''', '''▁を''', '''▁抜ける''', '''▁と''', '''▁雪''', '''国''', '''▁であった''', '''▁。'''] )
_A = subword_tokenizer.tokenize('''こんばんは こんばん にち は こんにちは''' )
self.assertListEqual(__A , ['''▁こん''', '''ばん''', '''は''', '''▁こん''', '''ばん''', '''▁に''', '''ち''', '''▁は''', '''▁こんにちは'''] )
def __A ( self: List[str] ) -> Union[str, Any]:
_A = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese''' )
_A = tokenizer.encode('''ありがとう。''' , add_special_tokens=__A )
_A = tokenizer.encode('''どういたしまして。''' , add_special_tokens=__A )
_A = tokenizer.build_inputs_with_special_tokens(__A )
_A = tokenizer.build_inputs_with_special_tokens(__A , __A )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = BertJapaneseTokenizer
A_ = False
def __A ( self: Optional[Any] ) -> Union[str, Any]:
super().setUp()
_A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __A ( self: Optional[Any] , **__A: str ) -> Dict:
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='''character''' , **__A )
def __A ( self: List[str] , __A: Union[str, Any] ) -> Optional[int]:
_A = '''こんにちは、世界。 \nこんばんは、世界。'''
_A = '''こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'''
return input_text, output_text
def __A ( self: int ) -> List[str]:
pass # TODO add if relevant
def __A ( self: Dict ) -> Optional[int]:
pass # TODO add if relevant
def __A ( self: List[Any] ) -> Optional[int]:
pass # TODO add if relevant
def __A ( self: Optional[int] ) -> List[str]:
_A = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='''character''' )
_A = tokenizer.tokenize('''こんにちは、世界。 \nこんばんは、世界。''' )
self.assertListEqual(
__A , ['''こ''', '''ん''', '''に''', '''ち''', '''は''', '''、''', '''世''', '''界''', '''。''', '''こ''', '''ん''', '''ば''', '''ん''', '''は''', '''、''', '''世''', '''界''', '''。'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def __A ( self: Tuple ) -> Any:
_A = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
_A = {}
for i, token in enumerate(__A ):
_A = i
_A = CharacterTokenizer(vocab=__A , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''は'''] )
self.assertListEqual(tokenizer.tokenize('''こんにちほ''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''[UNK]'''] )
def __A ( self: Optional[Any] ) -> Dict:
_A = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese-char''' )
_A = tokenizer.encode('''ありがとう。''' , add_special_tokens=__A )
_A = tokenizer.encode('''どういたしまして。''' , add_special_tokens=__A )
_A = tokenizer.build_inputs_with_special_tokens(__A )
_A = tokenizer.build_inputs_with_special_tokens(__A , __A )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Union[str, Any] ) -> List[Any]:
_A = '''cl-tohoku/bert-base-japanese'''
_A = AutoTokenizer.from_pretrained(__A )
self.assertIsInstance(__A , __A )
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Dict ) -> List[Any]:
_A = '''cl-tohoku/bert-base-japanese'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertTokenizer.from_pretrained(__A )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
_A = '''bert-base-cased'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertJapaneseTokenizer.from_pretrained(__A )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase )
else:
_A = max(
mf_knapsack(i - 1 , _lowercase , _lowercase , _lowercase ) , mf_knapsack(i - 1 , _lowercase , _lowercase , j - wt[i - 1] ) + val[i - 1] , )
_A = val
return f[i][j]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
_A = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
_A = dp[i - 1][w_]
return dp[n][w_], dp
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , (list, tuple) ) and isinstance(_lowercase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
_A = len(_lowercase )
if num_items != len(_lowercase ):
_A = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(_lowercase )} values"""
)
raise ValueError(_lowercase )
for i in range(_lowercase ):
if not isinstance(wt[i] , _lowercase ):
_A = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(_lowercase )
_A ,_A = knapsack(_lowercase , _lowercase , _lowercase , _lowercase )
_A = set()
_construct_solution(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
return optimal_val, example_optional_set
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_lowercase , _lowercase , i - 1 , _lowercase , _lowercase )
else:
optimal_set.add(_lowercase )
_construct_solution(_lowercase , _lowercase , i - 1 , j - wt[i - 1] , _lowercase )
if __name__ == "__main__":
__A = [3, 2, 4, 4]
__A = [4, 3, 2, 3]
__A = 4
__A = 6
__A = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 62 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__A = 'hf-internal-testing/tiny-random-bert'
__A = os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
__A = '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Union[str, Any] ) -> Optional[Any]:
_A = cached_file(__A , __A )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(__A ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(__A , __A ) ) )
with open(os.path.join(__A , '''refs''' , '''main''' ) ) as f:
_A = f.read()
self.assertEqual(__A , os.path.join(__A , '''snapshots''' , __A , __A ) )
self.assertTrue(os.path.isfile(__A ) )
# File is cached at the same place the second time.
_A = cached_file(__A , __A )
self.assertEqual(__A , __A )
# Using a specific revision to test the full commit hash.
_A = cached_file(__A , __A , revision='''9b8c223''' )
self.assertEqual(__A , os.path.join(__A , '''snapshots''' , __A , __A ) )
def __A ( self: Optional[Any] ) -> Dict:
with self.assertRaisesRegex(__A , '''is not a valid model identifier''' ):
_A = cached_file('''tiny-random-bert''' , __A )
with self.assertRaisesRegex(__A , '''is not a valid git identifier''' ):
_A = cached_file(__A , __A , revision='''aaaa''' )
with self.assertRaisesRegex(__A , '''does not appear to have a file named''' ):
_A = cached_file(__A , '''conf''' )
def __A ( self: Dict ) -> List[str]:
with self.assertRaisesRegex(__A , '''does not appear to have a file named''' ):
_A = cached_file(__A , '''conf''' )
with open(os.path.join(__A , '''refs''' , '''main''' ) ) as f:
_A = f.read()
self.assertTrue(os.path.isfile(os.path.join(__A , '''.no_exist''' , __A , '''conf''' ) ) )
_A = cached_file(__A , '''conf''' , _raise_exceptions_for_missing_entries=__A )
self.assertIsNone(__A )
_A = cached_file(__A , '''conf''' , local_files_only=__A , _raise_exceptions_for_missing_entries=__A )
self.assertIsNone(__A )
_A = mock.Mock()
_A = 5_00
_A = {}
_A = HTTPError
_A = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=__A ) as mock_head:
_A = cached_file(__A , '''conf''' , _raise_exceptions_for_connection_errors=__A )
self.assertIsNone(__A )
# This check we did call the fake head request
mock_head.assert_called()
def __A ( self: Optional[Any] ) -> Dict:
self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __A ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __A ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __A ) )
def __A ( self: Tuple ) -> Tuple:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('''bert-base-cased''' , '''ahah.txt''' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(__A , '''is not a valid model identifier''' ):
get_file_from_repo('''bert-base-case''' , __A )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(__A , '''is not a valid git identifier''' ):
get_file_from_repo('''bert-base-cased''' , __A , revision='''ahaha''' )
_A = get_file_from_repo('''bert-base-cased''' , __A )
# The name is the cached name which is not very easy to test, so instead we load the content.
_A = json.loads(open(__A , '''r''' ).read() )
self.assertEqual(config['''hidden_size'''] , 7_68 )
def __A ( self: str ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
_A = Path(__A ) / '''a.txt'''
filename.touch()
self.assertEqual(get_file_from_repo(__A , '''a.txt''' ) , str(__A ) )
self.assertIsNone(get_file_from_repo(__A , '''b.txt''' ) )
| 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A = (3 * number) + 1
counter += 1
if inputa not in counters:
_A = counter
if counter > pre_counter:
_A = inputa
_A = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 62 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_A = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_A = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_A = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_A = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_A = []
_A = []
_A = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_A ,_A = [word], len(_lowercase )
_A = max_width - width - len(_lowercase )
answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 62 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , torch.Tensor ):
return image
elif isinstance(_lowercase , PIL.Image.Image ):
_A = [image]
_A = [trans(img.convert('''RGB''' ) ) for img in image]
_A = torch.stack(_lowercase )
return image
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Dict , __A: Optional[int] , __A: int ) -> Optional[Any]:
super().__init__()
# make sure scheduler can always be converted to DDIM
_A = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=__A , scheduler=__A )
def __A ( self: List[Any] , __A: Dict ) -> str:
if strength < 0 or strength > 1:
raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )
def __A ( self: Tuple , __A: str , __A: Tuple , __A: Tuple ) -> str:
# get the original timestep using init_timestep
_A = min(int(num_inference_steps * strength ) , __A )
_A = max(num_inference_steps - init_timestep , 0 )
_A = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __A ( self: Union[str, Any] , __A: Optional[int] , __A: List[str] , __A: List[str] , __A: Dict , __A: str , __A: Tuple=None ) -> List[Any]:
if not isinstance(__A , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__A )}""" )
_A = image.to(device=__A , dtype=__A )
if isinstance(__A , __A ) and len(__A ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(__A )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
_A = init_latents.shape
_A = randn_tensor(__A , generator=__A , device=__A , dtype=__A )
# get latents
print('''add noise to latents at timestep''' , __A )
_A = self.scheduler.add_noise(__A , __A , __A )
_A = init_latents
return latents
@torch.no_grad()
def __call__( self: Dict , __A: Union[torch.FloatTensor, PIL.Image.Image] = None , __A: float = 0.8 , __A: int = 1 , __A: Optional[Union[torch.Generator, List[torch.Generator]]] = None , __A: float = 0.0 , __A: int = 50 , __A: Optional[bool] = None , __A: Optional[str] = "pil" , __A: bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
self.check_inputs(__A )
# 2. Preprocess image
_A = preprocess(__A )
# 3. set timesteps
self.scheduler.set_timesteps(__A , device=self.device )
_A ,_A = self.get_timesteps(__A , __A , self.device )
_A = timesteps[:1].repeat(__A )
# 4. Prepare latent variables
_A = self.prepare_latents(__A , __A , __A , self.unet.dtype , self.device , __A )
_A = latents
# 5. Denoising loop
for t in self.progress_bar(__A ):
# 1. predict noise model_output
_A = self.unet(__A , __A ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_A = self.scheduler.step(
__A , __A , __A , eta=__A , use_clipped_model_output=__A , generator=__A , ).prev_sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
_A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_A = self.numpy_to_pil(__A )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=__A )
| 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A = '\\n Text data.\n Second line of data.'
__A = 'file'
@pytest.fixture(scope='''session''' )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_A = bytes(_lowercase , '''utf-8''' )
with zstd.open(_lowercase , '''wb''' ) as f:
f.write(_lowercase )
return path
@pytest.fixture
def __A ( _lowercase ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _lowercase ) , '''w''' ) as f:
f.write(_lowercase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_A = input_paths[compression_format]
_A = tmp_path / '''cache'''
_A = DownloadConfig(cache_dir=_lowercase , extract_compressed_file=_lowercase )
_A = cached_path(_lowercase , download_config=_lowercase )
with open(_lowercase ) as f:
_A = f.read()
with open(_lowercase ) as f:
_A = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = '''custom_cache'''
_A = '''custom_extracted_dir'''
_A = tmp_path / '''custom_extracted_path'''
if default_extracted:
_A = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _lowercase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_lowercase ) )
_A = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_A = xz_file
_A = (
DownloadConfig(extract_compressed_file=_lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_lowercase )
)
_A = cached_path(_lowercase , download_config=_lowercase )
assert Path(_lowercase ).parent.parts[-2:] == expected
def __A ( _lowercase ):
'''simple docstring'''
_A = str(Path(_lowercase ).resolve() )
assert cached_path(_lowercase ) == text_file
# relative path
_A = str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_lowercase ) == text_file
def __A ( _lowercase ):
'''simple docstring'''
_A = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_lowercase ):
cached_path(_lowercase )
# relative path
_A = '''./__missing_file__.txt'''
with pytest.raises(_lowercase ):
cached_path(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(_lowercase ) as f:
_A = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( ):
'''simple docstring'''
with pytest.raises(_lowercase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
http_get('''https://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
ftp_get('''ftp://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_lowercase ):
fsspec_get('''s3://huggingface.co''' , temp_file=_lowercase )
with pytest.raises(_lowercase ):
fsspec_head('''s3://huggingface.co''' )
| 62 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def __A ( ):
'''simple docstring'''
_A = '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
_A = Image.open(requests.get(_lowercase , stream=_lowercase ).raw ).convert('''RGB''' )
return image
def __A ( _lowercase ):
'''simple docstring'''
_A = []
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''') )
# fmt: on
return rename_keys
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = dct.pop(_lowercase )
_A = val
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_A = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
_A = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
_A = torch.cat((q_bias, torch.zeros_like(_lowercase , requires_grad=_lowercase ), v_bias) )
_A = qkv_bias
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = 3_64 if '''coco''' in model_name else 2_24
_A = BlipaVisionConfig(image_size=_lowercase ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
_A = OPTConfig.from_pretrained('''facebook/opt-2.7b''' , eos_token_id=_lowercase ).to_dict()
elif "opt-6.7b" in model_name:
_A = OPTConfig.from_pretrained('''facebook/opt-6.7b''' , eos_token_id=_lowercase ).to_dict()
elif "t5-xl" in model_name:
_A = TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_A = TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
_A = BlipaConfig(vision_config=_lowercase , text_config=_lowercase )
return config, image_size
@torch.no_grad()
def __A ( _lowercase , _lowercase=None , _lowercase=False ):
'''simple docstring'''
_A = (
AutoTokenizer.from_pretrained('''facebook/opt-2.7b''' )
if '''opt''' in model_name
else AutoTokenizer.from_pretrained('''google/flan-t5-xl''' )
)
_A = tokenizer('''\n''' , add_special_tokens=_lowercase ).input_ids[0]
_A ,_A = get_blipa_config(_lowercase , eos_token_id=_lowercase )
_A = BlipaForConditionalGeneration(_lowercase ).eval()
_A = {
'''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
'''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
'''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
'''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
'''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
'''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
'''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
}
_A ,_A = model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
_A = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_A ,_A ,_A = load_model_and_preprocess(
name=_lowercase , model_type=_lowercase , is_eval=_lowercase , device=_lowercase )
original_model.eval()
print('''Done!''' )
# update state dict keys
_A = original_model.state_dict()
_A = create_rename_keys(_lowercase )
for src, dest in rename_keys:
rename_key(_lowercase , _lowercase , _lowercase )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_A = state_dict.pop(_lowercase )
if key.startswith('''Qformer.bert''' ):
_A = key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
_A = key.replace('''self''' , '''attention''' )
if "opt_proj" in key:
_A = key.replace('''opt_proj''' , '''language_projection''' )
if "t5_proj" in key:
_A = key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''opt''' ):
_A = key.replace('''opt''' , '''language''' )
if key.startswith('''t5''' ):
_A = key.replace('''t5''' , '''language''' )
_A = val
# read in qv biases
read_in_q_v_bias(_lowercase , _lowercase )
_A ,_A = hf_model.load_state_dict(_lowercase , strict=_lowercase )
assert len(_lowercase ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
_A = load_demo_image()
_A = vis_processors['''eval'''](_lowercase ).unsqueeze(0 ).to(_lowercase )
_A = tokenizer(['''\n'''] , return_tensors='''pt''' ).input_ids.to(_lowercase )
# create processor
_A = BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=_lowercase , image_std=_lowercase )
_A = BlipaProcessor(image_processor=_lowercase , tokenizer=_lowercase )
_A = processor(images=_lowercase , return_tensors='''pt''' ).pixel_values.to(_lowercase )
# make sure processor creates exact same pixel values
assert torch.allclose(_lowercase , _lowercase )
original_model.to(_lowercase )
hf_model.to(_lowercase )
with torch.no_grad():
if "opt" in model_name:
_A = original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
_A = hf_model(_lowercase , _lowercase ).logits
else:
_A = original_model(
{'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
_A = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_00 )
_A = hf_model(_lowercase , _lowercase , labels=_lowercase ).logits
assert original_logits.shape == logits.shape
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
_A = torch.tensor(
[[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]] , device=_lowercase )
assert torch.allclose(logits[0, :3, :3] , _lowercase , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
_A = torch.tensor(
[[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]] , device=_lowercase )
else:
# cast to same type
_A = logits.dtype
assert torch.allclose(original_logits.to(_lowercase ) , _lowercase , atol=1e-2 )
print('''Looks ok!''' )
print('''Generating a caption...''' )
_A = ''''''
_A = tokenizer(_lowercase , return_tensors='''pt''' ).input_ids.to(_lowercase )
_A = original_model.generate({'''image''': original_pixel_values} )
_A = hf_model.generate(
_lowercase , _lowercase , do_sample=_lowercase , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('''Original generation:''' , _lowercase )
_A = input_ids.shape[1]
_A = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_lowercase )
_A = [text.strip() for text in output_text]
print('''HF generation:''' , _lowercase )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_lowercase )
hf_model.save_pretrained(_lowercase )
if push_to_hub:
processor.push_to_hub(f"""nielsr/{model_name}""" )
hf_model.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
__A = [
'blip2-opt-2.7b',
'blip2-opt-6.7b',
'blip2-opt-2.7b-coco',
'blip2-opt-6.7b-coco',
'blip2-flan-t5-xl',
'blip2-flan-t5-xl-coco',
'blip2-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='blip2-opt-2.7b',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
__A = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowercase )
for i in range(start * start , end + 1 , _lowercase ):
_A = False
start += 1
prime += in_prime
_A = end + 1
_A = min(2 * end , _lowercase )
while low <= n:
_A = [True] * (high - low + 1)
for each in in_prime:
_A = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowercase , high + 1 , _lowercase ):
_A = False
for j in range(len(_lowercase ) ):
if temp[j] is True:
prime.append(j + low )
_A = high + 1
_A = min(high + end , _lowercase )
return prime
print(sieve(10**6))
| 62 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[Any] , __A: Tuple , __A: Optional[Any]=14 , __A: Optional[Any]=7 , __A: Optional[Any]=True , __A: Optional[int]=True , __A: str=False , __A: Any=True , __A: Optional[Any]=99 , __A: Optional[Any]=32 , __A: Optional[int]=4 , __A: List[Any]=4 , __A: Optional[int]=4 , __A: Dict=37 , __A: Union[str, Any]="gelu" , __A: Any=0.1 , __A: Optional[int]=0.1 , __A: List[Any]=5_12 , __A: List[str]=0.02 , ) -> List[Any]:
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = rotary_dim
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = initializer_range
_A = None
_A = vocab_size - 1
_A = vocab_size - 1
_A = vocab_size - 1
def __A ( self: str ) -> Any:
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=__A , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def __A ( self: Any ) -> List[str]:
_A = self.prepare_config_and_inputs()
_A ,_A ,_A = config_and_inputs
_A = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __A ( self: Dict , __A: int , __A: Tuple , __A: int , __A: int ) -> Dict:
_A = 20
_A = model_class_name(__A )
_A = model.init_cache(input_ids.shape[0] , __A )
_A = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
_A = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_A = model(
input_ids[:, :-1] , attention_mask=__A , past_key_values=__A , position_ids=__A , )
_A = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
_A = model(
input_ids[:, -1:] , attention_mask=__A , past_key_values=outputs_cache.past_key_values , position_ids=__A , )
_A = model(__A )
_A = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def __A ( self: Any , __A: Optional[Any] , __A: Any , __A: Optional[int] , __A: Dict ) -> str:
_A = 20
_A = model_class_name(__A )
_A = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
_A = model.init_cache(input_ids.shape[0] , __A )
_A = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_A = model(
input_ids[:, :-1] , attention_mask=__A , past_key_values=__A , position_ids=__A , )
_A = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
_A = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=__A , position_ids=__A , )
_A = model(__A , attention_mask=__A )
_A = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
A_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def __A ( self: Dict ) -> Dict:
_A = FlaxGPTJModelTester(self )
def __A ( self: Any ) -> Optional[Any]:
for model_class_name in self.all_model_classes:
_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(__A , __A , __A , __A )
def __A ( self: int ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
__A , __A , __A , __A )
@tooslow
def __A ( self: str ) -> Optional[Any]:
_A = GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
_A = tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=__A , truncation=__A )
_A = FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
_A = False
_A = model.config.eos_token_id
_A = jax.jit(model.generate )
_A = jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
_A = tokenizer.batch_decode(__A , skip_special_tokens=__A )
_A = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(__A , __A )
@is_pt_flax_cross_test
def __A ( self: Optional[Any] ) -> Union[str, Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_A = self._prepare_for_class(__A , __A )
_A = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_A = model_class.__name__[4:] # Skip the "Flax" at the beginning
_A = getattr(__A , __A )
_A ,_A = pt_inputs['''input_ids'''].shape
_A = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__A ):
_A = 0
_A = 1
_A = 0
_A = 1
_A = pt_model_class(__A ).eval()
_A = model_class(__A , dtype=jnp.floataa )
_A = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __A )
_A = fx_state
with torch.no_grad():
_A = pt_model(**__A ).to_tuple()
_A = fx_model(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__A )
_A = model_class.from_pretrained(__A , from_pt=__A )
_A = fx_model_loaded(**__A ).to_tuple()
self.assertEqual(
len(__A ) , len(__A ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def __A ( self: List[Any] ) -> List[str]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_A = self._prepare_for_class(__A , __A )
_A = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_A = model_class.__name__[4:] # Skip the "Flax" at the beginning
_A = getattr(__A , __A )
_A = pt_model_class(__A ).eval()
_A = model_class(__A , dtype=jnp.floataa )
_A = load_flax_weights_in_pytorch_model(__A , fx_model.params )
_A ,_A = pt_inputs['''input_ids'''].shape
_A = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__A ):
_A = 0
_A = 1
_A = 0
_A = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_A = pt_model(**__A ).to_tuple()
_A = fx_model(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__A )
_A = pt_model_class.from_pretrained(__A , from_flax=__A )
with torch.no_grad():
_A = pt_model_loaded(**__A ).to_tuple()
self.assertEqual(
len(__A ) , len(__A ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def __A ( self: Tuple ) -> Tuple:
for model_class_name in self.all_model_classes:
_A = model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
_A = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Dict , __A: Dict ) -> Tuple:
_A ,_A ,_A ,_A = hidden_states.shape
_A = jax.image.resize(
__A , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: List[str] ) -> Tuple:
_A = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self: Union[str, Any] , __A: List[Any] ) -> Union[str, Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_A = self.conv(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = None
A_ = 0.0
A_ = None
A_ = jnp.floataa
def __A ( self: Dict ) -> Dict:
_A = self.in_channels if self.out_channels is None else self.out_channels
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = nn.Dense(__A , dtype=self.dtype )
_A = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_A = nn.Dropout(self.dropout_prob )
_A = nn.Conv(
__A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_A = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_A = None
if use_nin_shortcut:
_A = nn.Conv(
__A , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self: Dict , __A: List[Any] , __A: List[Any] , __A: Any=True ) -> List[Any]:
_A = hidden_states
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.conva(__A )
_A = self.time_emb_proj(nn.swish(__A ) )
_A = jnp.expand_dims(jnp.expand_dims(__A , 1 ) , 1 )
_A = hidden_states + temb
_A = self.norma(__A )
_A = nn.swish(__A )
_A = self.dropout(__A , __A )
_A = self.conva(__A )
if self.conv_shortcut is not None:
_A = self.conv_shortcut(__A )
return hidden_states + residual
| 62 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
__A = datasets.logging.get_logger(__name__)
__A = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n booktitle = {Proceedings of the Fifth Conference on Machine Translation},\n month = {November},\n year = {2020},\n address = {Online},\n publisher = {Association for Computational Linguistics},\n pages = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n title = "{COMET}: A Neural Framework for {MT} Evaluation",\n author = "Rei, Ricardo and\n Stewart, Craig and\n Farinha, Ana C and\n Lavie, Alon",\n booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n month = nov,\n year = "2020",\n address = "Online",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n pages = "2685--2702",\n}\n'
__A = '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'
__A = '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n `scores`: List of scores.\n\nExamples:\n\n >>> comet_metric = datasets.load_metric(\'comet\')\n >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use\n >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n >>> print([round(v, 2) for v in results["scores"]])\n [0.19, 0.92]\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __A ( self: Tuple ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def __A ( self: Union[str, Any] , __A: str ) -> Dict:
if self.config_name == "default":
_A = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
_A = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def __A ( self: Tuple , __A: str , __A: Union[str, Any] , __A: Union[str, Any] , __A: Any=None , __A: Optional[int]=False ) -> Dict:
if gpus is None:
_A = 1 if torch.cuda.is_available() else 0
_A = {'''src''': sources, '''mt''': predictions, '''ref''': references}
_A = [dict(zip(__A , __A ) ) for t in zip(*data.values() )]
_A ,_A = self.scorer.predict(__A , gpus=__A , progress_bar=__A )
return {"mean_score": mean_score, "scores": scores}
| 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
if indegree[i] == 0:
queue.append(_lowercase )
while queue:
_A = queue.pop(0 )
cnt += 1
topo.append(_lowercase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(_lowercase )
if cnt != len(_lowercase ):
print('''Cycle exists''' )
else:
print(_lowercase )
# Adjacency List of Graph
__A = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 62 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = AudioLDMPipeline
A_ = TEXT_TO_AUDIO_PARAMS
A_ = TEXT_TO_AUDIO_BATCH_PARAMS
A_ = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
] )
def __A ( self: List[Any] ) -> Dict:
torch.manual_seed(0 )
_A = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=(32, 64) , class_embed_type='''simple_projection''' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=__A , )
_A = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
_A = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
_A = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , projection_dim=32 , )
_A = ClapTextModelWithProjection(__A )
_A = RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta''' , model_max_length=77 )
_A = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_60_00 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=__A , )
_A = SpeechTaHifiGan(__A )
_A = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''vocoder''': vocoder,
}
return components
def __A ( self: Optional[int] , __A: List[str] , __A: List[str]=0 ) -> Tuple:
if str(__A ).startswith('''mps''' ):
_A = torch.manual_seed(__A )
else:
_A = torch.Generator(device=__A ).manual_seed(__A )
_A = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
}
return inputs
def __A ( self: int ) -> Dict:
_A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs(__A )
_A = audioldm_pipe(**__A )
_A = output.audios[0]
assert audio.ndim == 1
assert len(__A ) == 2_56
_A = audio[:10]
_A = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def __A ( self: Optional[Any] ) -> Tuple:
_A = self.get_dummy_components()
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs(__A )
_A = 3 * [inputs['''prompt''']]
# forward
_A = audioldm_pipe(**__A )
_A = output.audios[0]
_A = self.get_dummy_inputs(__A )
_A = 3 * [inputs.pop('''prompt''' )]
_A = audioldm_pipe.tokenizer(
__A , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__A , return_tensors='''pt''' , )
_A = text_inputs['''input_ids'''].to(__A )
_A = audioldm_pipe.text_encoder(
__A , )
_A = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_A = F.normalize(__A , dim=-1 )
_A = prompt_embeds
# forward
_A = audioldm_pipe(**__A )
_A = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def __A ( self: Union[str, Any] ) -> Optional[Any]:
_A = self.get_dummy_components()
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs(__A )
_A = 3 * ['''this is a negative prompt''']
_A = negative_prompt
_A = 3 * [inputs['''prompt''']]
# forward
_A = audioldm_pipe(**__A )
_A = output.audios[0]
_A = self.get_dummy_inputs(__A )
_A = 3 * [inputs.pop('''prompt''' )]
_A = []
for p in [prompt, negative_prompt]:
_A = audioldm_pipe.tokenizer(
__A , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__A , return_tensors='''pt''' , )
_A = text_inputs['''input_ids'''].to(__A )
_A = audioldm_pipe.text_encoder(
__A , )
_A = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_A = F.normalize(__A , dim=-1 )
embeds.append(__A )
_A ,_A = embeds
# forward
_A = audioldm_pipe(**__A )
_A = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def __A ( self: int ) -> str:
_A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = PNDMScheduler(skip_prk_steps=__A )
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_dummy_inputs(__A )
_A = '''egg cracking'''
_A = audioldm_pipe(**__A , negative_prompt=__A )
_A = output.audios[0]
assert audio.ndim == 1
assert len(__A ) == 2_56
_A = audio[:10]
_A = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def __A ( self: Optional[Any] ) -> Union[str, Any]:
_A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = PNDMScheduler(skip_prk_steps=__A )
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = '''A hammer hitting a wooden surface'''
# test num_waveforms_per_prompt=1 (default)
_A = audioldm_pipe(__A , num_inference_steps=2 ).audios
assert audios.shape == (1, 2_56)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_A = 2
_A = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 2_56)
# test num_waveforms_per_prompt for single prompt
_A = 2
_A = audioldm_pipe(__A , num_inference_steps=2 , num_waveforms_per_prompt=__A ).audios
assert audios.shape == (num_waveforms_per_prompt, 2_56)
# test num_waveforms_per_prompt for batch of prompts
_A = 2
_A = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=__A ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)
def __A ( self: Union[str, Any] ) -> Any:
_A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = audioldm_pipe.vocoder.config.sampling_rate
_A = self.get_dummy_inputs(__A )
_A = audioldm_pipe(audio_length_in_s=0.016 , **__A )
_A = output.audios[0]
assert audio.ndim == 1
assert len(__A ) / vocoder_sampling_rate == 0.016
_A = audioldm_pipe(audio_length_in_s=0.032 , **__A )
_A = output.audios[0]
assert audio.ndim == 1
assert len(__A ) / vocoder_sampling_rate == 0.032
def __A ( self: str ) -> Any:
_A = self.get_dummy_components()
_A = AudioLDMPipeline(**__A )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = ['''hey''']
_A = audioldm_pipe(__A , num_inference_steps=1 )
_A = output.audios.shape
assert audio_shape == (1, 2_56)
_A = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_A = SpeechTaHifiGan(__A ).to(__A )
_A = audioldm_pipe(__A , num_inference_steps=1 )
_A = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 2_56)
def __A ( self: Union[str, Any] ) -> Optional[Any]:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__A )
def __A ( self: int ) -> Union[str, Any]:
self._test_inference_batch_single_identical(test_mean_pixel_difference=__A )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __A ( self: Dict ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__A )
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: List[str] ) -> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self: int , __A: Optional[Any] , __A: str="cpu" , __A: Dict=torch.floataa , __A: Union[str, Any]=0 ) -> Any:
_A = torch.Generator(device=__A ).manual_seed(__A )
_A = np.random.RandomState(__A ).standard_normal((1, 8, 1_28, 16) )
_A = torch.from_numpy(__A ).to(device=__A , dtype=__A )
_A = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 2.5,
}
return inputs
def __A ( self: Any ) -> int:
_A = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_inputs(__A )
_A = 25
_A = audioldm_pipe(**__A ).audios[0]
assert audio.ndim == 1
assert len(__A ) == 8_19_20
_A = audio[7_72_30:7_72_40]
_A = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
_A = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def __A ( self: Optional[Any] ) -> str:
_A = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
_A = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
_A = audioldm_pipe.to(__A )
audioldm_pipe.set_progress_bar_config(disable=__A )
_A = self.get_inputs(__A )
_A = audioldm_pipe(**__A ).audios[0]
assert audio.ndim == 1
assert len(__A ) == 8_19_20
_A = audio[2_77_80:2_77_90]
_A = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
_A = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""simple docstring"""
A_ = 1
@register_to_config
def __init__( self: Any , __A: int = 10_00 , __A: Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__A )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def __A ( self: str , __A: int , __A: Union[str, torch.device] = None ) -> int:
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(__A )
_A = []
def __A ( self: Tuple , __A: torch.FloatTensor , __A: int , __A: torch.FloatTensor , __A: bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__A )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(__A , __A , __A , __A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __A ( self: Optional[int] , __A: torch.FloatTensor , *__A: Tuple , **__A: List[Any] ) -> torch.FloatTensor:
return sample
def __A ( self: List[str] , __A: Optional[Any] , __A: Optional[Any] , __A: Any , __A: List[Any] ) -> List[Any]:
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(__A , 1e-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self: List[str] ) -> Dict:
return self.config.num_train_timesteps
| 62 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ):
"""simple docstring"""
A_ = [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self: int , __A: int , __A: int , __A: Optional[int] = None , __A: int = 5_02_57 , __A: int = 10_24 , __A: int = 7_68 , __A: int = 12 , __A: int = 12 , __A: Optional[int] = None , __A: str = "gelu_new" , __A: float = 0.1 , __A: float = 0.1 , __A: float = 0.1 , __A: float = 1e-5 , __A: float = 0.02 , __A: bool = True , __A: bool = True , __A: bool = False , __A: bool = False , ) -> Union[str, Any]:
super().__init__()
_A = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
_A = prefix_inner_dim
_A = prefix_hidden_dim
_A = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_A = (
nn.Linear(self.prefix_hidden_dim , __A ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_A = GPTaConfig(
vocab_size=__A , n_positions=__A , n_embd=__A , n_layer=__A , n_head=__A , n_inner=__A , activation_function=__A , resid_pdrop=__A , embd_pdrop=__A , attn_pdrop=__A , layer_norm_epsilon=__A , initializer_range=__A , scale_attn_weights=__A , use_cache=__A , scale_attn_by_inverse_layer_idx=__A , reorder_and_upcast_attn=__A , )
_A = GPTaLMHeadModel(__A )
def __A ( self: Any , __A: torch.Tensor , __A: torch.Tensor , __A: Optional[torch.Tensor] = None , __A: Optional[torch.Tensor] = None , ) -> List[Any]:
_A = self.transformer.transformer.wte(__A )
_A = self.encode_prefix(__A )
_A = self.decode_prefix(__A )
_A = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_A = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_A = torch.cat((dummy_token, input_ids) , dim=1 )
_A = self.transformer(inputs_embeds=__A , labels=__A , attention_mask=__A )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __A ( self: Union[str, Any] , __A: int , __A: torch.device ) -> torch.Tensor:
return torch.zeros(__A , self.prefix_length , dtype=torch.intaa , device=__A )
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Any:
return self.encode_prefix(__A )
@torch.no_grad()
def __A ( self: Any , __A: Dict , __A: Tuple , __A: str ) -> Tuple:
_A = torch.split(__A , 1 , dim=0 )
_A = []
_A = []
for feature in features:
_A = self.decode_prefix(feature.to(__A ) ) # back to the clip feature
# Only support beam search for now
_A ,_A = self.generate_beam(
input_embeds=__A , device=__A , eos_token_id=__A )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_A = torch.stack(__A )
_A = torch.stack(__A )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __A ( self: str , __A: str=None , __A: Any=None , __A: Dict=None , __A: int = 5 , __A: int = 67 , __A: float = 1.0 , __A: Optional[int] = None , ) -> Optional[Any]:
_A = eos_token_id
_A = None
_A = None
_A = torch.ones(__A , device=__A , dtype=torch.int )
_A = torch.zeros(__A , device=__A , dtype=torch.bool )
if input_embeds is not None:
_A = input_embeds
else:
_A = self.transformer.transformer.wte(__A )
for i in range(__A ):
_A = self.transformer(inputs_embeds=__A )
_A = outputs.logits
_A = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_A = logits.softmax(-1 ).log()
if scores is None:
_A ,_A = logits.topk(__A , -1 )
_A = generated.expand(__A , *generated.shape[1:] )
_A ,_A = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_A = next_tokens
else:
_A = tokens.expand(__A , *tokens.shape[1:] )
_A = torch.cat((tokens, next_tokens) , dim=1 )
else:
_A = -float(np.inf )
_A = 0
_A = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_A = scores_sum / seq_lengths[:, None]
_A ,_A = scores_sum_average.view(-1 ).topk(__A , -1 )
_A = next_tokens // scores_sum.shape[1]
_A = seq_lengths[next_tokens_source]
_A = next_tokens % scores_sum.shape[1]
_A = next_tokens.unsqueeze(1 )
_A = tokens[next_tokens_source]
_A = torch.cat((tokens, next_tokens) , dim=1 )
_A = generated[next_tokens_source]
_A = scores_sum_average * seq_lengths
_A = is_stopped[next_tokens_source]
_A = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_A = torch.cat((generated, next_token_embed) , dim=1 )
_A = is_stopped + next_tokens.eq(__A ).squeeze()
if is_stopped.all():
break
_A = scores / seq_lengths
_A = scores.argsort(descending=__A )
# tokens tensors are already padded to max_seq_length
_A = [tokens[i] for i in order]
_A = torch.stack(__A , dim=0 )
_A = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_A = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 1 |
__A = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',
'datasets': 'datasets!=2.5.0',
'decord': 'decord==0.6.0',
'deepspeed': 'deepspeed>=0.9.3',
'diffusers': 'diffusers',
'dill': 'dill<0.3.5',
'evaluate': 'evaluate>=0.2.0',
'fairscale': 'fairscale>0.3',
'faiss-cpu': 'faiss-cpu',
'fastapi': 'fastapi',
'filelock': 'filelock',
'flax': 'flax>=0.4.1,<=0.7.0',
'ftfy': 'ftfy',
'fugashi': 'fugashi>=1.0',
'GitPython': 'GitPython<3.1.19',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
'importlib_metadata': 'importlib_metadata',
'ipadic': 'ipadic>=1.0.0,<2.0',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
'jieba': 'jieba',
'kenlm': 'kenlm',
'keras-nlp': 'keras-nlp>=0.3.1',
'librosa': 'librosa',
'nltk': 'nltk',
'natten': 'natten>=0.14.6',
'numpy': 'numpy>=1.17',
'onnxconverter-common': 'onnxconverter-common',
'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
'onnxruntime': 'onnxruntime>=1.4.0',
'opencv-python': 'opencv-python',
'optuna': 'optuna',
'optax': 'optax>=0.0.8,<=0.1.4',
'packaging': 'packaging>=20.0',
'parameterized': 'parameterized',
'phonemizer': 'phonemizer',
'protobuf': 'protobuf',
'psutil': 'psutil',
'pyyaml': 'pyyaml>=5.1',
'pydantic': 'pydantic<2',
'pytest': 'pytest>=7.2.0',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'python': 'python>=3.8.0',
'ray[tune]': 'ray[tune]',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
'rjieba': 'rjieba',
'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
'ruff': 'ruff>=0.0.241,<=0.0.259',
'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
'sacremoses': 'sacremoses',
'safetensors': 'safetensors>=0.3.1',
'sagemaker': 'sagemaker>=2.31.0',
'scikit-learn': 'scikit-learn',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'sigopt': 'sigopt',
'starlette': 'starlette',
'sudachipy': 'sudachipy>=0.6.6',
'sudachidict_core': 'sudachidict_core>=20220729',
'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
'tensorflow': 'tensorflow>=2.6,<2.14',
'tensorflow-text': 'tensorflow-text<2.14',
'tf2onnx': 'tf2onnx',
'timeout-decorator': 'timeout-decorator',
'timm': 'timm',
'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
'torch': 'torch>=1.9,!=1.12.0',
'torchaudio': 'torchaudio',
'torchvision': 'torchvision',
'pyctcdecode': 'pyctcdecode>=0.4.0',
'tqdm': 'tqdm>=4.27',
'unidic': 'unidic>=1.0.2',
'unidic_lite': 'unidic_lite>=1.0.7',
'urllib3': 'urllib3<2.0.0',
'uvicorn': 'uvicorn',
}
| 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__A = NewType('DataClass', Any)
__A = NewType('DataClassType', Any)
def __A ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __A ( _lowercase ):
'''simple docstring'''
_A = {str(_lowercase ): choice for choice in choices}
return lambda _lowercase : str_to_choice.get(_lowercase , _lowercase )
def __A ( *,
_lowercase = None , _lowercase = None , _lowercase = dataclasses.MISSING , _lowercase = dataclasses.MISSING , _lowercase = None , **_lowercase , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_lowercase , default=_lowercase , default_factory=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = 42
def __init__( self: Optional[Any] , __A: Union[DataClassType, Iterable[DataClassType]] , **__A: List[Any] ) -> str:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**__A )
if dataclasses.is_dataclass(__A ):
_A = [dataclass_types]
_A = list(__A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__A )
@staticmethod
def __A ( __A: ArgumentParser , __A: dataclasses.Field ) -> str:
_A = f"""--{field.name}"""
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __A ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
_A = kwargs.pop('''aliases''' , [] )
if isinstance(__A , __A ):
_A = [aliases]
_A = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__A , '''UnionType''' ) and isinstance(__A , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__A ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__A ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(__A , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , __A ) and issubclass(field.type , __A )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(__A )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '''?'''
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(__A ) and issubclass(__A , __A ):
_A = field.type.__args__[0]
_A = '''+'''
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(__A , *__A , **__A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__A )
def __A ( self: Dict , __A: DataClassType ) -> List[Any]:
if hasattr(__A , '''_argument_group_name''' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(__A )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A ):
_A = '''.'''.join(map(__A , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__A ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(__A , __A )
def __A ( self: int , __A: Any=None , __A: int=False , __A: Any=True , __A: Optional[Any]=None , __A: Any=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(__A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(__A , type=__A , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A ,_A = args_file_parser.parse_known_args(args=__A )
_A = vars(__A ).get(args_file_flag.lstrip('''-''' ) , __A )
if cmd_args_file_paths:
args_files.extend([Path(__A ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A ,_A = self.parse_known_args(args=__A )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in vars(__A ).items() if k in keys}
for k in keys:
delattr(__A , __A )
_A = dtype(**__A )
outputs.append(__A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def __A ( self: Tuple , __A: Dict[str, Any] , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(__A ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**__A )
outputs.append(__A )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A )}""" )
return tuple(__A )
def __A ( self: Tuple , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
with open(Path(__A ) , encoding='''utf-8''' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(__A , allow_extra_keys=__A )
return tuple(__A )
def __A ( self: List[Any] , __A: str , __A: bool = False ) -> Tuple[DataClass, ...]:
_A = self.parse_dict(yaml.safe_load(Path(__A ).read_text() ) , allow_extra_keys=__A )
return tuple(__A )
| 62 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Optional[Any] , __A: AutoencoderKL , __A: CLIPTextModel , __A: CLIPTokenizer , __A: UNetaDConditionModel , __A: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __A: StableDiffusionSafetyChecker , __A: CLIPImageProcessor , ) -> Optional[Any]:
super().__init__()
self.register_modules(
vae=__A , text_encoder=__A , tokenizer=__A , unet=__A , scheduler=__A , safety_checker=__A , feature_extractor=__A , )
def __A ( self: List[str] , __A: Optional[Union[str, int]] = "auto" ) -> Union[str, Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_A = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__A )
def __A ( self: List[str] ) -> Tuple:
self.enable_attention_slicing(__A )
@torch.no_grad()
def __call__( self: Dict , __A: Union[str, List[str]] , __A: int = 5_12 , __A: int = 5_12 , __A: int = 50 , __A: float = 7.5 , __A: Optional[Union[str, List[str]]] = None , __A: Optional[int] = 1 , __A: float = 0.0 , __A: Optional[torch.Generator] = None , __A: Optional[torch.FloatTensor] = None , __A: Optional[str] = "pil" , __A: bool = True , __A: Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __A: int = 1 , __A: Optional[torch.FloatTensor] = None , **__A: List[str] , ) -> int:
if isinstance(__A , __A ):
_A = 1
elif isinstance(__A , __A ):
_A = len(__A )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__A )}""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__A , __A ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__A )}.""" )
# get prompt text embeddings
_A = self.tokenizer(
__A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
_A = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_A = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
_A = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
_A = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
_A ,_A ,_A = text_embeddings.shape
_A = text_embeddings.repeat(1 , __A , 1 )
_A = text_embeddings.view(bs_embed * num_images_per_prompt , __A , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_A = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_A = 42
if negative_prompt is None:
_A = ['''''']
elif type(__A ) is not type(__A ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(__A )} !="""
f""" {type(__A )}.""" )
elif isinstance(__A , __A ):
_A = [negative_prompt]
elif batch_size != len(__A ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(__A )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
''' the batch size of `prompt`.''' )
else:
_A = negative_prompt
_A = text_input_ids.shape[-1]
_A = self.tokenizer(
__A , padding='''max_length''' , max_length=__A , truncation=__A , return_tensors='''pt''' , )
_A = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_A = uncond_embeddings.shape[1]
_A = uncond_embeddings.repeat(__A , __A , 1 )
_A = uncond_embeddings.view(batch_size * num_images_per_prompt , __A , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_A = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_A = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
_A = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
_A = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
_A = torch.randn(
__A , generator=__A , device='''cpu''' , dtype=__A ).to(self.device )
_A = torch.randn(__A , generator=__A , device='''cpu''' , dtype=__A ).to(
self.device )
else:
_A = torch.randn(
__A , generator=__A , device=self.device , dtype=__A )
_A = torch.randn(__A , generator=__A , device=self.device , dtype=__A )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
_A = latents_reference.to(self.device )
_A = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
_A = (latents_shape[3] - latents_shape_reference[3]) // 2
_A = (latents_shape[2] - latents_shape_reference[2]) // 2
_A = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
_A = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
_A = 0 if dx < 0 else dx
_A = 0 if dy < 0 else dy
_A = max(-dx , 0 )
_A = max(-dy , 0 )
# import pdb
# pdb.set_trace()
_A = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(__A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
_A = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_A = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_A = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_A = {}
if accepts_eta:
_A = eta
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
_A = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A = self.scheduler.scale_model_input(__A , __A )
# predict the noise residual
_A = self.unet(__A , __A , encoder_hidden_states=__A ).sample
# perform guidance
if do_classifier_free_guidance:
_A ,_A = noise_pred.chunk(2 )
_A = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(__A , __A , __A , **__A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__A , __A , __A )
_A = 1 / 0.18_215 * latents
_A = self.vae.decode(__A ).sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_A = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
_A = self.feature_extractor(self.numpy_to_pil(__A ) , return_tensors='''pt''' ).to(
self.device )
_A ,_A = self.safety_checker(
images=__A , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
_A = None
if output_type == "pil":
_A = self.numpy_to_pil(__A )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=__A , nsfw_content_detected=__A )
| 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[int] , __A: Union[str, Any] , __A: int=2 , __A: List[str]=True , __A: List[Any]=False , __A: Union[str, Any]=10 , __A: Optional[int]=3 , __A: List[Any]=32 * 4 , __A: Dict=32 * 6 , __A: Optional[Any]=4 , __A: Any=32 , ) -> str:
_A = parent
_A = batch_size
_A = is_training
_A = use_auxiliary_loss
_A = num_queries
_A = num_channels
_A = min_size
_A = max_size
_A = num_labels
_A = mask_feature_size
def __A ( self: Dict ) -> Optional[int]:
_A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
_A = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
_A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
_A = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
_A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __A ( self: Optional[Any] ) -> Tuple:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __A ( self: Dict ) -> Tuple:
_A ,_A ,_A ,_A ,_A = self.prepare_config_and_inputs()
_A = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def __A ( self: Optional[int] , __A: Union[str, Any] , __A: Dict ) -> int:
_A = output.encoder_hidden_states
_A = output.pixel_decoder_hidden_states
_A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_config.decoder_layers )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Any , __A: Dict=False ) -> Any:
with torch.no_grad():
_A = MaskFormerModel(config=__A )
model.to(__A )
model.eval()
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A , output_hidden_states=__A )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def __A ( self: Optional[Any] , __A: Union[str, Any] , __A: Optional[Any] , __A: Union[str, Any] , __A: Union[str, Any] , __A: List[Any] ) -> int:
_A = MaskFormerForInstanceSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A: int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_A = model(pixel_values=__A , pixel_mask=__A )
_A = model(__A )
comm_check_on_output(__A )
_A = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
A_ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
A_ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def __A ( self: int ) -> Tuple:
_A = MaskFormerModelTester(self )
_A = ConfigTester(self , config_class=__A , has_text_modality=__A )
def __A ( self: List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def __A ( self: Optional[Any] ) -> int:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Dict ) -> Optional[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def __A ( self: int ) -> Tuple:
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def __A ( self: List[Any] ) -> Any:
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def __A ( self: Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def __A ( self: int ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __A ( self: Union[str, Any] ) -> List[Any]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self: List[Any] ) -> Any:
pass
def __A ( self: Dict ) -> Optional[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __A )
@slow
def __A ( self: int ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_A = MaskFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __A ( self: Optional[Any] ) -> Optional[int]:
_A = (self.model_tester.min_size,) * 2
_A = {
'''pixel_values''': torch.randn((2, 3, *size) , device=__A ),
'''mask_labels''': torch.randn((2, 10, *size) , device=__A ),
'''class_labels''': torch.zeros(2 , 10 , device=__A ).long(),
}
_A = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__A )
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
def __A ( self: Optional[Any] ) -> List[Any]:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__A , **__A , output_hidden_states=__A )
def __A ( self: Any ) -> Tuple:
_A ,_A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__A ).to(__A )
_A = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def __A ( self: Dict ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def __A ( self: Tuple ) -> Optional[Any]:
# only MaskFormerForInstanceSegmentation has the loss
_A = self.all_model_classes[1]
_A ,_A ,_A ,_A ,_A = self.model_tester.prepare_config_and_inputs()
_A = True
_A = True
_A = model_class(__A )
model.to(__A )
model.train()
_A = model(__A , mask_labels=__A , class_labels=__A )
_A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def __A ( ):
'''simple docstring'''
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self: Union[str, Any] ) -> Optional[int]:
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def __A ( self: List[Any] ) -> Any:
_A = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(__A )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
_A = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
_A = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Dict ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: List[Any] ) -> Dict:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(__A , return_tensors='''pt''' ).to(__A )
_A = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_A = model(**__A )
# masks_queries_logits
_A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_A = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
_A = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
_A = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_A = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def __A ( self: Optional[Any] ) -> str:
_A = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(__A )
.eval()
)
_A = self.default_image_processor
_A = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
_A = inputs['''pixel_values'''].to(__A )
_A = [el.to(__A ) for el in inputs['''mask_labels''']]
_A = [el.to(__A ) for el in inputs['''class_labels''']]
with torch.no_grad():
_A = model(**__A )
self.assertTrue(outputs.loss is not None )
| 62 | 1 |
from __future__ import annotations
from math import pi
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Optional[int] , __A: Optional[Any] ) -> str:
_A = question_encoder
_A = generator
_A = self.question_encoder
def __A ( self: Optional[int] , __A: Union[str, Any] ) -> Dict:
if os.path.isfile(__A ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(__A , exist_ok=__A )
_A = os.path.join(__A , '''question_encoder_tokenizer''' )
_A = os.path.join(__A , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def __A ( cls: Optional[Any] , __A: List[str] , **__A: int ) -> Any:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_A = kwargs.pop('''config''' , __A )
if config is None:
_A = RagConfig.from_pretrained(__A )
_A = AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
_A = AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__A , generator=__A )
def __call__( self: int , *__A: Optional[int] , **__A: List[str] ) -> int:
return self.current_tokenizer(*__A , **__A )
def __A ( self: Dict , *__A: List[str] , **__A: List[str] ) -> Dict:
return self.generator.batch_decode(*__A , **__A )
def __A ( self: Union[str, Any] , *__A: Tuple , **__A: List[str] ) -> Tuple:
return self.generator.decode(*__A , **__A )
def __A ( self: Dict ) -> List[str]:
_A = self.question_encoder
def __A ( self: Union[str, Any] ) -> int:
_A = self.generator
def __A ( self: Dict , __A: List[str] , __A: Optional[List[str]] = None , __A: Optional[int] = None , __A: Optional[int] = None , __A: str = "longest" , __A: str = None , __A: bool = True , **__A: Tuple , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __A , )
if max_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_A = self.current_tokenizer.model_max_length
_A = self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_A = labels['''input_ids''']
return model_inputs
| 62 | 0 |
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