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import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self, _a, _a=2, _a=3, _a=4, _a=2, _a=7, _a=True, _a=True, _a=True, _a=True, _a=99, _a=36, _a=3, _a=4, _a=37, _a="gelu", _a=0.1, _a=0.1, _a=5_12, _a=16, _a=2, _a=0.02, _a=6, _a=6, _a=3, _a=4, _a=None, _a=10_00, ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = text_seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_input_mask
__SCREAMING_SNAKE_CASE = use_token_type_ids
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = coordinate_size
__SCREAMING_SNAKE_CASE = shape_size
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = num_choices
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__SCREAMING_SNAKE_CASE = text_seq_length
__SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2 + 1
__SCREAMING_SNAKE_CASE = self.text_seq_length + self.image_seq_length
def __lowerCAmelCase ( self ) -> Dict:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__SCREAMING_SNAKE_CASE = bbox[i, j, 3]
__SCREAMING_SNAKE_CASE = bbox[i, j, 1]
__SCREAMING_SNAKE_CASE = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__SCREAMING_SNAKE_CASE = bbox[i, j, 2]
__SCREAMING_SNAKE_CASE = bbox[i, j, 0]
__SCREAMING_SNAKE_CASE = t
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.text_seq_length] )
__SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length], self.type_vocab_size )
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size], self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length], self.num_labels )
__SCREAMING_SNAKE_CASE = LayoutLMvaConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, coordinate_size=self.coordinate_size, shape_size=self.shape_size, input_size=self.image_size, patch_size=self.patch_size, )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __lowerCAmelCase ( self, _a, _a, _a, _a, _a, _a, _a, _a ) -> int:
__SCREAMING_SNAKE_CASE = LayoutLMvaModel(config=_a )
model.to(_a )
model.eval()
# text + image
__SCREAMING_SNAKE_CASE = model(_a, pixel_values=_a )
__SCREAMING_SNAKE_CASE = model(
_a, bbox=_a, pixel_values=_a, attention_mask=_a, token_type_ids=_a )
__SCREAMING_SNAKE_CASE = model(_a, bbox=_a, pixel_values=_a, token_type_ids=_a )
__SCREAMING_SNAKE_CASE = model(_a, bbox=_a, pixel_values=_a )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__SCREAMING_SNAKE_CASE = model(_a )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__SCREAMING_SNAKE_CASE = model(pixel_values=_a )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size) )
def __lowerCAmelCase ( self, _a, _a, _a, _a, _a, _a, _a, _a ) -> List[str]:
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = LayoutLMvaForSequenceClassification(_a )
model.to(_a )
model.eval()
__SCREAMING_SNAKE_CASE = model(
_a, bbox=_a, pixel_values=_a, attention_mask=_a, token_type_ids=_a, labels=_a, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self, _a, _a, _a, _a, _a, _a, _a, _a ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = LayoutLMvaForTokenClassification(config=_a )
model.to(_a )
model.eval()
__SCREAMING_SNAKE_CASE = model(
_a, bbox=_a, pixel_values=_a, attention_mask=_a, token_type_ids=_a, labels=_a, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels) )
def __lowerCAmelCase ( self, _a, _a, _a, _a, _a, _a, _a, _a ) -> Dict:
__SCREAMING_SNAKE_CASE = LayoutLMvaForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
__SCREAMING_SNAKE_CASE = model(
_a, bbox=_a, pixel_values=_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 __lowerCAmelCase ( self ) -> List[Any]:
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
__SCREAMING_SNAKE_CASE = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ =False
SCREAMING_SNAKE_CASE__ =False
SCREAMING_SNAKE_CASE__ =False
SCREAMING_SNAKE_CASE__ =(
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ =(
{"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
if is_torch_available()
else {}
)
def __lowerCAmelCase ( self, _a, _a, _a, _a, _a ) -> Dict:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def __lowerCAmelCase ( self ) -> int:
__SCREAMING_SNAKE_CASE = LayoutLMvaModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self, config_class=_a, hidden_size=37 )
def __lowerCAmelCase ( self, _a, _a, _a=False ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = copy.deepcopy(_a )
if model_class in get_values(_a ):
__SCREAMING_SNAKE_CASE = {
k: v.unsqueeze(1 ).expand(-1, self.model_tester.num_choices, -1 ).contiguous()
if isinstance(_a, torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(_a ):
__SCREAMING_SNAKE_CASE = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=_a )
elif model_class in get_values(_a ):
__SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=_a )
__SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=_a )
elif model_class in [
*get_values(_a ),
]:
__SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=_a )
elif model_class in [
*get_values(_a ),
]:
__SCREAMING_SNAKE_CASE = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=torch.long, device=_a, )
return inputs_dict
def __lowerCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowerCAmelCase ( self ) -> Dict:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__SCREAMING_SNAKE_CASE = type
self.model_tester.create_and_check_model(*_a )
def __lowerCAmelCase ( self ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def __lowerCAmelCase ( self ) -> List[str]:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
def __lowerCAmelCase ( self ) -> List[Any]:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
@slow
def __lowerCAmelCase ( self ) -> Optional[int]:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = LayoutLMvaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _A ( ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self ) -> int:
return LayoutLMvaImageProcessor(apply_ocr=_a ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ) -> Any:
__SCREAMING_SNAKE_CASE = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(_a )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=_a, return_tensors="pt" ).pixel_values.to(_a )
__SCREAMING_SNAKE_CASE = torch.tensor([[1, 2]] )
__SCREAMING_SNAKE_CASE = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
__SCREAMING_SNAKE_CASE = model(
input_ids=input_ids.to(_a ), bbox=bbox.to(_a ), pixel_values=pixel_values.to(_a ), )
# verify the logits
__SCREAMING_SNAKE_CASE = torch.Size((1, 1_99, 7_68) )
self.assertEqual(outputs.last_hidden_state.shape, _a )
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(_a )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], _a, atol=1E-4 ) )
| 693
|
from __future__ import annotations
import math
def _A ( __snake_case :int , __snake_case :int , __snake_case :bool , __snake_case :list[int] , __snake_case :float ) -> int:
"""simple docstring"""
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def _A ( ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [90, 23, 6, 33, 21, 65, 123, 3_4423]
__SCREAMING_SNAKE_CASE = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 693
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase ) -> List[List[ImageInput]]:
if isinstance(_A , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_A , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_A ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class _lowerCAmelCase ( _UpperCAmelCase ):
A__ = ['pixel_values']
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
super().__init__(**__UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = size if size is not None else {'''shortest_edge''': 224}
lowerCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
lowerCAmelCase__ : Dict = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCAmelCase__ : str = get_size_dict(__UpperCamelCase , param_name='''crop_size''' )
lowerCAmelCase__ : List[Any] = do_resize
lowerCAmelCase__ : List[Any] = size
lowerCAmelCase__ : Optional[Any] = do_center_crop
lowerCAmelCase__ : Optional[Any] = crop_size
lowerCAmelCase__ : str = resample
lowerCAmelCase__ : str = do_rescale
lowerCAmelCase__ : List[Any] = rescale_factor
lowerCAmelCase__ : List[str] = do_normalize
lowerCAmelCase__ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = None , **__UpperCAmelCase , ):
lowerCAmelCase__ : Dict = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
lowerCAmelCase__ : str = get_resize_output_image_size(__UpperCamelCase , size['''shortest_edge'''] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
lowerCAmelCase__ : Optional[int] = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
lowerCAmelCase__ : Dict = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(__UpperCamelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCamelCase , **__UpperCamelCase )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
lowerCAmelCase__ : List[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
lowerCAmelCase__ : Optional[Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
lowerCAmelCase__ : Any = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
lowerCAmelCase__ : str = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase )
if do_normalize:
lowerCAmelCase__ : Any = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
lowerCAmelCase__ : List[str] = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
lowerCAmelCase__ : List[Any] = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ : Tuple = resample if resample is not None else self.resample
lowerCAmelCase__ : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ : Optional[int] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ : Union[str, Any] = image_std if image_std is not None else self.image_std
lowerCAmelCase__ : Tuple = size if size is not None else self.size
lowerCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ : Dict = get_size_dict(__UpperCamelCase , param_name='''crop_size''' )
if not valid_images(__UpperCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
lowerCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
lowerCAmelCase__ : Optional[int] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
lowerCAmelCase__ : Dict = {'''pixel_values''': videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 719
|
lowerCAmelCase_ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
# Return True if there is node that has not iterated.
lowerCAmelCase__ : List[str] = [False] * len(UpperCamelCase )
lowerCAmelCase__ : int = [s]
lowerCAmelCase__ : Dict = True
while queue:
lowerCAmelCase__ : Union[str, Any] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(UpperCamelCase )
lowerCAmelCase__ : List[str] = True
lowerCAmelCase__ : int = u
return visited[t]
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Dict = [-1] * (len(UpperCamelCase ))
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Tuple = [i[:] for i in graph] # Record original cut, copy.
while bfs(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
lowerCAmelCase__ : Optional[Any] = float('''Inf''' )
lowerCAmelCase__ : Optional[Any] = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase__ : Optional[Any] = min(UpperCamelCase , graph[parent[s]][s] )
lowerCAmelCase__ : Union[str, Any] = parent[s]
max_flow += path_flow
lowerCAmelCase__ : List[str] = sink
while v != source:
lowerCAmelCase__ : Union[str, Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase__ : Dict = parent[v]
for i in range(len(UpperCamelCase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 470
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
"load_tf_weights_in_trajectory_transformer",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 532
|
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
@dataclass
class lowercase :
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
@dataclass
class lowercase :
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'train'
_SCREAMING_SNAKE_CASE = 'dev'
_SCREAMING_SNAKE_CASE = 'test'
class lowercase :
@staticmethod
def _snake_case ( lowercase , lowercase ) -> List[InputExample]:
raise NotImplementedError
@staticmethod
def _snake_case ( lowercase ) -> List[str]:
raise NotImplementedError
@staticmethod
def _snake_case ( lowercase , lowercase , lowercase , lowercase , lowercase=False , lowercase="[CLS]" , lowercase=1 , lowercase="[SEP]" , lowercase=False , lowercase=False , lowercase=0 , lowercase=0 , lowercase=-100 , lowercase=0 , lowercase=True , ) -> List[InputFeatures]:
lowerCAmelCase = {label: i for i, label in enumerate(lowercase )}
lowerCAmelCase = []
for ex_index, example in enumerate(lowercase ):
if ex_index % 10_000 == 0:
logger.info("""Writing example %d of %d""" , lowercase , len(lowercase ) )
lowerCAmelCase = []
lowerCAmelCase = []
for word, label in zip(example.words , example.labels ):
lowerCAmelCase = tokenizer.tokenize(lowercase )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(lowercase ) > 0:
tokens.extend(lowercase )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
lowerCAmelCase = tokenizer.num_special_tokens_to_add()
if len(lowercase ) > max_seq_length - special_tokens_count:
lowerCAmelCase = tokens[: (max_seq_length - special_tokens_count)]
lowerCAmelCase = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
lowerCAmelCase = [sequence_a_segment_id] * len(lowercase )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
lowerCAmelCase = [cls_token] + tokens
lowerCAmelCase = [pad_token_label_id] + label_ids
lowerCAmelCase = [cls_token_segment_id] + segment_ids
lowerCAmelCase = tokenizer.convert_tokens_to_ids(lowercase )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
lowerCAmelCase = [1 if mask_padding_with_zero else 0] * len(lowercase )
# Zero-pad up to the sequence length.
lowerCAmelCase = max_seq_length - len(lowercase )
if pad_on_left:
lowerCAmelCase = ([pad_token] * padding_length) + input_ids
lowerCAmelCase = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
lowerCAmelCase = ([pad_token_segment_id] * padding_length) + segment_ids
lowerCAmelCase = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(lowercase ) == max_seq_length
assert len(lowercase ) == max_seq_length
assert len(lowercase ) == max_seq_length
assert len(lowercase ) == max_seq_length
if ex_index < 5:
logger.info("""*** Example ***""" )
logger.info("""guid: %s""" , example.guid )
logger.info("""tokens: %s""" , """ """.join([str(lowercase ) for x in tokens] ) )
logger.info("""input_ids: %s""" , """ """.join([str(lowercase ) for x in input_ids] ) )
logger.info("""input_mask: %s""" , """ """.join([str(lowercase ) for x in input_mask] ) )
logger.info("""segment_ids: %s""" , """ """.join([str(lowercase ) for x in segment_ids] ) )
logger.info("""label_ids: %s""" , """ """.join([str(lowercase ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
lowerCAmelCase = None
features.append(
InputFeatures(
input_ids=lowercase , attention_mask=lowercase , token_type_ids=lowercase , label_ids=lowercase ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = nn.CrossEntropyLoss().ignore_index
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase = None , lowercase=False , lowercase = Split.train , ) -> List[str]:
# Load data features from cache or dataset file
lowerCAmelCase = os.path.join(
lowercase , """cached_{}_{}_{}""".format(mode.value , tokenizer.__class__.__name__ , str(lowercase ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(lowercase ):
if os.path.exists(lowercase ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
lowerCAmelCase = torch.load(lowercase )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
lowerCAmelCase = token_classification_task.read_examples_from_file(lowercase , lowercase )
# TODO clean up all this to leverage built-in features of tokenizers
lowerCAmelCase = token_classification_task.convert_examples_to_features(
lowercase , lowercase , lowercase , lowercase , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=lowercase , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f'Saving features into cached file {cached_features_file}' )
torch.save(self.features , lowercase )
def __len__( self ) -> int:
return len(self.features )
def __getitem__( self , lowercase ) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class lowercase :
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = -100
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase = None , lowercase=False , lowercase = Split.train , ) -> Any:
lowerCAmelCase = token_classification_task.read_examples_from_file(lowercase , lowercase )
# TODO clean up all this to leverage built-in features of tokenizers
lowerCAmelCase = token_classification_task.convert_examples_to_features(
lowercase , lowercase , lowercase , lowercase , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=lowercase , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
lowerCAmelCase = tf.data.Dataset.from_generator(
lowercase , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) , (
{"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
lowerCAmelCase = tf.data.Dataset.from_generator(
lowercase , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) , (
{
"""input_ids""": tf.TensorShape([None] ),
"""attention_mask""": tf.TensorShape([None] ),
"""token_type_ids""": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self ) -> Optional[int]:
return len(self.features )
def __getitem__( self , lowercase ) -> InputFeatures:
return self.features[i]
| 532
| 1
|
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
UpperCamelCase = 300 # TEMPERATURE (unit = K)
def lowerCAmelCase ( UpperCamelCase_: float , UpperCamelCase_: float , UpperCamelCase_: float , ) -> float:
if donor_conc <= 0:
raise ValueError("Donor concentration should be positive" )
elif acceptor_conc <= 0:
raise ValueError("Acceptor concentration should be positive" )
elif intrinsic_conc <= 0:
raise ValueError("Intrinsic concentration should be positive" )
elif donor_conc <= intrinsic_conc:
raise ValueError(
"Donor concentration should be greater than intrinsic concentration" )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
"Acceptor concentration should be greater than intrinsic concentration" )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716
|
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase_ (_UpperCAmelCase ):
def __init__( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , ) ->Optional[Any]:
'''simple docstring'''
super().__init__()
if safety_checker is None:
logger.warning(
f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
speech_model=a_ , speech_processor=a_ , vae=a_ , text_encoder=a_ , tokenizer=a_ , unet=a_ , scheduler=a_ , feature_extractor=a_ , )
def lowerCamelCase__ ( self , a_ = "auto" ) ->Optional[int]:
'''simple docstring'''
if slice_size == "auto":
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(a_ )
def lowerCamelCase__ ( self ) ->Any:
'''simple docstring'''
self.enable_attention_slicing(a_ )
@torch.no_grad()
def __call__( self , a_ , a_=1_6_0_0_0 , a_ = 5_1_2 , a_ = 5_1_2 , a_ = 5_0 , a_ = 7.5 , a_ = None , a_ = 1 , a_ = 0.0 , a_ = None , a_ = None , a_ = "pil" , a_ = True , a_ = None , a_ = 1 , **a_ , ) ->str:
'''simple docstring'''
_a = self.speech_processor.feature_extractor(
a_ , return_tensors="pt" , sampling_rate=a_ ).input_features.to(self.device )
_a = self.speech_model.generate(a_ , max_length=4_8_0_0_0_0 )
_a = self.speech_processor.tokenizer.batch_decode(a_ , skip_special_tokens=a_ , normalize=a_ )[
0
]
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]
_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 = [""] * batch_size
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(1 , 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 = 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 )
else:
_a = torch.randn(a_ , generator=a_ , device=self.device , dtype=a_ )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# 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 output_type == "pil":
_a = self.numpy_to_pil(a_ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=a_ , nsfw_content_detected=a_ )
| 612
| 0
|
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _snake_case ( snake_case ):
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCAmelCase__ , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(UpperCAmelCase__ , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(UpperCAmelCase__ , 'num_attention_heads' ) )
class _snake_case :
"""simple docstring"""
def __init__( self , UpperCAmelCase__ , UpperCAmelCase__=13 , UpperCAmelCase__=32 , UpperCAmelCase__=2 , UpperCAmelCase__=3 , UpperCAmelCase__=640 , UpperCAmelCase__=4 , UpperCAmelCase__="silu" , UpperCAmelCase__=3 , UpperCAmelCase__=32 , UpperCAmelCase__=0.1 , UpperCAmelCase__=0.1 , UpperCAmelCase__=0.1 , UpperCAmelCase__=0.0_2 , UpperCAmelCase__=True , UpperCAmelCase__=True , UpperCAmelCase__=10 , UpperCAmelCase__=None , ) -> Union[str, Any]:
a_ = parent
a_ = batch_size
a_ = image_size
a_ = patch_size
a_ = num_channels
a_ = last_hidden_size
a_ = num_attention_heads
a_ = hidden_act
a_ = conv_kernel_size
a_ = output_stride
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = classifier_dropout_prob
a_ = use_labels
a_ = is_training
a_ = num_labels
a_ = initializer_range
a_ = scope
def __SCREAMING_SNAKE_CASE ( self ) -> int:
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ = None
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.num_labels )
a_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
a_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
a_ = MobileViTModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
a_ = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
a_ = self.num_labels
a_ = MobileViTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
a_ = model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
a_ = self.num_labels
a_ = MobileViTForSemanticSegmentation(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
a_ = model(UpperCAmelCase__ )
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(UpperCAmelCase__ , labels=UpperCAmelCase__ )
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 __SCREAMING_SNAKE_CASE ( self ) -> int:
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 _snake_case ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
_UpperCamelCase = (
{
"feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a_ = MobileViTModelTester(self )
a_ = MobileViTConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
pass
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(UpperCAmelCase__ )
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] , UpperCAmelCase__ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
pass
def __SCREAMING_SNAKE_CASE ( self ) -> str:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
def check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
a_ = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
a_ = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
a_ = outputs.hidden_states
a_ = 5
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
a_ = 2
for i in range(len(UpperCAmelCase__ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
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(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase__ )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = MobileViTModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def a ( ) -> List[str]:
"""simple docstring"""
a_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> int:
a_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(UpperCAmelCase__ )
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
a_ = model(**UpperCAmelCase__ )
# verify the logits
a_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
a_ = torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1e-4 ) )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
a_ = model.to(UpperCAmelCase__ )
a_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
a_ = prepare_img()
a_ = image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
a_ = model(**UpperCAmelCase__ )
a_ = outputs.logits
# verify the logits
a_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , UpperCAmelCase__ )
a_ = torch.tensor(
[
[[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
[[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
[[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
] , device=UpperCAmelCase__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , UpperCAmelCase__ , atol=1e-4 ) )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
a_ = model.to(UpperCAmelCase__ )
a_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
a_ = prepare_img()
a_ = image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
a_ = model(**UpperCAmelCase__ )
a_ = outputs.logits.detach().cpu()
a_ = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase__ , target_sizes=[(50, 60)] )
a_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , UpperCAmelCase__ )
a_ = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase__ )
a_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , UpperCAmelCase__ )
| 697
|
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__lowerCAmelCase ="naver-clova-ix/donut-base"
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
a_ = DonutProcessor.from_pretrained(UpperCAmelCase__ )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
a_ = {
'name': 'John Doe',
'age': '99',
'city': 'Atlanta',
'state': 'GA',
'zip': '30301',
'phone': '123-4567',
'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
}
a_ = (
'<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
'<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
'<s_nicknames><s_nickname>Johnny</s_nickname>'
'<sep/><s_nickname>JD</s_nickname></s_nicknames>'
)
a_ = self.processor.tokenajson(UpperCAmelCase__ )
self.assertDictEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 697
| 1
|
def _lowercase ( _UpperCAmelCase ) -> int:
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase =grid[0]
for row_n in range(1 , len(_UpperCAmelCase ) ):
lowerCamelCase =grid[row_n]
lowerCamelCase =fill_row(_UpperCAmelCase , _UpperCAmelCase )
lowerCamelCase =grid[row_n]
return grid[-1][-1]
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> list:
current_row[0] += row_above[0]
for cell_n in range(1 , len(_UpperCAmelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 269
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A ( a ):
@staticmethod
@abstractmethod
def _snake_case ( UpperCAmelCase_ ):
raise NotImplementedError()
@abstractmethod
def _snake_case ( self ):
raise NotImplementedError()
| 269
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : Dict = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTBigCodeForSequenceClassification',
'GPTBigCodeForTokenClassification',
'GPTBigCodeForCausalLM',
'GPTBigCodeModel',
'GPTBigCodePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : str = 'Salesforce/blip-image-captioning-base'
_snake_case : Union[str, Any] = (
'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.'
)
_snake_case : List[Any] = 'image_captioner'
_snake_case : Union[str, Any] = AutoModelForVisionaSeq
_snake_case : Dict = ['image']
_snake_case : Optional[int] = ['text']
def __init__( self : Union[str, Any] , *lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''vision'''] )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case__ ( self : Tuple , lowerCAmelCase__ : "Image" ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(images=lowerCAmelCase__ , return_tensors='''pt''' )
def snake_case__ ( self : Any , lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.model.generate(**lowerCAmelCase__ )
def snake_case__ ( self : int , lowerCAmelCase__ : List[str] ) -> List[str]:
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )[0].strip()
| 98
| 1
|
from __future__ import annotations
def A_ ( __a : list[int] ):
"""simple docstring"""
return len(set(__a ) ) == len(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712
|
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def A_ ( __a : float ):
"""simple docstring"""
if velocity > c:
raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!""" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("""Speed must be greater than or equal to 1!""" )
return velocity / c
def A_ ( __a : float ):
"""simple docstring"""
return 1 / sqrt(1 - beta(__a ) ** 2 )
def A_ ( __a : float ):
"""simple docstring"""
return np.array(
[
[gamma(__a ), -gamma(__a ) * beta(__a ), 0, 0],
[-gamma(__a ) * beta(__a ), gamma(__a ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def A_ ( __a : float , __a : np.ndarray | None = None ):
"""simple docstring"""
# Ensure event is not empty
if event is None:
a__ = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(__a ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
UpperCAmelCase = transform(29_979_245)
print("""Example of four vector: """)
print(f"""ct' = {four_vector[0]}""")
print(f"""x' = {four_vector[1]}""")
print(f"""y' = {four_vector[2]}""")
print(f"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
UpperCAmelCase = {ct: c, x: 1, y: 1, z: 1}
UpperCAmelCase = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f"""\n{numerical_vector}""")
| 351
| 0
|
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def lowercase( UpperCamelCase_ ) -> Optional[int]:
'''simple docstring'''
return EnvironmentCommand()
def lowercase( UpperCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
return EnvironmentCommand(args.accelerate_config_file )
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : ArgumentParser ):
"""simple docstring"""
UpperCamelCase = parser.add_parser("""env""" )
download_parser.set_defaults(func=lowerCamelCase_ )
download_parser.add_argument(
"""--accelerate-config_file""" , default=lowerCamelCase_ , help="""The accelerate config file to use for the default values in the launching script.""" , )
download_parser.set_defaults(func=lowerCamelCase_ )
def __init__( self : Optional[int] , lowerCamelCase_ : Tuple , *lowerCamelCase_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = accelerate_config_file
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = """not installed"""
if is_safetensors_available():
import safetensors
UpperCamelCase = safetensors.__version__
elif importlib.util.find_spec("""safetensors""" ) is not None:
import safetensors
UpperCamelCase = f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""
UpperCamelCase = """not installed"""
UpperCamelCase = UpperCamelCase = """not found"""
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
UpperCamelCase = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase_ ):
UpperCamelCase = load_config_from_file(self._accelerate_config_file ).to_dict()
UpperCamelCase = (
"""\n""".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(lowerCamelCase_ , lowerCamelCase_ )
else f"""\t{accelerate_config}"""
)
UpperCamelCase = """not installed"""
UpperCamelCase = """NA"""
if is_torch_available():
import torch
UpperCamelCase = torch.__version__
UpperCamelCase = torch.cuda.is_available()
UpperCamelCase = """not installed"""
UpperCamelCase = """NA"""
if is_tf_available():
import tensorflow as tf
UpperCamelCase = tf.__version__
try:
# deprecated in v2.1
UpperCamelCase = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
UpperCamelCase = bool(tf.config.list_physical_devices("""GPU""" ) )
UpperCamelCase = """not installed"""
UpperCamelCase = """not installed"""
UpperCamelCase = """not installed"""
UpperCamelCase = """NA"""
if is_flax_available():
import flax
import jax
import jaxlib
UpperCamelCase = flax.__version__
UpperCamelCase = jax.__version__
UpperCamelCase = jaxlib.__version__
UpperCamelCase = jax.lib.xla_bridge.get_backend().platform
UpperCamelCase = {
"""`transformers` version""": version,
"""Platform""": platform.platform(),
"""Python version""": platform.python_version(),
"""Huggingface_hub version""": huggingface_hub.__version__,
"""Safetensors version""": f"""{safetensors_version}""",
"""Accelerate version""": f"""{accelerate_version}""",
"""Accelerate config""": f"""{accelerate_config_str}""",
"""PyTorch version (GPU?)""": f"""{pt_version} ({pt_cuda_available})""",
"""Tensorflow version (GPU?)""": f"""{tf_version} ({tf_cuda_available})""",
"""Flax version (CPU?/GPU?/TPU?)""": f"""{flax_version} ({jax_backend})""",
"""Jax version""": f"""{jax_version}""",
"""JaxLib version""": f"""{jaxlib_version}""",
"""Using GPU in script?""": """<fill in>""",
"""Using distributed or parallel set-up in script?""": """<fill in>""",
}
print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n""" )
print(self.format_dict(lowerCamelCase_ ) )
return info
@staticmethod
def lowerCamelCase_ ( lowerCamelCase_ : Optional[int] ):
"""simple docstring"""
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"
| 537
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
__lowerCAmelCase = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase = """LayoutLMv3ImageProcessor"""
__lowerCAmelCase = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
def __init__( self : Dict , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : Dict=None , **lowerCamelCase_ : str ):
"""simple docstring"""
UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCamelCase_ , )
UpperCamelCase = kwargs.pop("""feature_extractor""" )
UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def __call__( self : int , lowerCamelCase_ : str , lowerCamelCase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCamelCase_ : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowerCamelCase_ : Union[List[List[int]], List[List[List[int]]]] = None , lowerCamelCase_ : Optional[Union[List[int], List[List[int]]]] = None , lowerCamelCase_ : bool = True , lowerCamelCase_ : Union[bool, str, PaddingStrategy] = False , lowerCamelCase_ : Union[bool, str, TruncationStrategy] = None , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : int = 0 , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = False , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Union[str, TensorType]] = None , **lowerCamelCase_ : Optional[int] , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
# first, apply the image processor
UpperCamelCase = self.image_processor(images=lowerCamelCase_ , return_tensors=lowerCamelCase_ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
UpperCamelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)
UpperCamelCase = features["""words"""]
UpperCamelCase = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , padding=lowerCamelCase_ , truncation=lowerCamelCase_ , max_length=lowerCamelCase_ , stride=lowerCamelCase_ , pad_to_multiple_of=lowerCamelCase_ , return_token_type_ids=lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , return_overflowing_tokens=lowerCamelCase_ , return_special_tokens_mask=lowerCamelCase_ , return_offsets_mapping=lowerCamelCase_ , return_length=lowerCamelCase_ , verbose=lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ , )
# add pixel values
UpperCamelCase = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
UpperCamelCase = self.get_overflowing_images(lowerCamelCase_ , encoded_inputs["""overflow_to_sample_mapping"""] )
UpperCamelCase = images
return encoded_inputs
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Dict ):
"""simple docstring"""
UpperCamelCase = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
f""" {len(lowerCamelCase_ )} and {len(lowerCamelCase_ )}""" )
return images_with_overflow
def lowerCamelCase_ ( self : Union[str, Any] , *lowerCamelCase_ : Any , **lowerCamelCase_ : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , *lowerCamelCase_ : Dict , **lowerCamelCase_ : str ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase_ , **lowerCamelCase_ )
@property
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowerCamelCase_ , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowerCamelCase_ , )
return self.image_processor
| 537
| 1
|
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _lowerCAmelCase ( ):
"""simple docstring"""
raise RuntimeError("CUDA out of memory." )
class _SCREAMING_SNAKE_CASE (nn.Module ):
def __init__( self : Optional[int] )->Optional[int]:
super().__init__()
__SCREAMING_SNAKE_CASE : Tuple = nn.Linear(3 , 4 )
__SCREAMING_SNAKE_CASE : Dict = nn.BatchNormad(4 )
__SCREAMING_SNAKE_CASE : Any = nn.Linear(4 , 5 )
def __snake_case ( self : str , UpperCamelCase : Any )->List[Any]:
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase ) ) )
class _SCREAMING_SNAKE_CASE (unittest.TestCase ):
def __snake_case ( self : Optional[Any] )->Union[str, Any]:
__SCREAMING_SNAKE_CASE : List[Any] = []
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(UpperCamelCase : Any ):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(UpperCamelCase , [1_2_8, 6_4, 3_2, 1_6, 8] )
def __snake_case ( self : int )->Optional[int]:
__SCREAMING_SNAKE_CASE : str = []
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(UpperCamelCase : Tuple , UpperCamelCase : int ):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
__SCREAMING_SNAKE_CASE : List[str] = mock_training_loop_function("hello" )
self.assertListEqual(UpperCamelCase , [1_2_8, 6_4, 3_2, 1_6, 8] )
self.assertListEqual([bs, arga] , [8, "hello"] )
def __snake_case ( self : Union[str, Any] )->Any:
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(UpperCamelCase : Dict ):
pass
with self.assertRaises(UpperCamelCase ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def __snake_case ( self : str )->List[str]:
@find_executable_batch_size(starting_batch_size=1_6 )
def mock_training_loop_function(UpperCamelCase : Tuple ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(UpperCamelCase ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def __snake_case ( self : Union[str, Any] )->Dict:
@find_executable_batch_size(starting_batch_size=1_2_8 )
def mock_training_loop_function(UpperCamelCase : Any , UpperCamelCase : List[Any] , UpperCamelCase : List[Any] ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(UpperCamelCase ) as cm:
mock_training_loop_function(1_2_8 , "hello" , "world" )
self.assertIn("Batch size was passed into `f`" , cm.exception.args[0] )
self.assertIn("`f(arg1='hello', arg2='world')" , cm.exception.args[0] )
def __snake_case ( self : Tuple )->Union[str, Any]:
@find_executable_batch_size(starting_batch_size=1_6 )
def mock_training_loop_function(UpperCamelCase : Any ):
raise ValueError("Oops, we had an error!" )
with self.assertRaises(UpperCamelCase ) as cm:
mock_training_loop_function()
self.assertIn("Oops, we had an error!" , cm.exception.args[0] )
@require_cuda
def __snake_case ( self : Dict )->Union[str, Any]:
__SCREAMING_SNAKE_CASE : str = torch.cuda.memory_allocated()
__SCREAMING_SNAKE_CASE : Optional[int] = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , UpperCamelCase )
__SCREAMING_SNAKE_CASE : str = release_memory(UpperCamelCase )
self.assertEqual(torch.cuda.memory_allocated() , UpperCamelCase )
| 709
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def _lowerCAmelCase ( __lowerCamelCase : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = botoa.client("iam" )
__SCREAMING_SNAKE_CASE : List[Any] = {
"Version": "2012-10-17",
"Statement": [
{"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=__lowerCamelCase , AssumeRolePolicyDocument=json.dumps(__lowerCamelCase , indent=2 ) )
__SCREAMING_SNAKE_CASE : str = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"sagemaker:*",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage",
"ecr:BatchCheckLayerAvailability",
"ecr:GetAuthorizationToken",
"cloudwatch:PutMetricData",
"cloudwatch:GetMetricData",
"cloudwatch:GetMetricStatistics",
"cloudwatch:ListMetrics",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:DescribeLogStreams",
"logs:PutLogEvents",
"logs:GetLogEvents",
"s3:CreateBucket",
"s3:ListBucket",
"s3:GetBucketLocation",
"s3:GetObject",
"s3:PutObject",
],
"Resource": "*",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=__lowerCamelCase , PolicyName=F"""{role_name}_policy_permission""" , PolicyDocument=json.dumps(__lowerCamelCase , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F"""role {role_name} already exists. Using existing one""" )
def _lowerCAmelCase ( __lowerCamelCase : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = botoa.client("iam" )
return iam_client.get_role(RoleName=__lowerCamelCase )["Role"]["Arn"]
def _lowerCAmelCase ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = _ask_options(
"How do you want to authorize?" , ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "] , __lowerCamelCase , )
__SCREAMING_SNAKE_CASE : str = None
if credentials_configuration == 0:
__SCREAMING_SNAKE_CASE : List[str] = _ask_field("Enter your AWS Profile name: [default] " , default="default" )
__SCREAMING_SNAKE_CASE : Any = aws_profile
else:
print(
"Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
"`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
__SCREAMING_SNAKE_CASE : Dict = _ask_field("AWS Access Key ID: " )
__SCREAMING_SNAKE_CASE : Union[str, Any] = aws_access_key_id
__SCREAMING_SNAKE_CASE : Union[str, Any] = _ask_field("AWS Secret Access Key: " )
__SCREAMING_SNAKE_CASE : Optional[int] = aws_secret_access_key
__SCREAMING_SNAKE_CASE : List[Any] = _ask_field("Enter your AWS Region: [us-east-1]" , default="us-east-1" )
__SCREAMING_SNAKE_CASE : Any = aws_region
__SCREAMING_SNAKE_CASE : int = _ask_options(
"Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?" , ["Provide IAM Role name", "Create new IAM role using credentials"] , __lowerCamelCase , )
if role_management == 0:
__SCREAMING_SNAKE_CASE : List[str] = _ask_field("Enter your IAM role name: " )
else:
__SCREAMING_SNAKE_CASE : Any = "accelerate_sagemaker_execution_role"
print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials""" )
_create_iam_role_for_sagemaker(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = _ask_field(
"Do you want to use custom Docker image? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
__SCREAMING_SNAKE_CASE : Tuple = None
if is_custom_docker_image:
__SCREAMING_SNAKE_CASE : List[Any] = _ask_field("Enter your Docker image: " , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() )
__SCREAMING_SNAKE_CASE : Dict = _ask_field(
"Do you want to provide SageMaker input channels with data locations? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
__SCREAMING_SNAKE_CASE : List[Any] = None
if is_sagemaker_inputs_enabled:
__SCREAMING_SNAKE_CASE : List[str] = _ask_field(
"Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): " , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , )
__SCREAMING_SNAKE_CASE : Dict = _ask_field(
"Do you want to enable SageMaker metrics? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
__SCREAMING_SNAKE_CASE : int = None
if is_sagemaker_metrics_enabled:
__SCREAMING_SNAKE_CASE : Dict = _ask_field(
"Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): " , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , )
__SCREAMING_SNAKE_CASE : Any = _ask_options(
"What is the distributed mode?" , ["No distributed training", "Data parallelism"] , _convert_sagemaker_distributed_mode , )
__SCREAMING_SNAKE_CASE : Tuple = {}
__SCREAMING_SNAKE_CASE : str = _ask_field(
"Do you wish to optimize your script with torch dynamo?[yes/NO]:" , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
if use_dynamo:
__SCREAMING_SNAKE_CASE : Optional[Any] = "dynamo_"
__SCREAMING_SNAKE_CASE : Optional[Any] = _ask_options(
"Which dynamo backend would you like to use?" , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = _ask_field(
"Do you want to customize the defaults sent to torch.compile? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
if use_custom_options:
__SCREAMING_SNAKE_CASE : Union[str, Any] = _ask_options(
"Which mode do you want to use?" , __lowerCamelCase , lambda __lowerCamelCase : TORCH_DYNAMO_MODES[int(__lowerCamelCase )] , default="default" , )
__SCREAMING_SNAKE_CASE : str = _ask_field(
"Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
__SCREAMING_SNAKE_CASE : Any = _ask_field(
"Do you want to enable dynamic shape tracing? [yes/NO]: " , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="Please enter yes or no." , )
__SCREAMING_SNAKE_CASE : Optional[int] = "Which EC2 instance type you want to use for your training?"
if distributed_type != SageMakerDistributedType.NO:
__SCREAMING_SNAKE_CASE : List[Any] = _ask_options(
__lowerCamelCase , __lowerCamelCase , lambda __lowerCamelCase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__lowerCamelCase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
__SCREAMING_SNAKE_CASE : Union[str, Any] = _ask_field(__lowerCamelCase , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , default="ml.p3.2xlarge" )
__SCREAMING_SNAKE_CASE : List[Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
__SCREAMING_SNAKE_CASE : Any = _ask_field(
"How many machines do you want use? [1]: " , __lowerCamelCase , default=1 , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = _ask_options(
"Do you wish to use FP16 or BF16 (mixed precision)?" , ["no", "fp16", "bf16", "fp8"] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
"Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )
return SageMakerConfig(
image_uri=__lowerCamelCase , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=__lowerCamelCase , use_cpu=__lowerCamelCase , dynamo_config=__lowerCamelCase , eca_instance_type=__lowerCamelCase , profile=__lowerCamelCase , region=__lowerCamelCase , iam_role_name=__lowerCamelCase , mixed_precision=__lowerCamelCase , num_machines=__lowerCamelCase , sagemaker_inputs_file=__lowerCamelCase , sagemaker_metrics_file=__lowerCamelCase , )
| 447
| 0
|
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] )-> Union[str, Any]:
'''simple docstring'''
__snake_case = OmegaConf.load(_lowerCamelCase )
__snake_case = torch.load(_lowerCamelCase , map_location='''cpu''' )['''model''']
__snake_case = list(state_dict.keys() )
# extract state_dict for VQVAE
__snake_case = {}
__snake_case = '''first_stage_model.'''
for key in keys:
if key.startswith(_lowerCamelCase ):
__snake_case = state_dict[key]
# extract state_dict for UNetLDM
__snake_case = {}
__snake_case = '''model.diffusion_model.'''
for key in keys:
if key.startswith(_lowerCamelCase ):
__snake_case = state_dict[key]
__snake_case = config.model.params.first_stage_config.params
__snake_case = config.model.params.unet_config.params
__snake_case = VQModel(**_lowerCamelCase ).eval()
vqvae.load_state_dict(_lowerCamelCase )
__snake_case = UNetLDMModel(**_lowerCamelCase ).eval()
unet.load_state_dict(_lowerCamelCase )
__snake_case = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='''scaled_linear''' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=_lowerCamelCase , )
__snake_case = LDMPipeline(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
pipeline.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
UpperCAmelCase_ : List[str] = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 24
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case_ :
"""simple docstring"""
def __init__(self: Optional[Any] , __UpperCAmelCase: List[str] , __UpperCAmelCase: int=12 , __UpperCAmelCase: Dict=7 , __UpperCAmelCase: str=True , __UpperCAmelCase: Tuple=True , __UpperCAmelCase: Tuple=True , __UpperCAmelCase: Optional[int]=99 , __UpperCAmelCase: str=32 , __UpperCAmelCase: Any=32 , __UpperCAmelCase: int=2 , __UpperCAmelCase: List[str]=4 , __UpperCAmelCase: Tuple=37 , __UpperCAmelCase: Optional[int]=0.1 , __UpperCAmelCase: List[Any]=0.1 , __UpperCAmelCase: Dict=512 , __UpperCAmelCase: Union[str, Any]=0.02 , __UpperCAmelCase: List[Any]=0 , __UpperCAmelCase: str=None , ) -> Tuple:
'''simple docstring'''
__a : Union[str, Any] = parent
__a : Dict = batch_size
__a : Optional[Any] = seq_length
__a : int = is_training
__a : Union[str, Any] = use_input_mask
__a : str = use_labels
__a : Tuple = vocab_size
__a : Dict = hidden_size
__a : Optional[int] = projection_dim
__a : Tuple = num_hidden_layers
__a : Dict = num_attention_heads
__a : Any = intermediate_size
__a : Optional[int] = dropout
__a : Dict = attention_dropout
__a : List[Any] = max_position_embeddings
__a : Any = initializer_range
__a : Tuple = scope
__a : Dict = bos_token_id
def UpperCAmelCase__ (self: str ) -> Union[str, Any]:
'''simple docstring'''
__a : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : Any = None
if self.use_input_mask:
__a : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
__a : Tuple = input_mask.numpy()
__a , __a : int = input_mask.shape
__a : Dict = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__UpperCAmelCase ):
__a : Any = 1
__a : int = 0
__a : Union[str, Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase )
def UpperCAmelCase__ (self: List[str] ) -> Optional[int]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCAmelCase__ (self: List[str] , __UpperCAmelCase: Any , __UpperCAmelCase: Dict , __UpperCAmelCase: str ) -> List[str]:
'''simple docstring'''
__a : int = TFBlipTextModel(config=__UpperCAmelCase )
__a : List[str] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , training=__UpperCAmelCase )
__a : Any = model(__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ (self: List[str] ) -> str:
'''simple docstring'''
__a : Tuple = self.prepare_config_and_inputs()
__a , __a , __a : Dict = config_and_inputs
__a : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class snake_case_ ( __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = (TFBlipTextModel,) if is_tf_available() else ()
snake_case__ = False
snake_case__ = False
snake_case__ = False
def UpperCAmelCase__ (self: List[str] ) -> Optional[int]:
'''simple docstring'''
__a : str = BlipTextModelTester(self )
__a : str = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def UpperCAmelCase__ (self: str ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ (self: Any ) -> Union[str, Any]:
'''simple docstring'''
__a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def UpperCAmelCase__ (self: Tuple ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase__ (self: Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase__ (self: Any ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase__ (self: List[Any] ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase__ (self: Union[str, Any] ) -> int:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ (self: Optional[Any] ) -> Tuple:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : List[str] = TFBlipTextModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def UpperCAmelCase__ (self: Dict , __UpperCAmelCase: Optional[Any]=True ) -> Any:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase )
| 351
| 0
|
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] ):
a__ = OmegaConf.load(__lowerCAmelCase )
a__ = torch.load(__lowerCAmelCase , map_location='cpu' )['model']
a__ = list(state_dict.keys() )
# extract state_dict for VQVAE
a__ = {}
a__ = 'first_stage_model.'
for key in keys:
if key.startswith(__lowerCAmelCase ):
a__ = state_dict[key]
# extract state_dict for UNetLDM
a__ = {}
a__ = 'model.diffusion_model.'
for key in keys:
if key.startswith(__lowerCAmelCase ):
a__ = state_dict[key]
a__ = config.model.params.first_stage_config.params
a__ = config.model.params.unet_config.params
a__ = VQModel(**__lowerCAmelCase ).eval()
vqvae.load_state_dict(__lowerCAmelCase )
a__ = UNetLDMModel(**__lowerCAmelCase ).eval()
unet.load_state_dict(__lowerCAmelCase )
a__ = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='scaled_linear' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=__lowerCAmelCase , )
a__ = LDMPipeline(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
pipeline.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
snake_case : int = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
snake_case : Optional[int] = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 657
|
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
snake_case : Dict = logging.get_logger(__name__)
snake_case : Any = {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] ):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )
if tokenizer_name is None:
a__ = TOKENIZER_CLASSES
else:
a__ = {tokenizer_name: getattr(__lowerCAmelCase , tokenizer_name + 'Fast' )}
logger.info(F'Loading tokenizer classes: {tokenizer_names}' )
for tokenizer_name in tokenizer_names:
a__ = TOKENIZER_CLASSES[tokenizer_name]
a__ = True
if checkpoint_name is None:
a__ = list(tokenizer_class.max_model_input_sizes.keys() )
else:
a__ = [checkpoint_name]
logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )
for checkpoint in checkpoint_names:
logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )
# Load tokenizer
a__ = tokenizer_class.from_pretrained(__lowerCAmelCase , force_download=__lowerCAmelCase )
# Save fast tokenizer
logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )
# For organization names we create sub-directories
if "/" in checkpoint:
a__ , a__ = checkpoint.split('/' )
a__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
elif add_prefix:
a__ = checkpoint
a__ = dump_path
else:
a__ = None
a__ = dump_path
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
a__ = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
a__ = file_path.split(__lowerCAmelCase )[-1][0]
if next_char == "/":
a__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
a__ = None
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
a__ = tokenizer.save_pretrained(
__lowerCAmelCase , legacy_format=__lowerCAmelCase , filename_prefix=__lowerCAmelCase )
logger.info(F'=> File names {file_names}' )
for file_name in file_names:
if not file_name.endswith('tokenizer.json' ):
os.remove(__lowerCAmelCase )
logger.info(F'=> removing {file_name}' )
if __name__ == "__main__":
snake_case : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
)
parser.add_argument(
'''--tokenizer_name''',
default=None,
type=str,
help=(
f"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
'''download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--checkpoint_name''',
default=None,
type=str,
help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
)
parser.add_argument(
'''--force_download''',
action='''store_true''',
help='''Re-download checkpoints.''',
)
snake_case : List[str] = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 657
| 1
|
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE__ : str = get_logger(__name__)
class UpperCAmelCase_ :
__lowerCamelCase = 'dummy_data'
__lowerCamelCase = 'datasets'
__lowerCamelCase = False
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = True , _lowerCAmelCase = None , ):
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : str = dataset_name
UpperCAmelCase__ : int = cache_dir
UpperCAmelCase__ : List[str] = use_local_dummy_data
UpperCAmelCase__ : List[str] = config
# download_callbacks take a single url as input
UpperCAmelCase__ : List[str] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
UpperCAmelCase__ : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
UpperCAmelCase__ : int = str(A__ )
# to be downloaded
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Optional[int] = None
@property
def __UpperCAmelCase ( self ):
if self._dummy_file is None:
UpperCAmelCase__ : Optional[Any] = self.download_dummy_data()
return self._dummy_file
@property
def __UpperCAmelCase ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("""dummy""" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("""dummy""" , self.version_name )
@property
def __UpperCAmelCase ( self ):
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : int = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
UpperCAmelCase__ : Optional[int] = cached_path(
A__ , cache_dir=self.cache_dir , extract_compressed_file=A__ , force_extract=A__ )
return os.path.join(A__ , self.dummy_file_name )
@property
def __UpperCAmelCase ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __UpperCAmelCase ( self ):
if self._bucket_url is None:
UpperCAmelCase__ : Any = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def __UpperCAmelCase ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , """/""" ).split("""/""" )[:-1] )
def __UpperCAmelCase ( self , _lowerCAmelCase , *_lowerCAmelCase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
UpperCAmelCase__ : Optional[Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
UpperCAmelCase__ : Any = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A__ , A__ ):
return self.create_dummy_data_dict(A__ , A__ )
elif isinstance(A__ , (list, tuple) ):
return self.create_dummy_data_list(A__ , A__ )
else:
return self.create_dummy_data_single(A__ , A__ )
def __UpperCAmelCase ( self , _lowerCAmelCase , *_lowerCAmelCase ):
return self.download_and_extract(A__ )
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
return self.download_and_extract(A__ )
def __UpperCAmelCase ( self , _lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase ):
return path
def __UpperCAmelCase ( self ):
return {}
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ : Optional[int] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A__ , A__ ):
for single_url in single_urls:
download_callback(A__ )
else:
UpperCAmelCase__ : int = single_urls
download_callback(A__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A__ , A__ ):
UpperCAmelCase__ : Union[str, Any] = [os.path.join(A__ , urllib.parse.quote_plus(Path(A__ ).name ) ) for x in single_urls]
else:
UpperCAmelCase__ : List[str] = single_urls
UpperCAmelCase__ : int = os.path.join(A__ , urllib.parse.quote_plus(Path(A__ ).name ) )
UpperCAmelCase__ : str = value
# make sure that values are unique
if all(isinstance(A__ , A__ ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
UpperCAmelCase__ : Any = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ : int = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
UpperCAmelCase__ : Optional[Any] = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , A__ ) ) for url in data_url )
UpperCAmelCase__ : Optional[Any] = all(
url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed""" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
UpperCAmelCase__ : str = [data_url[0]] * len(A__ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
UpperCAmelCase__ : List[Any] = os.path.join(A__ , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(A__ )
return dummy_data_list
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
for download_callback in self.download_callbacks:
download_callback(A__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
UpperCAmelCase__ : Any = os.path.join(A__ , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(A__ ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __UpperCAmelCase ( self ):
pass
def __UpperCAmelCase ( self ):
pass
def __UpperCAmelCase ( self , _lowerCAmelCase ):
def _iter_archive_members(_lowerCAmelCase ):
# this preserves the order of the members inside the ZIP archive
UpperCAmelCase__ : List[str] = Path(self.dummy_file ).parent
UpperCAmelCase__ : Tuple = path.relative_to(A__ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
UpperCAmelCase__ : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A__ )
UpperCAmelCase__ : List[Any] = Path(A__ )
UpperCAmelCase__ : Union[str, Any] = _iter_archive_members(A__ ) if self.use_local_dummy_data else path.rglob("""*""" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((""".""", """__""") ):
yield file_path.relative_to(A__ ).as_posix(), file_path.open("""rb""" )
def __UpperCAmelCase ( self , _lowerCAmelCase ):
if not isinstance(A__ , A__ ):
UpperCAmelCase__ : str = [paths]
for path in paths:
if os.path.isfile(A__ ):
if os.path.basename(A__ ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A__ ):
if os.path.basename(A__ ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(A__ ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(A__ , A__ )
| 79
|
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=2 , A__=32 , A__=16 , A__=3 , A__=True , A__=True , A__=32 , A__=4 , A__=[0, 1, 2, 3] , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=0.02 , A__=3 , A__=[1, 3_84, 24, 24] , A__=True , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = backbone_out_indices
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = backbone_featmap_shape
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
_SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
_SCREAMING_SNAKE_CASE = num_patches + 1
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
"""hidden_sizes""": [96, 1_92, 3_84, 7_68],
"""num_groups""": 2,
}
return DPTConfig(
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 , backbone_out_indices=self.backbone_out_indices , 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 , is_hybrid=self.is_hybrid , backbone_config=A__ , backbone_featmap_shape=self.backbone_featmap_shape , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = DPTModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = DPTForDepthEstimation(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = DPTForSemanticSegmentation(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = DPTModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> str:
self.config_tester.run_common_tests()
@unittest.skip(reason="""DPT does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> List[str]:
pass
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , nn.Linear ) )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
_SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = True
if model_class in get_values(A__ ):
continue
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.train()
_SCREAMING_SNAKE_CASE = self._prepare_for_class(A__ , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = model(**A__ ).loss
loss.backward()
def UpperCamelCase ( self ) -> Union[str, Any]:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = True
if model_class in get_values(A__ ) or not model_class.supports_gradient_checkpointing:
continue
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.gradient_checkpointing_enable()
model.train()
_SCREAMING_SNAKE_CASE = self._prepare_for_class(A__ , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = model(**A__ ).loss
loss.backward()
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = _config_zero_init(A__ )
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(config=A__ )
# Skip the check for the backbone
_SCREAMING_SNAKE_CASE = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
_SCREAMING_SNAKE_CASE = [F"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCamelCase ( self ) -> Any:
pass
@slow
def UpperCamelCase ( self ) -> List[Any]:
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
_SCREAMING_SNAKE_CASE = DPTModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def UpperCamelCase ( self ) -> List[Any]:
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = """add"""
with self.assertRaises(A__ ):
_SCREAMING_SNAKE_CASE = DPTForDepthEstimation(A__ )
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
@slow
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas""" )
_SCREAMING_SNAKE_CASE = DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas""" ).to(A__ )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""pt""" ).to(A__ )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**A__ )
_SCREAMING_SNAKE_CASE = outputs.predicted_depth
# verify the predicted depth
_SCREAMING_SNAKE_CASE = torch.Size((1, 3_84, 3_84) )
self.assertEqual(predicted_depth.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor(
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(A__ )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00 , A__ , atol=1E-4 ) )
| 591
| 0
|
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase_ ( a_ ):
'''simple docstring'''
_A : Dict = 'philschmid/bart-large-cnn-samsum'
_A : Optional[Any] = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
_A : str = 'summarizer'
_A : List[Any] = AutoTokenizer
_A : Any = AutoModelForSeqaSeqLM
_A : str = ['text']
_A : List[str] = ['text']
def UpperCamelCase_ ( self , snake_case__ ) -> str:
"""simple docstring"""
return self.pre_processor(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ )
def UpperCamelCase_ ( self , snake_case__ ) -> Tuple:
"""simple docstring"""
return self.model.generate(**snake_case__ )[0]
def UpperCamelCase_ ( self , snake_case__ ) -> List[Any]:
"""simple docstring"""
return self.pre_processor.decode(snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
| 716
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class UpperCamelCase_ ( a_ ):
_A : Tuple = 'canine'
def __init__( self , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1_63_84 , snake_case__=16 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=0xE_0_0_0 , snake_case__=0xE_0_0_1 , snake_case__=4 , snake_case__=4 , snake_case__=8 , snake_case__=1_63_84 , snake_case__=1_28 , **snake_case__ , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = type_vocab_size
UpperCAmelCase = layer_norm_eps
# Character config:
UpperCAmelCase = downsampling_rate
UpperCAmelCase = upsampling_kernel_size
UpperCAmelCase = num_hash_functions
UpperCAmelCase = num_hash_buckets
UpperCAmelCase = local_transformer_stride
| 378
| 0
|
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int = 200 ):
lowercase = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase = [0] * (pence + 1)
lowercase = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowercase_ , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 7_3682
| 588
|
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase_ : Any = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
lowercase_ : str = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[int] ):
lowercase = (images / 2 + 0.5).clamp(0 , 1 )
lowercase = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowercase = numpy_to_pil(lowercase_ )
return images
def SCREAMING_SNAKE_CASE ( lowercase_ : List[Any] ):
if images.ndim == 3:
lowercase = images[None, ...]
lowercase = (images * 255).round().astype("""uint8""" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
lowercase = [Image.fromarray(image.squeeze() , mode="""L""" ) for image in images]
else:
lowercase = [Image.fromarray(lowercase_ ) for image in images]
return pil_images
| 588
| 1
|
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class UpperCamelCase_ ( __snake_case , __snake_case ):
lowercase = 'pixel_values'
lowercase = False
lowercase = TimmBackboneConfig
def __init__( self , A , **A ) -> Tuple:
requires_backends(self , """timm""" )
super().__init__(__UpperCamelCase )
UpperCAmelCase : Optional[Any] = config
if config.backbone is None:
raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""" )
if config.backbone not in timm.list_models():
raise ValueError(f'''backbone {config.backbone} is not supported by timm.''' )
if hasattr(__UpperCamelCase , """out_features""" ) and config.out_features is not None:
raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )
UpperCAmelCase : Optional[Any] = getattr(__UpperCamelCase , """use_pretrained_backbone""" , __UpperCamelCase )
if pretrained is None:
raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""" )
# We just take the final layer by default. This matches the default for the transformers models.
UpperCAmelCase : Dict = config.out_indices if getattr(__UpperCamelCase , """out_indices""" , __UpperCamelCase ) is not None else (-1,)
UpperCAmelCase : Any = timm.create_model(
config.backbone , pretrained=__UpperCamelCase , features_only=config.features_only , in_chans=config.num_channels , out_indices=__UpperCamelCase , **__UpperCamelCase , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
UpperCAmelCase : int = self._backbone.return_layers
UpperCAmelCase : Dict = {layer["""module"""]: str(__UpperCamelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(__UpperCamelCase )
@classmethod
def _lowercase( cls , A , *A , **A ) -> List[str]:
requires_backends(cls , ["""vision""", """timm"""] )
from ...models.timm_backbone import TimmBackboneConfig
UpperCAmelCase : int = kwargs.pop("""config""" , TimmBackboneConfig() )
UpperCAmelCase : str = kwargs.pop("""use_timm_backbone""" , __UpperCamelCase )
if not use_timm:
raise ValueError("""use_timm_backbone must be True for timm backbones""" )
UpperCAmelCase : Dict = kwargs.pop("""num_channels""" , config.num_channels )
UpperCAmelCase : Optional[Any] = kwargs.pop("""features_only""" , config.features_only )
UpperCAmelCase : Tuple = kwargs.pop("""use_pretrained_backbone""" , config.use_pretrained_backbone )
UpperCAmelCase : Any = kwargs.pop("""out_indices""" , config.out_indices )
UpperCAmelCase : Union[str, Any] = TimmBackboneConfig(
backbone=__UpperCamelCase , num_channels=__UpperCamelCase , features_only=__UpperCamelCase , use_pretrained_backbone=__UpperCamelCase , out_indices=__UpperCamelCase , )
return super()._from_config(__UpperCamelCase , **__UpperCamelCase )
def _lowercase( self , A ) -> int:
pass
def _lowercase( self , A , A=None , A=None , A=None , **A ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : Any = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("""Cannot output attentions for timm backbones at the moment""" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
UpperCAmelCase : int = self._all_layers
UpperCAmelCase : List[str] = self._backbone(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase : Tuple = self._return_layers
UpperCAmelCase : Any = tuple(hidden_states[i] for i in self.out_indices )
else:
UpperCAmelCase : List[Any] = self._backbone(__UpperCamelCase , **__UpperCamelCase )
UpperCAmelCase : Any = None
UpperCAmelCase : Optional[int] = tuple(__UpperCamelCase )
UpperCAmelCase : List[Any] = tuple(__UpperCamelCase ) if hidden_states is not None else None
if not return_dict:
UpperCAmelCase : int = (feature_maps,)
if output_hidden_states:
UpperCAmelCase : Union[str, Any] = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=__UpperCamelCase , hidden_states=__UpperCamelCase , attentions=__UpperCamelCase )
| 702
|
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
a : int = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
a : int = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase_ ( datasets.Metric ):
def _lowercase( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase( self ) -> List[Any]:
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase( self , A , A , A=None , A="uniform_average" , A=True ) -> List[Any]:
UpperCAmelCase : List[Any] = mean_squared_error(
A , A , sample_weight=A , multioutput=A , squared=A )
return {"mse": mse}
| 672
| 0
|
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : Optional[int], lowerCAmelCase_ : int ):
__lowerCAmelCase = BertConfig.from_json_file(_A )
print(F"""Building PyTorch model from configuration: {config}""" )
__lowerCAmelCase = BertForPreTraining(_A )
# Load weights from tf checkpoint
load_tf_weights_in_bert(_A, _A, _A )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict(), _A )
if __name__ == "__main__":
_snake_case : Optional[int] = 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(
'--bert_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.'
)
_snake_case : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 53
|
from jiwer import compute_measures
import datasets
A_: Optional[int] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
A_: str = '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
A_: Dict = '\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
"""simple docstring"""
def _UpperCAmelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
] , )
def _UpperCAmelCase ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False ):
'''simple docstring'''
if concatenate_texts:
return compute_measures(UpperCAmelCase , UpperCAmelCase )["wer"]
else:
_lowercase = 0
_lowercase = 0
for prediction, reference in zip(UpperCAmelCase , UpperCAmelCase ):
_lowercase = compute_measures(UpperCAmelCase , UpperCAmelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 398
| 0
|
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class lowercase ( A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """xlnet"""
__SCREAMING_SNAKE_CASE = ["""mems"""]
__SCREAMING_SNAKE_CASE = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _snake_case=3_2000 , _snake_case=1024 , _snake_case=24 , _snake_case=16 , _snake_case=4096 , _snake_case="gelu" , _snake_case=True , _snake_case="bi" , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=0.1 , _snake_case=512 , _snake_case=None , _snake_case=True , _snake_case=False , _snake_case=False , _snake_case=-1 , _snake_case=False , _snake_case="last" , _snake_case=True , _snake_case="tanh" , _snake_case=0.1 , _snake_case=5 , _snake_case=5 , _snake_case=5 , _snake_case=1 , _snake_case=2 , **_snake_case , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = vocab_size
UpperCAmelCase = d_model
UpperCAmelCase = n_layer
UpperCAmelCase = n_head
if d_model % n_head != 0:
raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})""" )
UpperCAmelCase = d_model // n_head
UpperCAmelCase = ff_activation
UpperCAmelCase = d_inner
UpperCAmelCase = untie_r
UpperCAmelCase = attn_type
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = dropout
UpperCAmelCase = mem_len
UpperCAmelCase = reuse_len
UpperCAmelCase = bi_data
UpperCAmelCase = clamp_len
UpperCAmelCase = same_length
UpperCAmelCase = summary_type
UpperCAmelCase = summary_use_proj
UpperCAmelCase = summary_activation
UpperCAmelCase = summary_last_dropout
UpperCAmelCase = start_n_top
UpperCAmelCase = end_n_top
UpperCAmelCase = bos_token_id
UpperCAmelCase = pad_token_id
UpperCAmelCase = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , _snake_case , )
UpperCAmelCase = kwargs['''use_cache''']
UpperCAmelCase = use_mems_eval
UpperCAmelCase = use_mems_train
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
@property
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def snake_case_ ( self , _snake_case ) -> Dict:
"""simple docstring"""
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 718
|
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 lowercase ( A__ , A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
@register_to_config
def __init__( self , _snake_case = 1000 , _snake_case = None ) -> Dict:
"""simple docstring"""
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(_snake_case )
# standard deviation of the initial noise distribution
UpperCAmelCase = 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.
UpperCAmelCase = 4
# running values
UpperCAmelCase = []
def snake_case_ ( self , _snake_case , _snake_case = None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = num_inference_steps
UpperCAmelCase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
UpperCAmelCase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
UpperCAmelCase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
UpperCAmelCase = torch.sin(steps * math.pi / 2 ) ** 2
UpperCAmelCase = (1.0 - self.betas**2) ** 0.5
UpperCAmelCase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
UpperCAmelCase = timesteps.to(_snake_case )
UpperCAmelCase = []
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
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''' )
UpperCAmelCase = (self.timesteps == timestep).nonzero().item()
UpperCAmelCase = timestep_index + 1
UpperCAmelCase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_snake_case )
if len(self.ets ) == 1:
UpperCAmelCase = self.ets[-1]
elif len(self.ets ) == 2:
UpperCAmelCase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
UpperCAmelCase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
UpperCAmelCase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
UpperCAmelCase = self._get_prev_sample(_snake_case , _snake_case , _snake_case , _snake_case )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_snake_case )
def snake_case_ ( self , _snake_case , *_snake_case , **_snake_case ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.alphas[timestep_index]
UpperCAmelCase = self.betas[timestep_index]
UpperCAmelCase = self.alphas[prev_timestep_index]
UpperCAmelCase = self.betas[prev_timestep_index]
UpperCAmelCase = (sample - sigma * ets) / max(_snake_case , 1e-8 )
UpperCAmelCase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 391
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ : Any = {
'''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:
UpperCamelCase_ : Dict = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : Any = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : int = [
'''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
UpperCamelCase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 115
|
lowercase = 8.314_4598
def __lowerCAmelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> float:
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol""" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
lowercase = 3_0_0
lowercase = 2_8
lowercase = rms_speed_of_molecule(temperature, molar_mass)
print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 272
| 0
|
from math import isqrt
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , UpperCAmelCase_ , UpperCAmelCase_):
snake_case__ : Optional[Any] = False
return [i for i in range(2 , UpperCAmelCase_) if is_prime[i]]
def _lowercase ( UpperCAmelCase_ = 10**8):
"""simple docstring"""
snake_case__ : str = calculate_prime_numbers(max_number // 2)
snake_case__ : Dict = 0
snake_case__ : Dict = 0
snake_case__ : Optional[Any] = len(UpperCAmelCase_) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 127
|
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""")
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
snake_case__ : List[str] = grid[0]
for row_n in range(1 , len(UpperCAmelCase_)):
snake_case__ : List[str] = grid[row_n]
snake_case__ : Any = fill_row(UpperCAmelCase_ , UpperCAmelCase_)
snake_case__ : Union[str, Any] = grid[row_n]
return grid[-1][-1]
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(UpperCAmelCase_)):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n])
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 127
| 1
|
def lowerCAmelCase( __lowerCamelCase ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 559
|
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def lowerCAmelCase( __lowerCamelCase ):
__a = []
for line in lines:
__a = re.sub(r'#.*' , '' , __lowerCamelCase ) # remove comments
if line:
filtered_lines.append(__lowerCamelCase )
__a = '\n'.join(__lowerCamelCase )
# Make a hash from all this code
__a = full_str.encode('utf-8' )
return shaaaa(__lowerCamelCase ).hexdigest()
# get importable module names and hash for caching
lowerCamelCase_ : int = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
lowerCamelCase_ : List[Any] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase_ : int = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
lowerCamelCase_ : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 559
| 1
|
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""
UpperCAmelCase = """
Args:
predictions (`List[ndarray]`):
List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
references (`List[ndarray]`):
List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
num_labels (`int`):
Number of classes (categories).
ignore_index (`int`):
Index that will be ignored during evaluation.
nan_to_num (`int`, *optional*):
If specified, NaN values will be replaced by the number defined by the user.
label_map (`dict`, *optional*):
If specified, dictionary mapping old label indices to new label indices.
reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
Returns:
`Dict[str, float | ndarray]` comprising various elements:
- *mean_iou* (`float`):
Mean Intersection-over-Union (IoU averaged over all categories).
- *mean_accuracy* (`float`):
Mean accuracy (averaged over all categories).
- *overall_accuracy* (`float`):
Overall accuracy on all images.
- *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
Per category accuracy.
- *per_category_iou* (`ndarray` of shape `(num_labels,)`):
Per category IoU.
Examples:
>>> import numpy as np
>>> mean_iou = datasets.load_metric(\"mean_iou\")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
"""
UpperCAmelCase = """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""
def lowercase ( a__ : Optional[Any] , a__ : List[Any] , a__ : Tuple , a__ : bool , a__ : Optional[Dict[int, int]] = None , a__ : bool = False , ) -> Any:
if label_map is not None:
for old_id, new_id in label_map.items():
_UpperCamelCase = new_id
# turn into Numpy arrays
_UpperCamelCase = np.array(a__ )
_UpperCamelCase = np.array(a__ )
if reduce_labels:
_UpperCamelCase = 255
_UpperCamelCase = label - 1
_UpperCamelCase = 255
_UpperCamelCase = label != ignore_index
_UpperCamelCase = np.not_equal(a__ , a__ )
_UpperCamelCase = pred_label[mask]
_UpperCamelCase = np.array(a__ )[mask]
_UpperCamelCase = pred_label[pred_label == label]
_UpperCamelCase = np.histogram(a__ , bins=a__ , range=(0, num_labels - 1) )[0]
_UpperCamelCase = np.histogram(a__ , bins=a__ , range=(0, num_labels - 1) )[0]
_UpperCamelCase = np.histogram(a__ , bins=a__ , range=(0, num_labels - 1) )[0]
_UpperCamelCase = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def lowercase ( a__ : Optional[Any] , a__ : Optional[int] , a__ : int , a__ : bool , a__ : Optional[Dict[int, int]] = None , a__ : bool = False , ) -> Optional[Any]:
_UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
_UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
_UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
_UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a__ , a__ ):
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = intersect_and_union(
a__ , a__ , a__ , a__ , a__ , a__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def lowercase ( a__ : Optional[int] , a__ : Optional[Any] , a__ : str , a__ : bool , a__ : Optional[int] = None , a__ : Optional[Dict[int, int]] = None , a__ : bool = False , ) -> Any:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = total_intersect_and_union(
a__ , a__ , a__ , a__ , a__ , a__ )
# compute metrics
_UpperCamelCase = {}
_UpperCamelCase = total_area_intersect.sum() / total_area_label.sum()
_UpperCamelCase = total_area_intersect / total_area_union
_UpperCamelCase = total_area_intersect / total_area_label
_UpperCamelCase = np.nanmean(a__ )
_UpperCamelCase = np.nanmean(a__ )
_UpperCamelCase = all_acc
_UpperCamelCase = iou
_UpperCamelCase = acc
if nan_to_num is not None:
_UpperCamelCase = {metric: np.nan_to_num(a__ , nan=a__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCAmelCase_ ( datasets.Metric):
def _UpperCamelCase ( self : str ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
'''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
} ) , reference_urls=[
'''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
] , )
def _UpperCamelCase ( self : Dict , __UpperCamelCase : int , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : bool , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Optional[Dict[int, int]] = None , __UpperCamelCase : bool = False , ) -> Union[str, Any]:
_UpperCamelCase = mean_iou(
results=__UpperCamelCase , gt_seg_maps=__UpperCamelCase , num_labels=__UpperCamelCase , ignore_index=__UpperCamelCase , nan_to_num=__UpperCamelCase , label_map=__UpperCamelCase , reduce_labels=__UpperCamelCase , )
return iou_result
| 342
|
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
UpperCAmelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def lowercase ( a__ : Any , a__ : str , a__ : str , a__ : Optional[Any] , a__ : str ) -> Tuple:
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(a__ , a__ )
if weight_type is not None:
_UpperCamelCase = getattr(a__ , a__ ).shape
else:
_UpperCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowercase ( a__ : List[str] , a__ : Any ) -> str:
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
a__ , a__ , a__ , a__ , hf_model.config.feat_extract_norm == '''group''' , )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCamelCase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(a__ )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''' , a__ )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(a__ , a__ , a__ , a__ , a__ )
continue
if not is_used:
unused_weights.append(a__ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase ( a__ : List[str] , a__ : Union[str, Any] , a__ : List[str] , a__ : Optional[Any] , a__ : List[str] ) -> List[Any]:
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(a__ )
@torch.no_grad()
def lowercase ( a__ : Tuple , a__ : Dict , a__ : Optional[int]=None , a__ : Tuple=None , a__ : str=True ) -> Optional[Any]:
if config_path is not None:
_UpperCamelCase = UniSpeechSatConfig.from_pretrained(a__ )
else:
_UpperCamelCase = UniSpeechSatConfig()
_UpperCamelCase = ''''''
if is_finetuned:
_UpperCamelCase = UniSpeechSatForCTC(a__ )
else:
_UpperCamelCase = UniSpeechSatForPreTraining(a__ )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
_UpperCamelCase = model[0].eval()
recursively_load_weights(a__ , a__ )
hf_wavavec.save_pretrained(a__ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 342
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'processing_mctct': ['MCTCTProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MCTCTForCTC',
'MCTCTModel',
'MCTCTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
UpperCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50
|
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCamelCase__ ( lowercase__ : str , lowercase__ : List[Any] , lowercase__ : int ):
snake_case : Tuple = 0
if start < end:
snake_case : Tuple = randint(lowercase__ , lowercase__ )
snake_case : int = a[end]
snake_case : Dict = a[pivot]
snake_case : Dict = temp
snake_case , snake_case : int = _in_place_partition(lowercase__ , lowercase__ , lowercase__ )
count += _in_place_quick_sort(lowercase__ , lowercase__ , p - 1 )
count += _in_place_quick_sort(lowercase__ , p + 1 , lowercase__ )
return count
def UpperCamelCase__ ( lowercase__ : int , lowercase__ : Dict , lowercase__ : str ):
snake_case : Any = 0
snake_case : Optional[Any] = randint(lowercase__ , lowercase__ )
snake_case : Dict = a[end]
snake_case : str = a[pivot]
snake_case : List[str] = temp
snake_case : Optional[Any] = start - 1
for index in range(lowercase__ , lowercase__ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
snake_case : List[str] = new_pivot_index + 1
snake_case : Tuple = a[new_pivot_index]
snake_case : str = a[index]
snake_case : Dict = temp
snake_case : Dict = a[new_pivot_index + 1]
snake_case : str = a[end]
snake_case : List[str] = temp
return new_pivot_index + 1, count
__A = TemporaryFile()
__A = 100 # 1000 elements are to be sorted
__A , __A = 0, 1 # mean and standard deviation
__A = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
__A = np.load(outfile)
__A = len(M) - 1
__A = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 134
| 0
|
'''simple docstring'''
# Imports
import numpy as np
class __a :
def __init__( self : Dict ,lowerCamelCase : Optional[Any]=None ,lowerCamelCase : Dict=None ,lowerCamelCase : Optional[int]=None ,lowerCamelCase : Tuple=None ,lowerCamelCase : int=None ):
'''simple docstring'''
self.set_matricies(red=lowerCamelCase ,green=lowerCamelCase ,blue=lowerCamelCase ,red_edge=lowerCamelCase ,nir=lowerCamelCase )
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : Optional[int]=None ,lowerCamelCase : List[Any]=None ,lowerCamelCase : Optional[Any]=None ,lowerCamelCase : int=None ,lowerCamelCase : Any=None ):
'''simple docstring'''
if red is not None:
__SCREAMING_SNAKE_CASE = red
if green is not None:
__SCREAMING_SNAKE_CASE = green
if blue is not None:
__SCREAMING_SNAKE_CASE = blue
if red_edge is not None:
__SCREAMING_SNAKE_CASE = red_edge
if nir is not None:
__SCREAMING_SNAKE_CASE = nir
return True
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : List[str]="" ,lowerCamelCase : Tuple=None ,lowerCamelCase : int=None ,lowerCamelCase : Optional[int]=None ,lowerCamelCase : List[str]=None ,lowerCamelCase : str=None ):
'''simple docstring'''
self.set_matricies(red=lowerCamelCase ,green=lowerCamelCase ,blue=lowerCamelCase ,red_edge=lowerCamelCase ,nir=lowerCamelCase )
__SCREAMING_SNAKE_CASE = {
"""ARVI2""": self.arvaa,
"""CCCI""": self.ccci,
"""CVI""": self.cvi,
"""GLI""": self.gli,
"""NDVI""": self.ndvi,
"""BNDVI""": self.bndvi,
"""redEdgeNDVI""": self.red_edge_ndvi,
"""GNDVI""": self.gndvi,
"""GBNDVI""": self.gbndvi,
"""GRNDVI""": self.grndvi,
"""RBNDVI""": self.rbndvi,
"""PNDVI""": self.pndvi,
"""ATSAVI""": self.atsavi,
"""BWDRVI""": self.bwdrvi,
"""CIgreen""": self.ci_green,
"""CIrededge""": self.ci_rededge,
"""CI""": self.ci,
"""CTVI""": self.ctvi,
"""GDVI""": self.gdvi,
"""EVI""": self.evi,
"""GEMI""": self.gemi,
"""GOSAVI""": self.gosavi,
"""GSAVI""": self.gsavi,
"""Hue""": self.hue,
"""IVI""": self.ivi,
"""IPVI""": self.ipvi,
"""I""": self.i,
"""RVI""": self.rvi,
"""MRVI""": self.mrvi,
"""MSAVI""": self.m_savi,
"""NormG""": self.norm_g,
"""NormNIR""": self.norm_nir,
"""NormR""": self.norm_r,
"""NGRDI""": self.ngrdi,
"""RI""": self.ri,
"""S""": self.s,
"""IF""": self._if,
"""DVI""": self.dvi,
"""TVI""": self.tvi,
"""NDRE""": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("""Index not in the list!""" )
return False
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase__ ( self : List[str] ,lowerCamelCase : List[str]=0.08 ,lowerCamelCase : Dict=1.22 ,lowerCamelCase : int=0.03 ):
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
return (self.nir / self.green) - 1
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.red - self.blue) / self.red
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return self.nir - self.green
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : Union[str, Any]=0.16 ):
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase__ ( self : Tuple ,lowerCamelCase : Optional[int]=0.5 ):
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def UpperCAmelCase__ ( self : List[str] ,lowerCamelCase : List[Any]=None ,lowerCamelCase : Union[str, Any]=None ):
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
__SCREAMING_SNAKE_CASE = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 13
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__UpperCAmelCase , __UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __UpperCAmelCase ):
for row in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(__UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__UpperCAmelCase )
]
def __magic_name__ ( __UpperCAmelCase ) -> Callable[[int], int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(__UpperCAmelCase )] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(__UpperCAmelCase , __UpperCAmelCase )
def interpolated_func(__UpperCAmelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__UpperCAmelCase ) )
return interpolated_func
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __magic_name__ ( __UpperCAmelCase = question_function , __UpperCAmelCase = 10 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [func(__UpperCAmelCase ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(__UpperCAmelCase ) == poly(__UpperCAmelCase ):
x_val += 1
ret += poly(__UpperCAmelCase )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 13
| 1
|
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : Union[str, Any] = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
_snake_case : Union[str, Any] = {
"vocab_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
},
"merges_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
},
"tokenizer_config_file": {
"facebook/blenderbot_small-90M": (
"https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
)
},
}
_snake_case : int = {"facebook/blenderbot_small-90M": 512}
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : Tuple = set()
__snake_case : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__snake_case : Optional[int] = char
__snake_case : Dict = set(__lowerCamelCase )
return pairs
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
__UpperCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Union[str, Any] = ["input_ids", "attention_mask"]
def __init__( self : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : List[Any] , lowerCamelCase : Dict="__start__" , lowerCamelCase : int="__end__" , lowerCamelCase : Optional[Any]="__unk__" , lowerCamelCase : Union[str, Any]="__null__" , **lowerCamelCase : Dict , ) -> Tuple:
super().__init__(unk_token=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , pad_token=lowerCamelCase , **lowerCamelCase )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__snake_case : Optional[int] = json.load(lowerCamelCase )
__snake_case : int = {v: k for k, v in self.encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__snake_case : Union[str, Any] = merges_handle.read().split("\n" )[1:-1]
__snake_case : str = [tuple(merge.split() ) for merge in merges]
__snake_case : Optional[int] = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__snake_case : int = {}
@property
def __snake_case ( self : List[str] ) -> int:
return len(self.encoder )
def __snake_case ( self : int ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def __snake_case ( self : str , lowerCamelCase : str ) -> str:
if token in self.cache:
return self.cache[token]
__snake_case : str = re.sub("([.,!?()])" , R" \1" , lowerCamelCase )
__snake_case : Union[str, Any] = re.sub("(')" , R" \1 " , lowerCamelCase )
__snake_case : Optional[int] = re.sub(R"\s{2,}" , " " , lowerCamelCase )
if "\n" in token:
__snake_case : Tuple = token.replace("\n" , " __newln__" )
__snake_case : Optional[Any] = token.split(" " )
__snake_case : Dict = []
for token in tokens:
if not len(lowerCamelCase ):
continue
__snake_case : List[Any] = token.lower()
__snake_case : Optional[Any] = tuple(lowerCamelCase )
__snake_case : str = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__snake_case : Optional[Any] = get_pairs(lowerCamelCase )
if not pairs:
words.append(lowerCamelCase )
continue
while True:
__snake_case : Optional[Any] = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__snake_case , __snake_case : Dict = bigram
__snake_case : int = []
__snake_case : Optional[int] = 0
while i < len(lowerCamelCase ):
try:
__snake_case : Optional[int] = word.index(lowerCamelCase , lowerCamelCase )
new_word.extend(word[i:j] )
__snake_case : Optional[int] = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__snake_case : List[Any] = tuple(lowerCamelCase )
__snake_case : Union[str, Any] = new_word
if len(lowerCamelCase ) == 1:
break
else:
__snake_case : Tuple = get_pairs(lowerCamelCase )
__snake_case : List[Any] = "@@ ".join(lowerCamelCase )
__snake_case : List[str] = word[:-4]
__snake_case : List[Any] = word
words.append(lowerCamelCase )
return " ".join(lowerCamelCase )
def __snake_case ( self : int , lowerCamelCase : str ) -> List[str]:
__snake_case : int = []
__snake_case : Optional[int] = re.findall(R"\S+\n?" , lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCamelCase ).split(" " ) ) )
return split_tokens
def __snake_case ( self : Optional[Any] , lowerCamelCase : str ) -> int:
__snake_case : str = token.lower()
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def __snake_case ( self : List[Any] , lowerCamelCase : int ) -> str:
return self.decoder.get(lowerCamelCase , self.unk_token )
def __snake_case ( self : Dict , lowerCamelCase : List[str] ) -> str:
__snake_case : List[str] = " ".join(lowerCamelCase ).replace("@@ " , "" ).strip()
return out_string
def __snake_case ( self : Tuple , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__snake_case : int = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__snake_case : Tuple = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__snake_case : Tuple = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
" Please check that the tokenizer is not corrupted!" )
__snake_case : Dict = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
| 81
|
from PIL import Image
def A__ ( _a : Image , _a : float ):
'''simple docstring'''
def brightness(_a : int ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(_a )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change brightness to 100
__lowerCamelCase : str = change_brightness(img, 1_00)
brigt_img.save("""image_data/lena_brightness.png""", format="""png""")
| 385
| 0
|
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_lowercase : List[str] =logging.get_logger(__name__)
def UpperCAmelCase ( lowercase__ : int , lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : int=False ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
if not is_sharded:
a__ = os.path.abspath(lowercase__ )
logger.info(f'Loading PyTorch weights from {pt_path}' )
a__ = torch.load(lowercase__ , map_location="""cpu""" )
logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.' )
a__ = convert_pytorch_state_dict_to_flax(lowercase__ , lowercase__ )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
a__ = convert_pytorch_sharded_state_dict_to_flax(lowercase__ , lowercase__ )
return flax_state_dict
def UpperCAmelCase ( lowercase__ : Tuple[str] , lowercase__ : np.ndarray , lowercase__ : Dict[str, jnp.ndarray] , lowercase__ : str , ):
'''simple docstring'''
def is_key_or_prefix_key_in_dict(lowercase__ : Tuple[str] ) -> bool:
return len(set(lowercase__ ) & {key, (model_prefix,) + key} ) > 0
# layer norm
a__ = pt_tuple_key[:-1] + ("""scale""",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowercase__ ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
a__ = pt_tuple_key[:-1] + ("""mean""",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowercase__ ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
a__ = pt_tuple_key[:-1] + ("""var""",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowercase__ ):
return renamed_pt_tuple_key, pt_tensor
# embedding
a__ = pt_tuple_key[:-1] + ("""embedding""",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowercase__ ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
a__ = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowercase__ ):
a__ = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
a__ = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowercase__ ):
a__ = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
a__ = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
a__ = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
a__ = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
a__ = pt_tuple_key[-2] + """_g"""
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
a__ = pt_tuple_key[-2] + """_v"""
if name is not None:
a__ = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase ( lowercase__ : Any , lowercase__ : Any ):
'''simple docstring'''
a__ = {k: v.numpy() for k, v in pt_state_dict.items()}
a__ = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
a__ = flax_model.params["""params"""]
else:
a__ = flax_model.params
a__ = flatten_dict(lowercase__ )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
a__ = flatten_dict(flax_model.params["""batch_stats"""] )
random_flax_state_dict.update(lowercase__ )
a__ = {}
a__ = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
a__ = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
a__ = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
a__ = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
a__ = pt_tuple_key[1:]
# Correctly rename weight parameters
a__ , a__ = rename_key_and_reshape_tensor(
lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# add model prefix if necessary
a__ = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
a__ = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
a__ = jnp.asarray(lowercase__ )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowercase__ , lowercase__ )
continue
# also add unexpected weight so that warning is thrown
a__ = jnp.asarray(lowercase__ )
else:
# also add unexpected weight so that warning is thrown
a__ = jnp.asarray(lowercase__ )
return unflatten_dict(lowercase__ )
def UpperCAmelCase ( lowercase__ : List[Any] , lowercase__ : List[Any] ):
'''simple docstring'''
import torch
# Load the index
a__ = {}
for shard_file in shard_filenames:
# load using msgpack utils
a__ = torch.load(lowercase__ )
a__ = {k: v.numpy() for k, v in pt_state_dict.items()}
a__ = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
a__ = flax_model.params["""params"""]
a__ = flatten_dict(lowercase__ )
random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
else:
a__ = flax_model.params
a__ = flatten_dict(lowercase__ )
a__ = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
a__ = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
a__ = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
a__ = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
a__ = pt_tuple_key[1:]
# Correctly rename weight parameters
a__ , a__ = rename_key_and_reshape_tensor(
lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# add model prefix if necessary
a__ = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
a__ = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
a__ = jnp.asarray(lowercase__ )
continue
if "var" in flax_key[-1]:
a__ = jnp.asarray(lowercase__ )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowercase__ , lowercase__ )
continue
# also add unexpected weight so that warning is thrown
a__ = jnp.asarray(lowercase__ )
else:
# also add unexpected weight so that warning is thrown
a__ = jnp.asarray(lowercase__ )
return unflatten_dict(lowercase__ )
def UpperCAmelCase ( lowercase__ : Optional[int] , lowercase__ : int ):
'''simple docstring'''
a__ = os.path.abspath(lowercase__ )
logger.info(f'Loading Flax weights from {flax_checkpoint_path}' )
# import correct flax class
a__ = getattr(lowercase__ , """Flax""" + model.__class__.__name__ )
# load flax weight dict
with open(lowercase__ , """rb""" ) as state_f:
try:
a__ = from_bytes(lowercase__ , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(lowercase__ , lowercase__ )
def UpperCAmelCase ( lowercase__ : Union[str, Any] , lowercase__ : List[str] ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
a__ = flatten_dict(jax.tree_util.tree_map(lambda lowercase__ : x.dtype == jnp.bfloataa , lowercase__ ) ).values()
if any(lowercase__ ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
a__ = jax.tree_util.tree_map(
lambda lowercase__ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , lowercase__ )
a__ = flatten_dict(lowercase__ )
a__ = pt_model.state_dict()
a__ = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
a__ = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
a__ = []
a__ = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
a__ = flax_key_tuple[0] == pt_model.base_model_prefix
a__ = """.""".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
a__ = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
a__ = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowercase__ ) not in pt_model_dict:
# conv layer
a__ = flax_key_tuple[:-1] + ("""weight""",)
a__ = jnp.transpose(lowercase__ , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase__ ) not in pt_model_dict:
# linear layer
a__ = flax_key_tuple[:-1] + ("""weight""",)
a__ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
a__ = flax_key_tuple[:-1] + ("""weight""",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
a__ = flax_key_tuple[:-1] + ("""running_mean""",)
elif "var" in flax_key_tuple[-1]:
a__ = flax_key_tuple[:-1] + ("""running_var""",)
if "batch_stats" in flax_state:
a__ = """.""".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
a__ = """.""".join(lowercase__ )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
a__ = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
a__ = key.split(""".""" )
a__ = None
if key_components[-3::2] == ["parametrizations", "original0"]:
a__ = key_components[-2] + """_g"""
elif key_components[-3::2] == ["parametrizations", "original1"]:
a__ = key_components[-2] + """_v"""
if name is not None:
a__ = key_components[:-3] + [name]
a__ = """.""".join(lowercase__ )
a__ = key
if flax_key in special_pt_names:
a__ = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
a__ = np.asarray(lowercase__ ) if not isinstance(lowercase__ , np.ndarray ) else flax_tensor
a__ = torch.from_numpy(lowercase__ )
# remove from missing keys
missing_keys.remove(lowercase__ )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(lowercase__ )
pt_model.load_state_dict(lowercase__ )
# re-transform missing_keys to list
a__ = list(lowercase__ )
if len(lowercase__ ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
else:
logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n' )
if len(lowercase__ ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
""" use it for predictions and inference.""" )
else:
logger.warning(
f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
"""If your task is similar to the task the model of the checkpoint was trained on, """
f'you can already use {pt_model.__class__.__name__} for predictions without further training.' )
return pt_model
| 412
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
a__ = list(lowerCamelCase )
a__ = degree
def __add__( self , lowerCamelCase ):
'''simple docstring'''
if self.degree > polynomial_a.degree:
a__ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , lowerCamelCase )
else:
a__ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , lowerCamelCase )
def __sub__( self , lowerCamelCase ):
'''simple docstring'''
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self ):
'''simple docstring'''
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self , lowerCamelCase ):
'''simple docstring'''
a__ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , lowerCamelCase )
def _A ( self , lowerCamelCase ):
'''simple docstring'''
a__ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self ):
'''simple docstring'''
a__ = """"""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(lowerCamelCase )
return polynomial
def __repr__( self ):
'''simple docstring'''
return self.__str__()
def _A ( self ):
'''simple docstring'''
a__ = [0] * self.degree
for i in range(self.degree ):
a__ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , lowerCamelCase )
def _A ( self , lowerCamelCase = 0 ):
'''simple docstring'''
a__ = [0] * (self.degree + 2)
a__ = constant
for i in range(self.degree + 1 ):
a__ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , lowerCamelCase )
def __eq__( self , lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self , lowerCamelCase ):
'''simple docstring'''
return not self.__eq__(lowerCamelCase )
| 412
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : Any = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'OwlViTConfig',
'OwlViTOnnxConfig',
'OwlViTTextConfig',
'OwlViTVisionConfig',
],
'processing_owlvit': ['OwlViTProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = ['OwlViTFeatureExtractor']
a_ : List[str] = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = [
'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OwlViTModel',
'OwlViTPreTrainedModel',
'OwlViTTextModel',
'OwlViTVisionModel',
'OwlViTForObjectDetection',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
a_ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 73
|
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = OrderedDict()
for key, value in state_dict.items():
if key.startswith('module.encoder' ):
_UpperCAmelCase = key.replace('module.encoder' , 'glpn.encoder' )
if key.startswith('module.decoder' ):
_UpperCAmelCase = key.replace('module.decoder' , 'decoder.stages' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_UpperCAmelCase = key[key.find('patch_embed' ) + len('patch_embed' )]
_UpperCAmelCase = key.replace(f'patch_embed{idx}' , f'patch_embeddings.{int(A )-1}' )
if "norm" in key:
_UpperCAmelCase = key.replace('norm' , 'layer_norm' )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_UpperCAmelCase = key[key.find('glpn.encoder.layer_norm' ) + len('glpn.encoder.layer_norm' )]
_UpperCAmelCase = key.replace(f'layer_norm{idx}' , f'layer_norm.{int(A )-1}' )
if "layer_norm1" in key:
_UpperCAmelCase = key.replace('layer_norm1' , 'layer_norm_1' )
if "layer_norm2" in key:
_UpperCAmelCase = key.replace('layer_norm2' , 'layer_norm_2' )
if "block" in key:
# replace for example block1 by block.0
_UpperCAmelCase = key[key.find('block' ) + len('block' )]
_UpperCAmelCase = key.replace(f'block{idx}' , f'block.{int(A )-1}' )
if "attn.q" in key:
_UpperCAmelCase = key.replace('attn.q' , 'attention.self.query' )
if "attn.proj" in key:
_UpperCAmelCase = key.replace('attn.proj' , 'attention.output.dense' )
if "attn" in key:
_UpperCAmelCase = key.replace('attn' , 'attention.self' )
if "fc1" in key:
_UpperCAmelCase = key.replace('fc1' , 'dense1' )
if "fc2" in key:
_UpperCAmelCase = key.replace('fc2' , 'dense2' )
if "linear_pred" in key:
_UpperCAmelCase = key.replace('linear_pred' , 'classifier' )
if "linear_fuse" in key:
_UpperCAmelCase = key.replace('linear_fuse.conv' , 'linear_fuse' )
_UpperCAmelCase = key.replace('linear_fuse.bn' , 'batch_norm' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_UpperCAmelCase = key[key.find('linear_c' ) + len('linear_c' )]
_UpperCAmelCase = key.replace(f'linear_c{idx}' , f'linear_c.{int(A )-1}' )
if "bot_conv" in key:
_UpperCAmelCase = key.replace('bot_conv' , '0.convolution' )
if "skip_conv1" in key:
_UpperCAmelCase = key.replace('skip_conv1' , '1.convolution' )
if "skip_conv2" in key:
_UpperCAmelCase = key.replace('skip_conv2' , '2.convolution' )
if "fusion1" in key:
_UpperCAmelCase = key.replace('fusion1' , '1.fusion' )
if "fusion2" in key:
_UpperCAmelCase = key.replace('fusion2' , '2.fusion' )
if "fusion3" in key:
_UpperCAmelCase = key.replace('fusion3' , '3.fusion' )
if "fusion" in key and "conv" in key:
_UpperCAmelCase = key.replace('conv' , 'convolutional_layer' )
if key.startswith('module.last_layer_depth' ):
_UpperCAmelCase = key.replace('module.last_layer_depth' , 'head.head' )
_UpperCAmelCase = value
return new_state_dict
def UpperCAmelCase ( A : Union[str, Any] , A : Union[str, Any] ):
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_UpperCAmelCase = state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
_UpperCAmelCase = state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
_UpperCAmelCase = kv_weight[
: config.hidden_sizes[i], :
]
_UpperCAmelCase = kv_bias[: config.hidden_sizes[i]]
_UpperCAmelCase = kv_weight[
config.hidden_sizes[i] :, :
]
_UpperCAmelCase = kv_bias[config.hidden_sizes[i] :]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(A , stream=A ).raw )
return image
@torch.no_grad()
def UpperCAmelCase ( A : int , A : List[Any] , A : Optional[int]=False , A : int=None ):
'''simple docstring'''
_UpperCAmelCase = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_UpperCAmelCase = GLPNImageProcessor()
# prepare image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt' ).pixel_values
logger.info('Converting model...' )
# load original state dict
_UpperCAmelCase = torch.load(A , map_location=torch.device('cpu' ) )
# rename keys
_UpperCAmelCase = rename_keys(A )
# key and value matrices need special treatment
read_in_k_v(A , A )
# create HuggingFace model and load state dict
_UpperCAmelCase = GLPNForDepthEstimation(A )
model.load_state_dict(A )
model.eval()
# forward pass
_UpperCAmelCase = model(A )
_UpperCAmelCase = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_UpperCAmelCase = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
_UpperCAmelCase = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f'Unknown model name: {model_name}' )
_UpperCAmelCase = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , A , atol=1e-4 )
print('Looks ok!' )
# finally, push to hub if required
if push_to_hub:
logger.info('Pushing model and image processor to the hub...' )
model.push_to_hub(
repo_path_or_name=Path(A , A ) , organization='nielsr' , commit_message='Add model' , use_temp_dir=A , )
image_processor.push_to_hub(
repo_path_or_name=Path(A , A ) , organization='nielsr' , commit_message='Add image processor' , use_temp_dir=A , )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''',
default=None,
type=str,
help='''Path to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
parser.add_argument(
'''--model_name''',
default='''glpn-kitti''',
type=str,
help='''Name of the model in case you\'re pushing to the hub.''',
)
lowercase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 573
| 0
|
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Dict = 'char'
UpperCAmelCase__ : Union[str, Any] = 'bpe'
UpperCAmelCase__ : str = 'wp'
UpperCAmelCase_ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['image_processor', 'char_tokenizer']
UpperCAmelCase__ : int = 'ViTImageProcessor'
UpperCAmelCase__ : Any = 'MgpstrTokenizer'
def __init__( self: Union[str, Any] , UpperCamelCase_: Union[str, Any]=None , UpperCamelCase_: Any=None , **UpperCamelCase_: Union[str, Any] ):
__lowerCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , UpperCamelCase_ , )
__lowerCamelCase = kwargs.pop("""feature_extractor""" )
__lowerCamelCase = 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`.""" )
__lowerCamelCase = tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained("""gpt2""" )
__lowerCamelCase = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(UpperCamelCase_ , UpperCamelCase_ )
def __call__( self: Optional[Any] , UpperCamelCase_: Optional[Any]=None , UpperCamelCase_: Tuple=None , UpperCamelCase_: Optional[Any]=None , **UpperCamelCase_: Union[str, Any] ):
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
__lowerCamelCase = self.image_processor(UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
if text is not None:
__lowerCamelCase = self.char_tokenizer(UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
__lowerCamelCase = encodings["""input_ids"""]
return inputs
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: Any ):
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase = sequences
__lowerCamelCase = char_preds.size(0 )
__lowerCamelCase, __lowerCamelCase = self._decode_helper(UpperCamelCase_ , """char""" )
__lowerCamelCase, __lowerCamelCase = self._decode_helper(UpperCamelCase_ , """bpe""" )
__lowerCamelCase, __lowerCamelCase = self._decode_helper(UpperCamelCase_ , """wp""" )
__lowerCamelCase = []
__lowerCamelCase = []
for i in range(UpperCamelCase_ ):
__lowerCamelCase = [char_scores[i], bpe_scores[i], wp_scores[i]]
__lowerCamelCase = [char_strs[i], bpe_strs[i], wp_strs[i]]
__lowerCamelCase = scores.index(max(UpperCamelCase_ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
__lowerCamelCase = {}
__lowerCamelCase = final_strs
__lowerCamelCase = final_scores
__lowerCamelCase = char_strs
__lowerCamelCase = bpe_strs
__lowerCamelCase = wp_strs
return out
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Tuple ):
if format == DecodeType.CHARACTER:
__lowerCamelCase = self.char_decode
__lowerCamelCase = 1
__lowerCamelCase = """[s]"""
elif format == DecodeType.BPE:
__lowerCamelCase = self.bpe_decode
__lowerCamelCase = 2
__lowerCamelCase = """#"""
elif format == DecodeType.WORDPIECE:
__lowerCamelCase = self.wp_decode
__lowerCamelCase = 1_02
__lowerCamelCase = """[SEP]"""
else:
raise ValueError(F'Format {format} is not supported.' )
__lowerCamelCase, __lowerCamelCase = [], []
__lowerCamelCase = pred_logits.size(0 )
__lowerCamelCase = pred_logits.size(1 )
__lowerCamelCase, __lowerCamelCase = pred_logits.topk(1 , dim=-1 , largest=UpperCamelCase_ , sorted=UpperCamelCase_ )
__lowerCamelCase = preds_index.view(-1 , UpperCamelCase_ )[:, 1:]
__lowerCamelCase = decoder(UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = torch.nn.functional.softmax(UpperCamelCase_ , dim=2 ).max(dim=2 )
__lowerCamelCase = preds_max_prob[:, 1:]
for index in range(UpperCamelCase_ ):
__lowerCamelCase = preds_str[index].find(UpperCamelCase_ )
__lowerCamelCase = preds_str[index][:pred_eos]
__lowerCamelCase = preds_index[index].cpu().tolist()
__lowerCamelCase = pred_index.index(UpperCamelCase_ ) if eos_token in pred_index else -1
__lowerCamelCase = preds_max_prob[index][: pred_eos_index + 1]
__lowerCamelCase = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(UpperCamelCase_ )
conf_scores.append(UpperCamelCase_ )
return dec_strs, conf_scores
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: Dict ):
__lowerCamelCase = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(UpperCamelCase_ )]
return decode_strs
def lowerCAmelCase__ ( self: int , UpperCamelCase_: Any ):
return self.bpe_tokenizer.batch_decode(UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Any ):
__lowerCamelCase = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase_ )]
return decode_strs
| 80
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
'cl-tohoku/bert-base-japanese-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
),
'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Tuple = 'bert'
def __init__( self: List[str] , UpperCamelCase_: str=3_05_22 , UpperCamelCase_: Optional[int]=7_68 , UpperCamelCase_: Tuple=12 , UpperCamelCase_: int=12 , UpperCamelCase_: int=30_72 , UpperCamelCase_: Optional[int]="gelu" , UpperCamelCase_: Optional[Any]=0.1 , UpperCamelCase_: List[Any]=0.1 , UpperCamelCase_: Optional[int]=5_12 , UpperCamelCase_: List[Any]=2 , UpperCamelCase_: int=0.02 , UpperCamelCase_: List[str]=1E-12 , UpperCamelCase_: Dict=0 , UpperCamelCase_: List[Any]="absolute" , UpperCamelCase_: Tuple=True , UpperCamelCase_: Tuple=None , **UpperCamelCase_: Optional[Any] , ):
super().__init__(pad_token_id=UpperCamelCase_ , **UpperCamelCase_ )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = position_embedding_type
__lowerCamelCase = use_cache
__lowerCamelCase = classifier_dropout
class lowerCamelCase__( __lowerCamelCase):
@property
def lowerCAmelCase__ ( self: Any ):
if self.task == "multiple-choice":
__lowerCamelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__lowerCamelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 80
| 1
|
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
'''
_lowerCamelCase : List[str] = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''
_lowerCamelCase : Dict = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the CUAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
\'aupr\': Area Under the Precision-Recall curve
\'prec_at_80_recall\': Precision at 80% recall
\'prec_at_90_recall\': Precision at 90% recall
Examples:
>>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> cuad_metric = datasets.load_metric("cuad")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
'''simple docstring'''
def A ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': {
'id': datasets.Value('string' ),
'prediction_text': datasets.features.Sequence(datasets.Value('string' ) ),
},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) , codebase_urls=['https://www.atticusprojectai.org/cuad'] , reference_urls=['https://www.atticusprojectai.org/cuad'] , )
def A ( self : str , lowercase : Optional[int] , lowercase : int ):
'''simple docstring'''
_snake_case = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
_snake_case = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
_snake_case = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 686
|
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a_ ( ) -> Optional[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(__lowercase ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def a_ ( ) -> Optional[int]:
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def a_ ( ) -> Dict:
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(__lowercase ):
http_head('https://huggingface.co' )
| 686
| 1
|
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase = """facebook/wmt19-en-de"""
__lowerCAmelCase = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
__lowerCAmelCase = FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')
# Test
__lowerCAmelCase = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__lowerCAmelCase = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
__lowerCAmelCase = """tiny-wmt19-en-de"""
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 319
|
'''simple docstring'''
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[str] ,_a : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
_a : List[Any] = n
_a : int = [
[math.inf for j in range(0 ,_a )] for i in range(0 ,_a )
] # adjacency matrix for weight
_a : List[Any] = [
[math.inf for j in range(0 ,_a )] for i in range(0 ,_a )
] # dp[i][j] stores minimum distance from i to j
def __lowercase ( self : List[str] ,_a : Optional[Any] ,_a : List[Any] ,_a : Union[str, Any] ):
'''simple docstring'''
_a : str = w
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
for k in range(0 ,self.n ):
for i in range(0 ,self.n ):
for j in range(0 ,self.n ):
_a : Optional[Any] = min(self.dp[i][j] ,self.dp[i][k] + self.dp[k][j] )
def __lowercase ( self : Union[str, Any] ,_a : Optional[int] ,_a : int ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 319
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2m_100': ['M2M100Tokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
'M2M100ForConditionalGeneration',
'M2M100Model',
'M2M100PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5
|
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int =6_5521
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->int:
_lowerCamelCase : Union[str, Any] = 1
_lowerCamelCase : List[str] = 0
for plain_chr in plain_text:
_lowerCamelCase : Dict = (a + ord(SCREAMING_SNAKE_CASE_ )) % MOD_ADLER
_lowerCamelCase : Tuple = (b + a) % MOD_ADLER
return (b << 16) | a
| 434
| 0
|
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
_snake_case : Optional[int] = get_logger(__name__)
class __SCREAMING_SNAKE_CASE :
def __init__( self, _a = None ) -> str:
__SCREAMING_SNAKE_CASE = (
os.path.join(_a, config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
__SCREAMING_SNAKE_CASE = Extractor
def __lowerCAmelCase ( self, _a ) -> 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"
__SCREAMING_SNAKE_CASE = os.path.abspath(_a )
return os.path.join(self.extract_dir, hash_url_to_filename(_a ) )
def __lowerCAmelCase ( self, _a, _a ) -> bool:
return force_extract or (
not os.path.isfile(_a ) and not (os.path.isdir(_a ) and os.listdir(_a ))
)
def __lowerCAmelCase ( self, _a, _a = False ) -> str:
__SCREAMING_SNAKE_CASE = self.extractor.infer_extractor_format(_a )
if not extractor_format:
return input_path
__SCREAMING_SNAKE_CASE = self._get_output_path(_a )
if self._do_extract(_a, _a ):
self.extractor.extract(_a, _a, _a )
return output_path
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
@classmethod
@abstractmethod
def __lowerCAmelCase ( cls, _a, **_a ) -> bool:
...
@staticmethod
@abstractmethod
def __lowerCAmelCase ( _a, _a ) -> None:
...
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> Union[str, Any]:
with open(_a, "rb" ) as f:
return f.read(_a )
@classmethod
def __lowerCAmelCase ( cls, _a, _a = b"" ) -> bool:
if not magic_number:
__SCREAMING_SNAKE_CASE = max(len(_a ) for cls_magic_number in cls.magic_numbers )
try:
__SCREAMING_SNAKE_CASE = 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 ( __SCREAMING_SNAKE_CASE ):
@classmethod
def __lowerCAmelCase ( cls, _a, **_a ) -> bool:
return tarfile.is_tarfile(_a )
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> int:
def resolved(_a ) -> str:
return os.path.realpath(os.path.abspath(_a ) )
def badpath(_a, _a ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(_a, _a ) ).startswith(_a )
def badlink(_a, _a ) -> bool:
# Links are interpreted relative to the directory containing the link
__SCREAMING_SNAKE_CASE = resolved(os.path.join(_a, os.path.dirname(info.name ) ) )
return badpath(info.linkname, base=_a )
__SCREAMING_SNAKE_CASE = 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 __lowerCAmelCase ( _a, _a ) -> None:
os.makedirs(_a, exist_ok=_a )
__SCREAMING_SNAKE_CASE = tarfile.open(_a )
tar_file.extractall(_a, members=TarExtractor.safemembers(_a, _a ) )
tar_file.close()
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\x1F\x8B"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> None:
with gzip.open(_a, "rb" ) as gzip_file:
with open(_a, "wb" ) as extracted_file:
shutil.copyfileobj(_a, _a )
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[
B"""PK\x03\x04""",
B"""PK\x05\x06""", # empty archive
B"""PK\x07\x08""", # spanned archive
]
@classmethod
def __lowerCAmelCase ( cls, _a, _a = 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:
__SCREAMING_SNAKE_CASE = _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:
__SCREAMING_SNAKE_CASE = fp.read(_a ) # CD is where we expect it to be
if len(_a ) == sizeCentralDir:
__SCREAMING_SNAKE_CASE = 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 __lowerCAmelCase ( _a, _a ) -> 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 ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\xFD\x37\x7A\x58\x5A\x00"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> None:
with lzma.open(_a ) as compressed_file:
with open(_a, "wb" ) as extracted_file:
shutil.copyfileobj(_a, _a )
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""] # RAR_ID # RAR5_ID
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(_a, exist_ok=_a )
__SCREAMING_SNAKE_CASE = rarfile.RarFile(_a )
rf.extractall(_a )
rf.close()
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\x28\xb5\x2F\xFD"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
__SCREAMING_SNAKE_CASE = zstd.ZstdDecompressor()
with open(_a, "rb" ) as ifh, open(_a, "wb" ) as ofh:
dctx.copy_stream(_a, _a )
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\x42\x5A\x68"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> None:
with bza.open(_a, "rb" ) as compressed_file:
with open(_a, "wb" ) as extracted_file:
shutil.copyfileobj(_a, _a )
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\x37\x7A\xBC\xAF\x27\x1C"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> 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 ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =[B"""\x04\x22\x4D\x18"""]
@staticmethod
def __lowerCAmelCase ( _a, _a ) -> 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 :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
SCREAMING_SNAKE_CASE__ ={
"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 __lowerCAmelCase ( cls ) -> Union[str, 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 __lowerCAmelCase ( _a, _a ) -> int:
try:
return MagicNumberBaseExtractor.read_magic_number(_a, magic_number_length=_a )
except OSError:
return b""
@classmethod
def __lowerCAmelCase ( cls, _a, _a = 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, )
__SCREAMING_SNAKE_CASE = 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 __lowerCAmelCase ( cls, _a ) -> str: # <Added version="2.4.0"/>
__SCREAMING_SNAKE_CASE = cls._get_magic_number_max_length()
__SCREAMING_SNAKE_CASE = 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 __lowerCAmelCase ( cls, _a, _a, _a = None, _a = "deprecated", ) -> None:
os.makedirs(os.path.dirname(_a ), exist_ok=_a )
# Prevent parallel extractions
__SCREAMING_SNAKE_CASE = 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, )
__SCREAMING_SNAKE_CASE = extractor if extractor != "deprecated" else extractor_format
else:
__SCREAMING_SNAKE_CASE = 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 )
| 214
|
def _A ( __snake_case :list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
__SCREAMING_SNAKE_CASE = sum(__snake_case ) / len(__snake_case ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : Dict = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
"""feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
"""processing_whisper""": ["""WhisperProcessor"""],
"""tokenization_whisper""": ["""WhisperTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Optional[int] = ["""WhisperTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Union[str, Any] = [
"""WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""WhisperForConditionalGeneration""",
"""WhisperModel""",
"""WhisperPreTrainedModel""",
"""WhisperForAudioClassification""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Dict = [
"""TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWhisperForConditionalGeneration""",
"""TFWhisperModel""",
"""TFWhisperPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : str = [
"""FlaxWhisperForConditionalGeneration""",
"""FlaxWhisperModel""",
"""FlaxWhisperPreTrainedModel""",
"""FlaxWhisperForAudioClassification""",
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
snake_case : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 545
|
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = None
__UpperCAmelCase = None
def UpperCAmelCase__( __UpperCAmelCase : TreeNode | None ):
# Validation
def is_valid_tree(__UpperCAmelCase : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__UpperCAmelCase ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
__UpperCAmelCase : TreeNode | None , __UpperCAmelCase : float , __UpperCAmelCase : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __UpperCAmelCase , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __UpperCAmelCase )
)
return is_binary_search_tree_recursive_check(__UpperCAmelCase , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 576
| 0
|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_: int = IFInpaintingPipeline
SCREAMING_SNAKE_CASE_: Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
SCREAMING_SNAKE_CASE_: int = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE_: Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def __lowerCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
return self._get_dummy_components()
def __lowerCamelCase ( self : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any]=0 ) -> List[str]:
"""simple docstring"""
if str(UpperCAmelCase_ ).startswith('mps' ):
_lowerCAmelCase = torch.manual_seed(UpperCAmelCase_ )
else:
_lowerCAmelCase = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
_lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def __lowerCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def __lowerCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __lowerCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __lowerCamelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
self._test_save_load_local()
def __lowerCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 491
|
"""simple docstring"""
import pytest
_snake_case = '''__dummy_dataset1__'''
_snake_case = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def __snake_case ( ):
"""simple docstring"""
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def __snake_case ( ):
"""simple docstring"""
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def __snake_case ( SCREAMING_SNAKE_CASE: List[str] , SCREAMING_SNAKE_CASE: Optional[int] , SCREAMING_SNAKE_CASE: List[str] ):
"""simple docstring"""
_lowerCAmelCase = dataset_loading_script_name
_lowerCAmelCase = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=SCREAMING_SNAKE_CASE )
_lowerCAmelCase = script_dir / f"""{script_name}.py"""
with open(SCREAMING_SNAKE_CASE , 'w' ) as f:
f.write(SCREAMING_SNAKE_CASE )
return str(SCREAMING_SNAKE_CASE )
| 491
| 1
|
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError('only integers accepted as input' )
else:
lowercase_ : Union[str, Any] = str(abs(__SCREAMING_SNAKE_CASE ) )
lowercase_ : str = [list(__SCREAMING_SNAKE_CASE ) for char in range(len(__SCREAMING_SNAKE_CASE ) )]
for index in range(len(__SCREAMING_SNAKE_CASE ) ):
num_transpositions[index].pop(__SCREAMING_SNAKE_CASE )
return max(
int(''.join(list(__SCREAMING_SNAKE_CASE ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("doctest").testmod()
| 425
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE =TypeVar("T")
class UpperCamelCase ( Generic[T] ):
def __init__( self ,__UpperCamelCase ) -> Any:
'''simple docstring'''
lowercase_ : Optional[int] = data
lowercase_ : Node[T] | None = None
def __str__( self ) -> str:
'''simple docstring'''
return f'''{self.data}'''
class UpperCamelCase ( Generic[T] ):
def __init__( self ) -> None:
'''simple docstring'''
lowercase_ : Node[T] | None = None
def __iter__( self ) -> Iterator[T]:
'''simple docstring'''
lowercase_ : Dict = self.top
while node:
yield node.data
lowercase_ : int = node.next
def __str__( self ) -> str:
'''simple docstring'''
return "->".join([str(__UpperCamelCase ) for item in self] )
def __len__( self ) -> int:
'''simple docstring'''
return len(tuple(iter(self ) ) )
def _UpperCAmelCase ( self ) -> bool:
'''simple docstring'''
return self.top is None
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> None:
'''simple docstring'''
lowercase_ : List[str] = Node(__UpperCamelCase )
if not self.is_empty():
lowercase_ : Union[str, Any] = self.top
lowercase_ : int = node
def _UpperCAmelCase ( self ) -> T:
'''simple docstring'''
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top ,__UpperCamelCase )
lowercase_ : Optional[Any] = self.top
lowercase_ : List[str] = self.top.next
return pop_node.data
def _UpperCAmelCase ( self ) -> T:
'''simple docstring'''
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def _UpperCAmelCase ( self ) -> None:
'''simple docstring'''
lowercase_ : Any = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 425
| 1
|
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCAmelCase = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
UpperCAmelCase = F"""https://www.google.com/search?q={query}&num=100"""
UpperCAmelCase = requests.get(
url,
headers={'''User-Agent''': str(UserAgent().random)},
)
try:
UpperCAmelCase = (
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''yuRUbf'''})
.find('''a''')
.get('''href''')
)
except AttributeError:
UpperCAmelCase = parse_qs(
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''kCrYT'''})
.find('''a''')
.get('''href''')
)["""url"""][0]
webbrowser.open(link)
| 720
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class __magic_name__ ( unittest.TestCase ):
__A : int = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__A : List[str] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__A : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__A : Optional[int] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def __snake_case ( self : Tuple , snake_case__ : Any , snake_case__ : Union[str, Any] , snake_case__ : int ):
'''simple docstring'''
lowercase :Optional[int] = ZeroShotClassificationPipeline(
model=snake_case__ , tokenizer=snake_case__ , candidate_labels=['''polics''', '''health'''] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def __snake_case ( self : Any , snake_case__ : Union[str, Any] , snake_case__ : Tuple ):
'''simple docstring'''
lowercase :int = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics''' )
self.assertEqual(snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ )], '''scores''': [ANY(snake_case__ )]} )
# No kwarg
lowercase :Tuple = classifier('''Who are you voting for in 2020?''' , ['''politics'''] )
self.assertEqual(snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ )], '''scores''': [ANY(snake_case__ )]} )
lowercase :Tuple = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics'''] )
self.assertEqual(snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ )], '''scores''': [ANY(snake_case__ )]} )
lowercase :Union[str, Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics, public health''' )
self.assertEqual(
snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ ), ANY(snake_case__ )], '''scores''': [ANY(snake_case__ ), ANY(snake_case__ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase :Optional[Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health'''] )
self.assertEqual(
snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ ), ANY(snake_case__ )], '''scores''': [ANY(snake_case__ ), ANY(snake_case__ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase :Optional[Any] = classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''This text is about {}''' )
self.assertEqual(snake_case__ , {'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ )], '''scores''': [ANY(snake_case__ )]} )
# https://github.com/huggingface/transformers/issues/13846
lowercase :Optional[Any] = classifier(['''I am happy'''] , ['''positive''', '''negative'''] )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ ), ANY(snake_case__ )], '''scores''': [ANY(snake_case__ ), ANY(snake_case__ )]}
for i in range(1 )
] , )
lowercase :Tuple = classifier(['''I am happy''', '''I am sad'''] , ['''positive''', '''negative'''] )
self.assertEqual(
snake_case__ , [
{'''sequence''': ANY(snake_case__ ), '''labels''': [ANY(snake_case__ ), ANY(snake_case__ )], '''scores''': [ANY(snake_case__ ), ANY(snake_case__ )]}
for i in range(2 )
] , )
with self.assertRaises(snake_case__ ):
classifier('''''' , candidate_labels='''politics''' )
with self.assertRaises(snake_case__ ):
classifier(snake_case__ , candidate_labels='''politics''' )
with self.assertRaises(snake_case__ ):
classifier('''Who are you voting for in 2020?''' , candidate_labels='''''' )
with self.assertRaises(snake_case__ ):
classifier('''Who are you voting for in 2020?''' , candidate_labels=snake_case__ )
with self.assertRaises(snake_case__ ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''Not formatting template''' , )
with self.assertRaises(snake_case__ ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template=snake_case__ , )
self.run_entailment_id(snake_case__ )
def __snake_case ( self : Any , snake_case__ : Pipeline ):
'''simple docstring'''
lowercase :List[Any] = zero_shot_classifier.model.config
lowercase :int = config.labelaid
lowercase :str = zero_shot_classifier.entailment_id
lowercase :Dict = {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
lowercase :Optional[Any] = {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase :Tuple = {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase :str = {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
lowercase :Optional[Any] = original_labelaid
self.assertEqual(snake_case__ , zero_shot_classifier.entailment_id )
@require_torch
def __snake_case ( self : List[str] ):
'''simple docstring'''
lowercase :List[Any] = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'''Who are you voting for in 2020?''' * 1_0_0 , candidate_labels=['''politics''', '''public health''', '''science'''] )
@require_torch
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
lowercase :str = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
lowercase :Any = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.3_33, 0.3_33, 0.3_33],
} , )
@require_tf
def __snake_case ( self : List[str] ):
'''simple docstring'''
lowercase :Tuple = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''tf''' , )
lowercase :List[Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.3_33, 0.3_33, 0.3_33],
} , )
@slow
@require_torch
def __snake_case ( self : Optional[int] ):
'''simple docstring'''
lowercase :Dict = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''pt''' )
lowercase :Union[str, Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.9_76, 0.0_15, 0.0_09],
} , )
lowercase :Optional[Any] = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=snake_case__ , )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
@slow
@require_tf
def __snake_case ( self : Any ):
'''simple docstring'''
lowercase :str = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''tf''' )
lowercase :Optional[int] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.9_76, 0.0_15, 0.0_09],
} , )
lowercase :str = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=snake_case__ , )
self.assertEqual(
nested_simplify(snake_case__ ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
| 475
| 0
|
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 __magic_name__ :
_lowerCAmelCase = LEDConfig
_lowerCAmelCase = {}
_lowerCAmelCase = "gelu"
def __init__( self : str , lowerCamelCase__ : str , lowerCamelCase__ : Optional[int]=1_3 , lowerCamelCase__ : List[Any]=7 , lowerCamelCase__ : Tuple=True , lowerCamelCase__ : Optional[Any]=False , lowerCamelCase__ : List[str]=9_9 , lowerCamelCase__ : Any=3_2 , lowerCamelCase__ : List[str]=2 , lowerCamelCase__ : Optional[int]=4 , lowerCamelCase__ : Optional[int]=3_7 , lowerCamelCase__ : Dict=0.1 , lowerCamelCase__ : Any=0.1 , lowerCamelCase__ : Any=2_0 , lowerCamelCase__ : Union[str, Any]=2 , lowerCamelCase__ : Dict=1 , lowerCamelCase__ : List[str]=0 , lowerCamelCase__ : List[str]=4 , ):
lowerCAmelCase : Optional[int] = parent
lowerCAmelCase : Dict = batch_size
lowerCAmelCase : Dict = seq_length
lowerCAmelCase : List[Any] = is_training
lowerCAmelCase : Tuple = use_labels
lowerCAmelCase : str = vocab_size
lowerCAmelCase : List[str] = hidden_size
lowerCAmelCase : Any = num_hidden_layers
lowerCAmelCase : Union[str, Any] = num_attention_heads
lowerCAmelCase : Dict = intermediate_size
lowerCAmelCase : List[str] = hidden_dropout_prob
lowerCAmelCase : List[Any] = attention_probs_dropout_prob
lowerCAmelCase : Union[str, Any] = max_position_embeddings
lowerCAmelCase : List[Any] = eos_token_id
lowerCAmelCase : int = pad_token_id
lowerCAmelCase : Union[str, Any] = bos_token_id
lowerCAmelCase : List[Any] = 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
lowerCAmelCase : Optional[int] = 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
lowerCAmelCase : List[str] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _A ( self : Dict ):
lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowerCAmelCase : str = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowerCAmelCase : List[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
lowerCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase : List[str] = 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 , )
lowerCAmelCase : List[str] = prepare_led_inputs_dict(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase : Tuple = tf.concat(
[tf.zeros_like(lowerCamelCase__ )[:, :-1], tf.ones_like(lowerCamelCase__ )[:, -1:]] , axis=-1 , )
lowerCAmelCase : int = global_attention_mask
return config, inputs_dict
def _A ( self : Optional[int] , lowerCamelCase__ : Any , lowerCamelCase__ : List[Any] ):
lowerCAmelCase : Optional[int] = TFLEDModel(config=lowerCamelCase__ ).get_decoder()
lowerCAmelCase : List[Any] = inputs_dict['''input_ids''']
lowerCAmelCase : Dict = input_ids[:1, :]
lowerCAmelCase : List[Any] = inputs_dict['''attention_mask'''][:1, :]
lowerCAmelCase : Dict = 1
# first forward pass
lowerCAmelCase : Any = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , use_cache=lowerCamelCase__ )
lowerCAmelCase , lowerCAmelCase : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase : int = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase : List[str] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowerCAmelCase : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
lowerCAmelCase : List[str] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowerCAmelCase : int = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )[0]
lowerCAmelCase : Any = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowerCAmelCase : Optional[int] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowerCAmelCase : Any = output_from_no_past[:, -3:, random_slice_idx]
lowerCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase__ , lowerCamelCase__ , rtol=1E-3 )
def UpperCAmelCase__ ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : str , __magic_name__ : str=None , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : List[Any]=None , ):
'''simple docstring'''
if attention_mask is None:
lowerCAmelCase : Union[str, Any] = tf.cast(tf.math.not_equal(__magic_name__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCAmelCase : int = 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:
lowerCAmelCase : int = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCAmelCase : List[str] = 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 __magic_name__ ( snake_case, snake_case, unittest.TestCase ):
_lowerCAmelCase = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_lowerCAmelCase = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_lowerCAmelCase = (
{
"conversational": TFLEDForConditionalGeneration,
"feature-extraction": TFLEDModel,
"summarization": TFLEDForConditionalGeneration,
"text2text-generation": TFLEDForConditionalGeneration,
"translation": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _A ( self : Optional[int] ):
lowerCAmelCase : Tuple = TFLEDModelTester(self )
lowerCAmelCase : str = ConfigTester(self , config_class=lowerCamelCase__ )
def _A ( self : str ):
self.config_tester.run_common_tests()
def _A ( self : List[Any] ):
lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__ )
def _A ( self : List[Any] ):
lowerCAmelCase , lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase : Optional[int] = tf.zeros_like(inputs_dict['''attention_mask'''] )
lowerCAmelCase : List[str] = 2
lowerCAmelCase : int = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
lowerCAmelCase : Tuple = True
lowerCAmelCase : List[str] = self.model_tester.seq_length
lowerCAmelCase : List[Any] = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(lowerCamelCase__ : Optional[int] ):
lowerCAmelCase : int = outputs.decoder_attentions
self.assertEqual(len(lowerCamelCase__ ) , 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(lowerCamelCase__ : Union[str, Any] ):
lowerCAmelCase : List[Any] = [t.numpy() for t in outputs.encoder_attentions]
lowerCAmelCase : List[Any] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(lowerCamelCase__ ) , 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:
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : int = False
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Any = model_class(lowerCamelCase__ )
lowerCAmelCase : Union[str, Any] = model(self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
lowerCAmelCase : Any = len(lowerCamelCase__ )
self.assertEqual(config.output_hidden_states , lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
if self.is_encoder_decoder:
lowerCAmelCase : Optional[int] = model_class(lowerCamelCase__ )
lowerCAmelCase : Union[str, Any] = model(self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertEqual(config.output_hidden_states , lowerCamelCase__ )
check_decoder_attentions_output(lowerCamelCase__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : Dict = model_class(lowerCamelCase__ )
lowerCAmelCase : Dict = model(self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertEqual(config.output_hidden_states , lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
# Check attention is always last and order is fine
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : str = True
lowerCAmelCase : Optional[Any] = model_class(lowerCamelCase__ )
lowerCAmelCase : Dict = model(self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(lowerCamelCase__ ) )
self.assertEqual(model.config.output_hidden_states , lowerCamelCase__ )
check_encoder_attentions_output(lowerCamelCase__ )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def _A ( self : Tuple ):
pass
def _A ( self : Any ):
# TODO: Head-masking not yet implement
pass
def UpperCAmelCase__ ( __magic_name__ : str ):
'''simple docstring'''
return tf.constant(__magic_name__ , dtype=tf.intaa )
__SCREAMING_SNAKE_CASE : str = 1E-4
@slow
@require_tf
class __magic_name__ ( unittest.TestCase ):
def _A ( self : Optional[Any] ):
lowerCAmelCase : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
lowerCAmelCase : Dict = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase : int = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase : Tuple = prepare_led_inputs_dict(model.config , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase : str = model(**lowerCamelCase__ )[0]
lowerCAmelCase : Dict = (1, 1_0_2_4, 7_6_8)
self.assertEqual(output.shape , lowerCamelCase__ )
# change to expected output here
lowerCAmelCase : Tuple = tf.convert_to_tensor(
[[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase__ , atol=1E-3 )
def _A ( self : Optional[int] ):
lowerCAmelCase : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
lowerCAmelCase : List[Any] = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase : List[Any] = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase : List[str] = prepare_led_inputs_dict(model.config , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase : Optional[int] = model(**lowerCamelCase__ )[0]
lowerCAmelCase : Any = (1, 1_0_2_4, model.config.vocab_size)
self.assertEqual(output.shape , lowerCamelCase__ )
# change to expected output here
lowerCAmelCase : str = tf.convert_to_tensor(
[[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase__ , atol=1E-3 , rtol=1E-3 )
| 348
|
from math import log
from scipy.constants import Boltzmann, physical_constants
__SCREAMING_SNAKE_CASE : int = 3_00 # TEMPERATURE (unit = K)
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348
| 1
|
"""simple docstring"""
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
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
super().__init__()
self.register_modules(
vae=lowerCamelCase , text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , safety_checker=lowerCamelCase , feature_extractor=lowerCamelCase , )
def a__ ( self , lowerCamelCase = "auto" ):
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(lowerCamelCase )
def a__ ( self ):
self.enable_attention_slicing(lowerCamelCase )
@torch.no_grad()
def __call__( self , lowerCamelCase , lowerCamelCase = 512 , lowerCamelCase = 512 , lowerCamelCase = 50 , lowerCamelCase = 7.5 , lowerCamelCase = None , lowerCamelCase = 1 , lowerCamelCase = 0.0 , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = "pil" , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = 1 , lowerCamelCase = None , **lowerCamelCase , ):
if isinstance(lowerCamelCase , lowerCamelCase ):
__a = 1
elif isinstance(lowerCamelCase , lowerCamelCase ):
__a = len(lowerCamelCase )
else:
raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase )}" )
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(lowerCamelCase , lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(lowerCamelCase )}." )
# get prompt text embeddings
__a = self.tokenizer(
lowerCamelCase , 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 , lowerCamelCase , 1 )
__a = text_embeddings.view(bs_embed * num_images_per_prompt , lowerCamelCase , -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(lowerCamelCase ) is not type(lowerCamelCase ):
raise TypeError(
F"`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase )} !="
F" {type(lowerCamelCase )}." )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__a = [negative_prompt]
elif batch_size != len(lowerCamelCase ):
raise ValueError(
F"`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase )}, 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(
lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , truncation=lowerCamelCase , 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(lowerCamelCase , lowerCamelCase , 1 )
__a = uncond_embeddings.view(batch_size * num_images_per_prompt , lowerCamelCase , -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(
lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(self.device )
__a = torch.randn(lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(
self.device )
else:
__a = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
__a = torch.randn(lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
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(lowerCamelCase )
# 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(lowerCamelCase ) ):
# 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(lowerCamelCase , lowerCamelCase )
# predict the noise residual
__a = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).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(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__a = 1 / 0.1_8215 * latents
__a = self.vae.decode(lowerCamelCase ).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(lowerCamelCase ) , return_tensors="pt" ).to(
self.device )
__a , __a = self.safety_checker(
images=lowerCamelCase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
__a = None
if output_type == "pil":
__a = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowerCamelCase , nsfw_content_detected=lowerCamelCase )
| 67
|
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 67
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 43
|
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : List[str] = (DDPMScheduler,)
def lowercase_ ( self : List[str] , **UpperCamelCase__ : str)-> str:
'''simple docstring'''
__lowerCAmelCase: List[Any] = {
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**UpperCamelCase__)
return config
def lowercase_ ( self : int)-> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCamelCase__)
def lowercase_ ( self : Tuple)-> str:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=UpperCamelCase__ , beta_end=UpperCamelCase__)
def lowercase_ ( self : str)-> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCamelCase__)
def lowercase_ ( self : Union[str, Any])-> int:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCamelCase__)
def lowercase_ ( self : Tuple)-> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCamelCase__)
def lowercase_ ( self : str)-> str:
'''simple docstring'''
self.check_over_configs(thresholding=UpperCamelCase__)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCamelCase__ , prediction_type=UpperCamelCase__ , sample_max_value=UpperCamelCase__ , )
def lowercase_ ( self : List[str])-> Union[str, Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase__)
def lowercase_ ( self : Optional[Any])-> Union[str, Any]:
'''simple docstring'''
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=UpperCamelCase__)
def lowercase_ ( self : Union[str, Any])-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: Dict = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config()
__lowerCAmelCase: Dict = scheduler_class(**UpperCamelCase__)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.00979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.02)) < 1e-5
def lowercase_ ( self : Optional[int])-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: int = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config()
__lowerCAmelCase: Optional[int] = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: List[str] = len(UpperCamelCase__)
__lowerCAmelCase: List[Any] = self.dummy_model()
__lowerCAmelCase: Tuple = self.dummy_sample_deter
__lowerCAmelCase: Tuple = torch.manual_seed(0)
for t in reversed(range(UpperCamelCase__)):
# 1. predict noise residual
__lowerCAmelCase: List[str] = model(UpperCamelCase__ , UpperCamelCase__)
# 2. predict previous mean of sample x_t-1
__lowerCAmelCase: List[Any] = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
__lowerCAmelCase: List[Any] = pred_prev_sample
__lowerCAmelCase: List[str] = torch.sum(torch.abs(UpperCamelCase__))
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCamelCase__))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def lowercase_ ( self : int)-> Any:
'''simple docstring'''
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: str = self.get_scheduler_config(prediction_type="v_prediction")
__lowerCAmelCase: Dict = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: Dict = len(UpperCamelCase__)
__lowerCAmelCase: Any = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
__lowerCAmelCase: str = torch.manual_seed(0)
for t in reversed(range(UpperCamelCase__)):
# 1. predict noise residual
__lowerCAmelCase: str = model(UpperCamelCase__ , UpperCamelCase__)
# 2. predict previous mean of sample x_t-1
__lowerCAmelCase: Tuple = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
__lowerCAmelCase: Optional[Any] = pred_prev_sample
__lowerCAmelCase: Union[str, Any] = torch.sum(torch.abs(UpperCamelCase__))
__lowerCAmelCase: Dict = torch.mean(torch.abs(UpperCamelCase__))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def lowercase_ ( self : Tuple)-> Any:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.scheduler_classes[0]
__lowerCAmelCase: List[Any] = self.get_scheduler_config()
__lowerCAmelCase: List[Any] = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: Optional[int] = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=UpperCamelCase__)
__lowerCAmelCase: str = scheduler.timesteps
for i, timestep in enumerate(UpperCamelCase__):
if i == len(UpperCamelCase__) - 1:
__lowerCAmelCase: str = -1
else:
__lowerCAmelCase: str = timesteps[i + 1]
__lowerCAmelCase: List[Any] = scheduler.previous_timestep(UpperCamelCase__)
__lowerCAmelCase: List[str] = prev_t.item()
self.assertEqual(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Union[str, Any])-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: int = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(UpperCamelCase__ , msg="`custom_timesteps` must be in descending order."):
scheduler.set_timesteps(timesteps=UpperCamelCase__)
def lowercase_ ( self : Any)-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.scheduler_classes[0]
__lowerCAmelCase: Any = self.get_scheduler_config()
__lowerCAmelCase: Dict = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: List[str] = [1_0_0, 8_7, 5_0, 1, 0]
__lowerCAmelCase: Tuple = len(UpperCamelCase__)
with self.assertRaises(UpperCamelCase__ , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."):
scheduler.set_timesteps(num_inference_steps=UpperCamelCase__ , timesteps=UpperCamelCase__)
def lowercase_ ( self : Optional[Any])-> int:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.scheduler_classes[0]
__lowerCAmelCase: Any = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCamelCase__ , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=UpperCamelCase__)
| 346
| 0
|
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
UpperCamelCase = 5
UpperCamelCase = 10
@require_sentencepiece
@require_tokenizers
class _a ( lowercase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ = SpeechaTextTokenizer
UpperCamelCase__ = False
UpperCamelCase__ = True
def __lowercase ( self) -> str:
'''simple docstring'''
super().setUp()
lowercase__: Optional[int] = sp.SentencePieceProcessor()
spm_model.Load(UpperCAmelCase_)
lowercase__: str = ["<s>", "<pad>", "</s>", "<unk>"]
vocab += [spm_model.IdToPiece(id_) for id_ in range(len(UpperCAmelCase_))]
lowercase__: Tuple = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_))))
lowercase__: Union[str, Any] = Path(self.tmpdirname)
save_json(UpperCAmelCase_ , save_dir / VOCAB_FILES_NAMES["vocab_file"])
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase_ , save_dir / VOCAB_FILES_NAMES["spm_file"])
lowercase__: Optional[int] = SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__: str = "<pad>"
lowercase__: Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_) , UpperCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_) , UpperCAmelCase_)
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
lowercase__: List[str] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<s>")
self.assertEqual(vocab_keys[1] , "<pad>")
self.assertEqual(vocab_keys[-1] , "j")
self.assertEqual(len(UpperCAmelCase_) , 1_001)
def __lowercase ( self) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_001)
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Union[str, Any] = SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
lowercase__: List[Any] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCAmelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_) , [289, 50, 14, 174, 386] , )
lowercase__: Optional[int] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCAmelCase_ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."] , )
lowercase__: Dict = tokenizer.convert_tokens_to_ids(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8])
lowercase__: Dict = tokenizer.convert_ids_to_tokens(UpperCAmelCase_)
self.assertListEqual(
UpperCAmelCase_ , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."] , )
@slow
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Union[str, Any] = {"input_ids": [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ , model_name="facebook/s2t-small-mustc-en-de-st" , revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad" , )
@require_sentencepiece
class _a ( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ = """valhalla/s2t_mustc_multilinguial_medium"""
UpperCamelCase__ = """C'est trop cool"""
UpperCamelCase__ = """Esto es genial"""
@classmethod
def __lowercase ( cls) -> List[str]:
'''simple docstring'''
lowercase__: SpeechaTextTokenizer = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
return cls
def __lowercase ( self) -> int:
'''simple docstring'''
self.assertEqual(self.tokenizer.lang_code_to_id["pt"] , 4)
self.assertEqual(self.tokenizer.lang_code_to_id["ru"] , 6)
self.assertEqual(self.tokenizer.lang_code_to_id["it"] , 9)
self.assertEqual(self.tokenizer.lang_code_to_id["de"] , 11)
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.vocab_size , 10_000)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
self.assertIn(UpperCAmelCase_ , self.tokenizer.all_special_ids)
lowercase__: Optional[int] = [ES_CODE, 4, 1_601, 47, 7_647, 2]
lowercase__: List[Any] = self.tokenizer.decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_)
lowercase__: List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase_)
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase_)
def __lowercase ( self) -> Any:
'''simple docstring'''
lowercase__: List[Any] = "fr"
lowercase__: Tuple = self.tokenizer(self.french_text).input_ids
self.assertEqual(encoded[0] , UpperCAmelCase_)
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
lowercase__: Any = "fr"
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE])
lowercase__: Any = "es"
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE])
| 120
|
"""simple docstring"""
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 _a ( lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = """new-model"""
if is_tf_available():
class _a ( lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = NewModelConfig
@require_tf
class _a ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowercase ( self) -> int:
'''simple docstring'''
lowercase__: Tuple = "bert-base-cased"
lowercase__: List[Any] = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: int = TFAutoModel.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> Dict:
'''simple docstring'''
lowercase__: Optional[int] = "bert-base-cased"
lowercase__: List[str] = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: Optional[int] = TFAutoModelForPreTraining.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> str:
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Dict = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: str = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_)
lowercase__ , lowercase__: List[str] = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: List[str] = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: Dict = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> List[str]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Tuple = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: Optional[int] = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_)
lowercase__ , lowercase__: int = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> List[str]:
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Tuple = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: Optional[int] = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_)
lowercase__ , lowercase__: str = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__: Tuple = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: int = TFAutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__: Tuple = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: List[Any] = TFAutoModelForQuestionAnswering.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
@slow
@require_tensorflow_probability
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
lowercase__: List[Any] = AutoConfig.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: Union[str, Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCAmelCase_)
lowercase__ , lowercase__: Tuple = TFAutoModelForTableQuestionAnswering.from_pretrained(
UpperCAmelCase_ , output_loading_info=UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
lowercase__: List[Any] = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
self.assertEqual(model.num_parameters() , 14_410)
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_) , 14_410)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
lowercase__: List[str] = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
self.assertEqual(model.num_parameters() , 14_410)
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_) , 14_410)
def __lowercase ( self) -> Dict:
'''simple docstring'''
lowercase__: int = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny")
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
lowercase__: List[Any] = copy.deepcopy(model.config)
lowercase__: Optional[Any] = ["FunnelBaseModel"]
lowercase__: Union[str, Any] = TFAutoModel.from_config(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_)
lowercase__: Union[str, Any] = TFAutoModel.from_pretrained(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
def __lowercase ( self) -> Dict:
'''simple docstring'''
try:
AutoConfig.register("new-model" , UpperCAmelCase_)
lowercase__: Any = [
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(UpperCAmelCase_):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_)
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCAmelCase_):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_)
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase__: Tuple = BertModelTester(self).get_config()
lowercase__: List[Any] = NewModelConfig(**tiny_config.to_dict())
lowercase__: Optional[int] = auto_class.from_config(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_)
lowercase__: int = auto_class.from_pretrained(UpperCAmelCase_)
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_)
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 __lowercase ( self) -> Tuple:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , "bert-base is not a local folder and is not a valid model identifier"):
lowercase__: List[str] = TFAutoModel.from_pretrained("bert-base")
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"):
lowercase__: Any = TFAutoModel.from_pretrained(UpperCAmelCase_ , revision="aaaaaa")
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCAmelCase_ , "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin" , ):
lowercase__: Any = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model")
def __lowercase ( self) -> Tuple:
'''simple docstring'''
with self.assertRaisesRegex(UpperCAmelCase_ , "Use `from_pt=True` to load this model"):
lowercase__: List[Any] = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
def __lowercase ( self) -> str:
'''simple docstring'''
lowercase__: Tuple = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with RequestCounter() as counter:
lowercase__: Dict = 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
lowercase__: Union[str, Any] = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
with RequestCounter() as counter:
lowercase__: List[str] = 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)
| 120
| 1
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _A ( _a ):
'''simple docstring'''
__lowerCamelCase : List[str] = 4_2
__lowerCamelCase : List[Any] = 4_2
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase__ ,scheduler=lowercase__ )
@torch.no_grad()
def __call__( self ,SCREAMING_SNAKE_CASE_ = 1 ,SCREAMING_SNAKE_CASE_ = 2000 ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = "pil" ,SCREAMING_SNAKE_CASE_ = True ,**SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
snake_case : Optional[int] = self.unet.config.sample_size
snake_case : Optional[int] = (batch_size, 3, img_size, img_size)
snake_case : Union[str, Any] = self.unet
snake_case : Dict = randn_tensor(lowercase__ ,generator=lowercase__ ) * self.scheduler.init_noise_sigma
snake_case : Any = sample.to(self.device )
self.scheduler.set_timesteps(lowercase__ )
self.scheduler.set_sigmas(lowercase__ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
snake_case : Optional[Any] = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
snake_case : Any = self.unet(lowercase__ ,lowercase__ ).sample
snake_case : Optional[int] = self.scheduler.step_correct(lowercase__ ,lowercase__ ,generator=lowercase__ ).prev_sample
# prediction step
snake_case : str = model(lowercase__ ,lowercase__ ).sample
snake_case : int = self.scheduler.step_pred(lowercase__ ,lowercase__ ,lowercase__ ,generator=lowercase__ )
snake_case , snake_case : Tuple = output.prev_sample, output.prev_sample_mean
snake_case : int = sample_mean.clamp(0 ,1 )
snake_case : Dict = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
snake_case : List[Any] = self.numpy_to_pil(lowercase__ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=lowercase__ )
| 36
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 6_50, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "pytorch",
"script": "run_ddp.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 6_00, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "tensorflow",
"script": "run_tf_dist.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 6_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
},
] )
class A_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ (self ) -> Any:
if self.framework == "pytorch":
subprocess.run(
F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding='''utf-8''' , check=lowercase__ , )
assert hasattr(self , '''env''' )
def lowerCAmelCase_ (self , lowercase__ ) -> Optional[int]:
__UpperCAmelCase = F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
# distributed data settings
__UpperCAmelCase = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=lowercase__ , instance_count=lowercase__ , instance_type=self.instance_type , debugger_hook_config=lowercase__ , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=lowercase__ , py_version='''py36''' , )
def lowerCAmelCase_ (self , lowercase__ ) -> str:
TrainingJobAnalytics(lowercase__ ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )
@parameterized.expand([(2,)] )
def lowerCAmelCase_ (self , lowercase__ ) -> Tuple:
# create estimator
__UpperCAmelCase = self.create_estimator(lowercase__ )
# run training
estimator.fit()
# result dataframe
__UpperCAmelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__UpperCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
__UpperCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__UpperCAmelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'''{estimator.latest_training_job.name}.json''' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , lowercase__ )
| 303
| 0
|
import functools
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(lowercase__ )
@functools.cache
def min_distance(lowercase__ : int , lowercase__ : int ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
SCREAMING_SNAKE_CASE__ : int = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , lowercase__ ) , 1 + min_distance(lowercase__ , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
# 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.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636
| 0
|
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : Optional[int] = [1]
for i in range(2 , _UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
snake_case_ : int = []
snake_case_ : Dict = list(range(_UpperCamelCase ) )
# Find permutation
while factorials:
snake_case_ : int = factorials.pop()
snake_case_ , snake_case_ : Tuple = divmod(_UpperCamelCase , _UpperCamelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60
|
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=4 , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_attention_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_choices
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_attention_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = FlaxAlbertModelTester(self )
@slow
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCAmelCase = model_class_name.from_pretrained('''albert-base-v2''' )
UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_snake_case )
@require_flax
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
UpperCAmelCase = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
UpperCAmelCase = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase = model(_snake_case , attention_mask=_snake_case )[0]
UpperCAmelCase = (1, 11, 768)
self.assertEqual(output.shape , _snake_case )
UpperCAmelCase = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _snake_case , atol=1e-4 ) )
| 254
| 0
|
"""simple docstring"""
import numpy as np
import datasets
UpperCAmelCase : Dict = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"
UpperCAmelCase : Dict = "\\n@article{de2000mahalanobis,\n title={The mahalanobis distance},\n author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n journal={Chemometrics and intelligent laboratory systems},\n volume={50},\n number={1},\n pages={1--18},\n year={2000},\n publisher={Elsevier}\n}\n"
UpperCAmelCase : Any = "\nArgs:\n X: List of datapoints to be compared with the `reference_distribution`.\n reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n >>> print(results)\n {'mahalanobis': array([0.5])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""X""": datasets.Sequence(datasets.Value("""float""" , id="""sequence""") , id="""X"""),
}) , )
def _UpperCAmelCase ( self : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str):
"""simple docstring"""
lowercase_ = np.array(lowerCAmelCase_)
lowercase_ = np.array(lowerCAmelCase_)
# Assert that arrays are 2D
if len(X.shape) != 2:
raise ValueError("""Expected `X` to be a 2D vector""")
if len(reference_distribution.shape) != 2:
raise ValueError("""Expected `reference_distribution` to be a 2D vector""")
if reference_distribution.shape[0] < 2:
raise ValueError(
"""Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension""")
# Get mahalanobis distance for each prediction
lowercase_ = X - np.mean(lowerCAmelCase_)
lowercase_ = np.cov(reference_distribution.T)
try:
lowercase_ = np.linalg.inv(lowerCAmelCase_)
except np.linalg.LinAlgError:
lowercase_ = np.linalg.pinv(lowerCAmelCase_)
lowercase_ = np.dot(lowerCAmelCase_ , lowerCAmelCase_)
lowercase_ = np.dot(lowerCAmelCase_ , X_minus_mu.T).diagonal()
return {"mahalanobis": mahal_dist}
| 100
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
while a != 0:
lowercase_ , lowercase_ = b % a, a
return b
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
if gcd(__lowerCAmelCase , __lowerCAmelCase ) != 1:
lowercase_ = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(__lowerCAmelCase )
lowercase_ , lowercase_ , lowercase_ = 1, 0, a
lowercase_ , lowercase_ , lowercase_ = 0, 1, m
while va != 0:
lowercase_ = ua // va
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 100
| 1
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] , _A : List[str] , _A : Union[str, Any]=3 , _A : Optional[int]=3_2 , _A : int=3 , _A : Optional[int]=1_0 , _A : str=[1_0, 2_0, 3_0, 4_0] , _A : int=[1, 1, 2, 1] , _A : int=True , _A : Optional[Any]=True , _A : Optional[Any]="relu" , _A : str=3 , _A : str=None , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = parent
_SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
_SCREAMING_SNAKE_CASE : List[str] = image_size
_SCREAMING_SNAKE_CASE : str = num_channels
_SCREAMING_SNAKE_CASE : Any = embeddings_size
_SCREAMING_SNAKE_CASE : int = hidden_sizes
_SCREAMING_SNAKE_CASE : Optional[Any] = depths
_SCREAMING_SNAKE_CASE : Optional[Any] = is_training
_SCREAMING_SNAKE_CASE : List[str] = use_labels
_SCREAMING_SNAKE_CASE : List[Any] = hidden_act
_SCREAMING_SNAKE_CASE : Dict = num_labels
_SCREAMING_SNAKE_CASE : Tuple = scope
_SCREAMING_SNAKE_CASE : Dict = len(_A)
def _lowerCAmelCase ( self : Tuple):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_SCREAMING_SNAKE_CASE : int = None
if self.use_labels:
_SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size] , self.num_labels)
_SCREAMING_SNAKE_CASE : Tuple = self.get_config()
return config, pixel_values, labels
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _lowerCAmelCase ( self : Optional[Any] , _A : Union[str, Any] , _A : Optional[Any] , _A : Optional[Any]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = TFResNetModel(config=_A)
_SCREAMING_SNAKE_CASE : List[Any] = model(_A)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _lowerCAmelCase ( self : Union[str, Any] , _A : List[str] , _A : Optional[Any] , _A : int):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple = self.num_labels
_SCREAMING_SNAKE_CASE : List[str] = TFResNetForImageClassification(_A)
_SCREAMING_SNAKE_CASE : Dict = model(_A , labels=_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowerCAmelCase ( self : List[Any]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = config_and_inputs
_SCREAMING_SNAKE_CASE : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _snake_case ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
a = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a = False
a = False
a = False
a = False
a = False
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = TFResNetModelTester(self)
_SCREAMING_SNAKE_CASE : int = ConfigTester(self , config_class=_A , has_text_modality=_A)
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowerCAmelCase ( self : Optional[int]):
"""simple docstring"""
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""")
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""")
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
pass
def _lowerCAmelCase ( self : Tuple):
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Any = model_class(_A)
_SCREAMING_SNAKE_CASE : Any = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE : int = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _A)
def _lowerCAmelCase ( self : List[Any]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A)
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
def check_hidden_states_output(_A : int , _A : Optional[int] , _A : Dict):
_SCREAMING_SNAKE_CASE : Tuple = model_class(_A)
_SCREAMING_SNAKE_CASE : Optional[int] = model(**self._prepare_for_class(_A , _A))
_SCREAMING_SNAKE_CASE : List[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_A) , expected_num_stages + 1)
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE : List[str] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_SCREAMING_SNAKE_CASE : Dict = layer_type
_SCREAMING_SNAKE_CASE : List[Any] = True
check_hidden_states_output(_A , _A , _A)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE : Optional[Any] = True
check_hidden_states_output(_A , _A , _A)
def _lowerCAmelCase ( self : Optional[int]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A)
@slow
def _lowerCAmelCase ( self : int):
"""simple docstring"""
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = TFResNetModel.from_pretrained(_A)
self.assertIsNotNone(_A)
def lowerCamelCase_()-> Optional[Any]:
_SCREAMING_SNAKE_CASE : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : List[Any]):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def _lowerCAmelCase ( self : Optional[int]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Dict = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
_SCREAMING_SNAKE_CASE : List[str] = self.default_image_processor
_SCREAMING_SNAKE_CASE : Optional[Any] = prepare_img()
_SCREAMING_SNAKE_CASE : List[str] = image_processor(images=_A , return_tensors="""tf""")
# forward pass
_SCREAMING_SNAKE_CASE : int = model(**_A)
# verify the logits
_SCREAMING_SNAKE_CASE : Dict = tf.TensorShape((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , _A)
_SCREAMING_SNAKE_CASE : Union[str, Any] = tf.constant([-11.1_069, -9.7_877, -8.3_777])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _A , atol=1e-4))
| 338
|
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). " , __snake_case , )
class _snake_case ( __snake_case ):
"""simple docstring"""
a = RobertaConfig
a = "roberta"
def __init__( self : Optional[Any] , _A : Union[str, Any]):
"""simple docstring"""
super().__init__(_A)
_SCREAMING_SNAKE_CASE : Any = RobertaEmbeddings(_A)
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. " , __snake_case , )
class _snake_case ( __snake_case ):
"""simple docstring"""
a = RobertaConfig
a = "roberta"
def __init__( self : Dict , _A : List[str]):
"""simple docstring"""
super().__init__(_A)
_SCREAMING_SNAKE_CASE : List[Any] = config.num_labels
_SCREAMING_SNAKE_CASE : int = config.num_hidden_layers
_SCREAMING_SNAKE_CASE : List[Any] = DeeRobertaModel(_A)
_SCREAMING_SNAKE_CASE : List[str] = nn.Dropout(config.hidden_dropout_prob)
_SCREAMING_SNAKE_CASE : int = nn.Linear(config.hidden_size , self.config.num_labels)
@add_start_docstrings_to_model_forward(_A)
def _lowerCAmelCase ( self : List[Any] , _A : Dict=None , _A : Union[str, Any]=None , _A : List[Any]=None , _A : str=None , _A : Optional[Any]=None , _A : Any=None , _A : Tuple=None , _A : Union[str, Any]=-1 , _A : List[Any]=False , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = self.num_layers
try:
_SCREAMING_SNAKE_CASE : List[Any] = self.roberta(
_A , attention_mask=_A , token_type_ids=_A , position_ids=_A , head_mask=_A , inputs_embeds=_A , )
_SCREAMING_SNAKE_CASE : List[Any] = outputs[1]
_SCREAMING_SNAKE_CASE : Optional[Any] = self.dropout(_A)
_SCREAMING_SNAKE_CASE : List[str] = self.classifier(_A)
_SCREAMING_SNAKE_CASE : Tuple = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_SCREAMING_SNAKE_CASE : Dict = e.message
_SCREAMING_SNAKE_CASE : int = e.exit_layer
_SCREAMING_SNAKE_CASE : str = outputs[0]
if not self.training:
_SCREAMING_SNAKE_CASE : Dict = entropy(_A)
_SCREAMING_SNAKE_CASE : List[str] = []
_SCREAMING_SNAKE_CASE : int = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_SCREAMING_SNAKE_CASE : List[str] = MSELoss()
_SCREAMING_SNAKE_CASE : Any = loss_fct(logits.view(-1) , labels.view(-1))
else:
_SCREAMING_SNAKE_CASE : Optional[int] = CrossEntropyLoss()
_SCREAMING_SNAKE_CASE : str = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
# work with highway exits
_SCREAMING_SNAKE_CASE : List[str] = []
for highway_exit in outputs[-1]:
_SCREAMING_SNAKE_CASE : List[Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(_A)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_SCREAMING_SNAKE_CASE : str = MSELoss()
_SCREAMING_SNAKE_CASE : Optional[Any] = loss_fct(highway_logits.view(-1) , labels.view(-1))
else:
_SCREAMING_SNAKE_CASE : Tuple = CrossEntropyLoss()
_SCREAMING_SNAKE_CASE : Union[str, Any] = loss_fct(highway_logits.view(-1 , self.num_labels) , labels.view(-1))
highway_losses.append(_A)
if train_highway:
_SCREAMING_SNAKE_CASE : List[str] = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_SCREAMING_SNAKE_CASE : Any = (loss,) + outputs
if not self.training:
_SCREAMING_SNAKE_CASE : List[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_SCREAMING_SNAKE_CASE : Tuple = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 338
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class __lowerCAmelCase ( lowerCAmelCase):
_a = '''timesformer'''
def __init__( self: List[str] , _lowerCAmelCase: List[Any]=2_24 , _lowerCAmelCase: Optional[int]=16 , _lowerCAmelCase: Tuple=3 , _lowerCAmelCase: Optional[int]=8 , _lowerCAmelCase: str=7_68 , _lowerCAmelCase: Union[str, Any]=12 , _lowerCAmelCase: Dict=12 , _lowerCAmelCase: Optional[int]=30_72 , _lowerCAmelCase: Optional[Any]="gelu" , _lowerCAmelCase: int=0.0 , _lowerCAmelCase: Optional[Any]=0.0 , _lowerCAmelCase: Dict=0.02 , _lowerCAmelCase: Optional[int]=1e-6 , _lowerCAmelCase: Optional[int]=True , _lowerCAmelCase: Any="divided_space_time" , _lowerCAmelCase: Optional[int]=0 , **_lowerCAmelCase: List[str] , ):
super().__init__(**_lowerCAmelCase )
lowercase :List[Any] = image_size
lowercase :Optional[int] = patch_size
lowercase :Dict = num_channels
lowercase :str = num_frames
lowercase :List[Any] = hidden_size
lowercase :Optional[int] = num_hidden_layers
lowercase :Dict = num_attention_heads
lowercase :int = intermediate_size
lowercase :str = hidden_act
lowercase :Dict = hidden_dropout_prob
lowercase :Any = attention_probs_dropout_prob
lowercase :str = initializer_range
lowercase :Optional[Any] = layer_norm_eps
lowercase :Optional[int] = qkv_bias
lowercase :int = attention_type
lowercase :Union[str, Any] = drop_path_rate
| 453
|
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 UpperCAmelCase__ ( ):
lowercase :List[str] = torch.nn.Linear(2, 4 )
lowercase :List[Any] = torch.optim.AdamW(model.parameters(), lr=1.0 )
lowercase :int = torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase, max_lr=0.01, steps_per_epoch=2, epochs=1 )
lowercase :Optional[int] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
lowercase :List[str] = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def UpperCAmelCase__ ( lowerCamelCase ):
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :Dict = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(lowerCamelCase )
class __lowerCAmelCase ( lowerCAmelCase):
@require_cuda
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :List[str] = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(_lowerCAmelCase ):
lowercase :Any = Accelerator(cpu=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Any = Accelerator()
lowercase :Dict = GradientState()
assert state.num_steps == 1
lowercase :Tuple = 4
assert state.num_steps == 4
assert state.sync_gradients is True
lowercase :List[Any] = False
assert state.sync_gradients is False
GradientState._reset_state()
def SCREAMING_SNAKE_CASE ( self: int ):
lowercase :str = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :Any = create_components()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) :Tuple = accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
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 SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :Dict = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :List[str] = create_components()
accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
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 SCREAMING_SNAKE_CASE ( self: int ):
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*_lowerCAmelCase: List[str] , **_lowerCAmelCase: Optional[int] ):
pass
with patch("torch.cuda.set_device" , _lowerCAmelCase ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64" ):
lowercase :List[Any] = Accelerator()
self.assertEqual(str(accelerator.state.device ) , "cuda:64" )
def SCREAMING_SNAKE_CASE ( self: int ):
lowercase :Tuple = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :Optional[Any] = create_components()
accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase :Tuple = get_signature(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(_lowerCAmelCase )
# make sure random weights don't match
load_random_weights(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) > 1e-3 )
# make sure loaded weights match
accelerator.load_state(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) < 1e-3 )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
lowercase :Dict = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :int = create_components()
accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase :int = get_signature(_lowerCAmelCase )
# saving hook
def save_config(_lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: str , _lowerCAmelCase: List[Any] ):
lowercase :Dict = {"class_name": models[0].__class__.__name__}
with open(os.path.join(_lowerCAmelCase , "data.json" ) , "w" ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
# loading hook
def load_config(_lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: Union[str, Any] ):
with open(os.path.join(_lowerCAmelCase , "data.json" ) , "r" ) as f:
lowercase :int = json.load(_lowerCAmelCase )
lowercase :Optional[int] = config["class_name"]
lowercase :Optional[Any] = accelerator.register_save_state_pre_hook(_lowerCAmelCase )
lowercase :Tuple = accelerator.register_load_state_pre_hook(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(_lowerCAmelCase )
# make sure random weights don't match with hooks
load_random_weights(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) > 1e-3 )
# random class name to verify correct one is loaded
lowercase :Optional[int] = "random"
# make sure loaded weights match with hooks
accelerator.load_state(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) < 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(_lowerCAmelCase )
# make sure random weights don't match with hooks removed
load_random_weights(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) > 1e-3 )
# random class name to verify correct one is loaded
lowercase :str = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(_lowerCAmelCase )
self.assertTrue(abs(model_signature - get_signature(_lowerCAmelCase ) ) < 1e-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
lowercase :List[str] = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :List[Any] = create_components()
lowercase :List[str] = None
# This should work
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase :str = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.assertTrue(dummy_obj is None )
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Union[str, Any] = Accelerator()
lowercase , lowercase , lowercase , lowercase , lowercase :Union[str, Any] = create_components()
lowercase :Union[str, Any] = [1, 2, 3]
# This should work
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase :List[str] = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Dummy object should have `_is_accelerate_prepared` set to `True`" , )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Model is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Optimizer is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Scheduler is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(_lowerCAmelCase , "_is_accelerate_prepared" , _lowerCAmelCase ) , _lowerCAmelCase , "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
from transformers import AutoModelForCausalLM
lowercase :Any = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=_lowerCAmelCase , device_map={"": 0} , )
lowercase :int = Accelerator()
# This should work
lowercase :Optional[int] = accelerator.prepare(_lowerCAmelCase )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE ( self: List[str] ):
from transformers import AutoModelForCausalLM
lowercase :Optional[Any] = Accelerator()
with init_empty_weights():
lowercase :Any = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
lowercase :Dict = infer_auto_device_map(_lowerCAmelCase )
lowercase :int = "cpu"
lowercase :str = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , device_map=_lowerCAmelCase , load_in_abit=_lowerCAmelCase , llm_inta_enable_fpaa_cpu_offload=_lowerCAmelCase )
# This should not work and get value error
with self.assertRaises(_lowerCAmelCase ):
lowercase :Dict = accelerator.prepare(_lowerCAmelCase )
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self: Any ):
from transformers import AutoModelForCausalLM
lowercase :List[str] = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
lowercase :str = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
lowercase :Optional[Any] = infer_auto_device_map(_lowerCAmelCase )
lowercase :Any = 1
lowercase :str = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=_lowerCAmelCase , device_map=_lowerCAmelCase , )
lowercase :List[str] = Accelerator()
# This should not work and get value error
with self.assertRaises(_lowerCAmelCase ):
lowercase :Optional[Any] = accelerator.prepare(_lowerCAmelCase )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
from transformers import AutoModelForCausalLM
with init_empty_weights():
lowercase :List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
lowercase :List[Any] = infer_auto_device_map(_lowerCAmelCase )
lowercase :Optional[int] = 1
lowercase :Union[str, Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=_lowerCAmelCase , device_map=_lowerCAmelCase , )
lowercase :Union[str, Any] = Accelerator()
# This should work
lowercase :List[Any] = accelerator.prepare(_lowerCAmelCase )
@require_cuda
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :Optional[int] = torch.nn.Linear(10 , 10 )
lowercase :Optional[int] = torch.optim.SGD(model.parameters() , lr=0.01 )
lowercase :List[Any] = Accelerator(cpu=_lowerCAmelCase )
lowercase :List[str] = accelerator.prepare(_lowerCAmelCase )
| 453
| 1
|
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = ["image_processor", "tokenizer"]
lowerCamelCase_ = "OwlViTImageProcessor"
lowerCamelCase_ = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self :Optional[Any] , __A :int=None , __A :Optional[int]=None , **__A :str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 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 , )
SCREAMING_SNAKE_CASE__ = kwargs.pop("""feature_extractor""" )
SCREAMING_SNAKE_CASE__ = 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 )
def __call__( self :str , __A :Dict=None , __A :List[str]=None , __A :str=None , __A :Optional[int]="max_length" , __A :Tuple="np" , **__A :int ) -> Tuple:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
"""You have to specify at least one text or query image or image. All three cannot be none.""" )
if text is not None:
if isinstance(__A , __A ) or (isinstance(__A , __A ) and not isinstance(text[0] , __A )):
SCREAMING_SNAKE_CASE__ = [self.tokenizer(__A , padding=__A , return_tensors=__A , **__A )]
elif isinstance(__A , __A ) and isinstance(text[0] , __A ):
SCREAMING_SNAKE_CASE__ = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ = max([len(__A ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(__A ) != max_num_queries:
SCREAMING_SNAKE_CASE__ = t + [""" """] * (max_num_queries - len(__A ))
SCREAMING_SNAKE_CASE__ = self.tokenizer(__A , padding=__A , return_tensors=__A , **__A )
encodings.append(__A )
else:
raise TypeError("""Input text should be a string, a list of strings or a nested list of strings""" )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ = np.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ = np.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ = jnp.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ = jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ = torch.cat([encoding["""input_ids"""] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ = torch.cat([encoding["""attention_mask"""] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ = tf.stack([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ = tf.stack([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
else:
raise ValueError("""Target return tensor type could not be returned""" )
SCREAMING_SNAKE_CASE__ = BatchEncoding()
SCREAMING_SNAKE_CASE__ = input_ids
SCREAMING_SNAKE_CASE__ = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ = BatchEncoding()
SCREAMING_SNAKE_CASE__ = self.image_processor(
__A , return_tensors=__A , **__A ).pixel_values
SCREAMING_SNAKE_CASE__ = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ = self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def _snake_case ( self :List[Any] , *__A :Dict , **__A :Dict ) -> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process(*__A , **__A )
def _snake_case ( self :Optional[int] , *__A :Dict , **__A :List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*__A , **__A )
def _snake_case ( self :str , *__A :List[str] , **__A :Union[str, Any] ) -> Any:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*__A , **__A )
def _snake_case ( self :Dict , *__A :List[str] , **__A :List[str] ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*__A , **__A )
def _snake_case ( self :Dict , *__A :Dict , **__A :List[str] ) -> str:
"""simple docstring"""
return self.tokenizer.decode(*__A , **__A )
@property
def _snake_case ( self :List[Any] ) -> Optional[int]:
"""simple docstring"""
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 _snake_case ( self :Any ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __A , )
return self.image_processor
| 6
|
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Dict = sorted(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , key=lambda SCREAMING_SNAKE_CASE : x[0] / x[1] , reverse=SCREAMING_SNAKE_CASE )
UpperCamelCase , UpperCamelCase : int = [i[0] for i in r], [i[1] for i in r]
UpperCamelCase : Optional[Any] = list(accumulate(SCREAMING_SNAKE_CASE ) )
UpperCamelCase : Optional[Any] = bisect(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102
| 0
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.get_logger(__name__)
UpperCAmelCase__ : int = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = '''table-transformer'''
__UpperCAmelCase = ['''past_key_values''']
__UpperCAmelCase = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , lowercase_=True , lowercase_=None , lowercase_=3 , lowercase_=1_0_0 , lowercase_=6 , lowercase_=2_0_4_8 , lowercase_=8 , lowercase_=6 , lowercase_=2_0_4_8 , lowercase_=8 , lowercase_=0.0 , lowercase_=0.0 , lowercase_=True , lowercase_="relu" , lowercase_=2_5_6 , lowercase_=0.1 , lowercase_=0.0 , lowercase_=0.0 , lowercase_=0.02 , lowercase_=1.0 , lowercase_=False , lowercase_="sine" , lowercase_="resnet50" , lowercase_=True , lowercase_=False , lowercase_=1 , lowercase_=5 , lowercase_=2 , lowercase_=1 , lowercase_=1 , lowercase_=5 , lowercase_=2 , lowercase_=0.1 , **lowercase_ , ) -> Optional[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
__snake_case = CONFIG_MAPPING['resnet'](out_features=['stage4'])
elif isinstance(lowercase_ , lowercase_):
__snake_case = backbone_config.get('model_type')
__snake_case = CONFIG_MAPPING[backbone_model_type]
__snake_case = config_class.from_dict(lowercase_)
# set timm attributes to None
__snake_case , __snake_case , __snake_case = None, None, None
__snake_case = use_timm_backbone
__snake_case = backbone_config
__snake_case = num_channels
__snake_case = num_queries
__snake_case = d_model
__snake_case = encoder_ffn_dim
__snake_case = encoder_layers
__snake_case = encoder_attention_heads
__snake_case = decoder_ffn_dim
__snake_case = decoder_layers
__snake_case = decoder_attention_heads
__snake_case = dropout
__snake_case = attention_dropout
__snake_case = activation_dropout
__snake_case = activation_function
__snake_case = init_std
__snake_case = init_xavier_std
__snake_case = encoder_layerdrop
__snake_case = decoder_layerdrop
__snake_case = encoder_layers
__snake_case = auxiliary_loss
__snake_case = position_embedding_type
__snake_case = backbone
__snake_case = use_pretrained_backbone
__snake_case = dilation
# Hungarian matcher
__snake_case = class_cost
__snake_case = bbox_cost
__snake_case = giou_cost
# Loss coefficients
__snake_case = mask_loss_coefficient
__snake_case = dice_loss_coefficient
__snake_case = bbox_loss_coefficient
__snake_case = giou_loss_coefficient
__snake_case = eos_coefficient
super().__init__(is_encoder_decoder=lowercase_ , **lowercase_)
@property
def _a ( self) -> int:
return self.encoder_attention_heads
@property
def _a ( self) -> int:
return self.d_model
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = version.parse('''1.11''' )
@property
def _a ( self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
])
@property
def _a ( self) -> float:
return 1e-5
@property
def _a ( self) -> int:
return 1_2
| 676
|
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.parametrize('revision' , [None, 'v2'] )
def A ( snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Any ) -> Optional[int]:
'''simple docstring'''
__snake_case = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__ )
assert url == f"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(snake_case__ )}"
| 676
| 1
|
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self : Dict , A_ : int , A_ : Dict ):
'''simple docstring'''
__lowercase = name
__lowercase = val
def __str__( self : Tuple ):
'''simple docstring'''
return F'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self : Optional[Any] , A_ : List[str] ):
'''simple docstring'''
return self.val < other.val
class lowerCamelCase__ :
def __init__( self : str , A_ : Optional[int] ):
'''simple docstring'''
__lowercase = {}
__lowercase = {}
__lowercase = self.build_heap(A_ )
def __getitem__( self : Union[str, Any] , A_ : Tuple ):
'''simple docstring'''
return self.get_value(A_ )
def SCREAMING_SNAKE_CASE_ ( self : str , A_ : Optional[Any] ):
'''simple docstring'''
return (idx - 1) // 2
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : List[Any] ):
'''simple docstring'''
return idx * 2 + 1
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , A_ : Optional[Any] ):
'''simple docstring'''
return idx * 2 + 2
def SCREAMING_SNAKE_CASE_ ( self : str , A_ : Any ):
'''simple docstring'''
return self.heap_dict[key]
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , A_ : Union[str, Any] ):
'''simple docstring'''
__lowercase = len(A_ ) - 1
__lowercase = self.get_parent_idx(A_ )
for idx, i in enumerate(A_ ):
__lowercase = idx
__lowercase = i.val
for i in range(A_ , -1 , -1 ):
self.sift_down(A_ , A_ )
return array
def SCREAMING_SNAKE_CASE_ ( self : Any , A_ : List[str] , A_ : Tuple ):
'''simple docstring'''
while True:
__lowercase = self.get_left_child_idx(A_ ) # noqa: E741
__lowercase = self.get_right_child_idx(A_ )
__lowercase = idx
if l < len(A_ ) and array[l] < array[idx]:
__lowercase = l
if r < len(A_ ) and array[r] < array[smallest]:
__lowercase = r
if smallest != idx:
__lowercase , __lowercase = array[smallest], array[idx]
(
(
__lowercase
) , (
__lowercase
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
__lowercase = smallest
else:
break
def SCREAMING_SNAKE_CASE_ ( self : Any , A_ : str ):
'''simple docstring'''
__lowercase = self.get_parent_idx(A_ )
while p >= 0 and self.heap[p] > self.heap[idx]:
__lowercase , __lowercase = self.heap[idx], self.heap[p]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
__lowercase = p
__lowercase = self.get_parent_idx(A_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
return self.heap[0]
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
__lowercase , __lowercase = self.heap[-1], self.heap[0]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
__lowercase = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def SCREAMING_SNAKE_CASE_ ( self : Any , A_ : Tuple ):
'''simple docstring'''
self.heap.append(A_ )
__lowercase = len(self.heap ) - 1
__lowercase = node.val
self.sift_up(len(self.heap ) - 1 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
return len(self.heap ) == 0
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , A_ : Any , A_ : Optional[Any] ):
'''simple docstring'''
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
__lowercase = new_value
__lowercase = new_value
self.sift_up(self.idx_of_element[node] )
UpperCAmelCase__ =Node("R", -1)
UpperCAmelCase__ =Node("B", 6)
UpperCAmelCase__ =Node("A", 3)
UpperCAmelCase__ =Node("X", 1)
UpperCAmelCase__ =Node("E", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
UpperCAmelCase__ =MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
print(i)
print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 616
|
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ =logging.get_logger(__name__)
UpperCAmelCase__ ="▁"
UpperCAmelCase__ ={
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
UpperCAmelCase__ ={
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
UpperCAmelCase__ ={
"facebook/m2m100_418M": 1024,
}
# fmt: off
UpperCAmelCase__ ={
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class lowerCamelCase__ ( _a ):
a : str = VOCAB_FILES_NAMES
a : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : int = PRETRAINED_VOCAB_FILES_MAP
a : Union[str, Any] = ["""input_ids""", """attention_mask"""]
a : List[int] = []
a : List[int] = []
def __init__( self : Optional[Any] , A_ : str , A_ : Dict , A_ : str=None , A_ : Dict=None , A_ : str="<s>" , A_ : Any="</s>" , A_ : List[Any]="</s>" , A_ : List[str]="<pad>" , A_ : Optional[int]="<unk>" , A_ : str="m2m100" , A_ : Optional[Dict[str, Any]] = None , A_ : Tuple=8 , **A_ : Dict , ):
'''simple docstring'''
__lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
__lowercase = language_codes
__lowercase = FAIRSEQ_LANGUAGE_CODES[language_codes]
__lowercase = {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}
__lowercase = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A_ )
for lang_code in fairseq_language_code
if self.get_lang_token(A_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A_ , tgt_lang=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , unk_token=A_ , pad_token=A_ , language_codes=A_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A_ , **A_ , )
__lowercase = vocab_file
__lowercase = load_json(A_ )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = spm_file
__lowercase = load_spm(A_ , self.sp_model_kwargs )
__lowercase = len(self.encoder )
__lowercase = {
self.get_lang_token(A_ ): self.encoder_size + i for i, lang_code in enumerate(A_ )
}
__lowercase = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_ )}
__lowercase = {v: k for k, v in self.lang_token_to_id.items()}
__lowercase = src_lang if src_lang is not None else """en"""
__lowercase = tgt_lang
__lowercase = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
__lowercase = num_madeup_words
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
__lowercase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
return self.sp_model.encode(A_ , out_type=A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , A_ : str ):
'''simple docstring'''
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A_ , self.encoder[self.unk_token] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , A_ : int ):
'''simple docstring'''
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A_ , self.unk_token )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , A_ : Union[str, Any] ):
'''simple docstring'''
__lowercase = []
__lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A_ ) + token
__lowercase = []
else:
current_sub_tokens.append(A_ )
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self : List[str] , A_ : List[int] , A_ : Optional[List[int]] = None , A_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
__lowercase = [1] * len(self.prefix_tokens )
__lowercase = [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 SCREAMING_SNAKE_CASE_ ( self : Any , A_ : List[int] , A_ : Optional[List[int]] = None ):
'''simple docstring'''
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 SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
__lowercase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
'''simple docstring'''
__lowercase = self.__dict__.copy()
__lowercase = None
return state
def __setstate__( self : Optional[Any] , A_ : Dict ):
'''simple docstring'''
__lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__lowercase = {}
__lowercase = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , A_ : str , A_ : Optional[str] = None ):
'''simple docstring'''
__lowercase = Path(A_ )
if not save_dir.is_dir():
raise OSError(F'''{save_directory} should be a directory''' )
__lowercase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
__lowercase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , A_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(A_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A_ )
elif not os.path.isfile(self.spm_file ):
with open(A_ , """wb""" ) as fi:
__lowercase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (str(A_ ), str(A_ ))
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , A_ : List[str] , A_ : str = "en" , A_ : Optional[List[str]] = None , A_ : str = "ro" , **A_ : List[Any] , ):
'''simple docstring'''
__lowercase = src_lang
__lowercase = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A_ , A_ , **A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : List[Any] , A_ : Optional[str] , A_ : Optional[str] , **A_ : Optional[Any] ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__lowercase = src_lang
__lowercase = self(A_ , add_special_tokens=A_ , **A_ )
__lowercase = self.get_lang_id(A_ )
__lowercase = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
__lowercase = self.lang_token_to_id[lang_token]
__lowercase = [self.cur_lang_id]
__lowercase = [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
__lowercase = self.lang_token_to_id[lang_token]
__lowercase = [self.cur_lang_id]
__lowercase = [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
return self.lang_token_to_id[lang_token]
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Dict[str, Any] ):
"""simple docstring"""
__lowercase = sentencepiece.SentencePieceProcessor(**UpperCamelCase__ )
spm.Load(str(UpperCamelCase__ ) )
return spm
def lowerCAmelCase_ ( UpperCamelCase__ : str ):
"""simple docstring"""
with open(UpperCamelCase__ , """r""" ) as f:
return json.load(UpperCamelCase__ )
def lowerCAmelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : str ):
"""simple docstring"""
with open(UpperCamelCase__ , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ , indent=2 )
| 616
| 1
|
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(snake_case__ , snake_case__ ) ) )
def __lowerCAmelCase ( snake_case__ ):
if point:
if isinstance(snake_case__ , snake_case__ ):
for item in point:
if not isinstance(snake_case__ , (int, float) ):
__UpperCamelCase : Optional[Any] = (
"Expected a list of numbers as input, found "
F"{type(snake_case__ ).__name__}"
)
raise TypeError(snake_case__ )
else:
__UpperCamelCase : Optional[int] = F"Expected a list of numbers as input, found {type(snake_case__ ).__name__}"
raise TypeError(snake_case__ )
else:
raise ValueError("Missing an input" )
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(snake_case__ , snake_case__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 399
|
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=9_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=5_0 , _UpperCAmelCase=0.02 , _UpperCAmelCase=True , _UpperCAmelCase=None , ) -> Any:
__UpperCamelCase : Union[str, Any] = parent
__UpperCamelCase : Dict = batch_size
__UpperCamelCase : Dict = seq_length
__UpperCamelCase : Optional[int] = is_training
__UpperCamelCase : Optional[Any] = use_input_mask
__UpperCamelCase : Optional[Any] = vocab_size
__UpperCamelCase : Tuple = hidden_size
__UpperCamelCase : Optional[Any] = num_hidden_layers
__UpperCamelCase : Optional[Any] = num_attention_heads
__UpperCamelCase : Union[str, Any] = intermediate_size
__UpperCamelCase : List[str] = hidden_act
__UpperCamelCase : Optional[int] = hidden_dropout_prob
__UpperCamelCase : Any = attention_probs_dropout_prob
__UpperCamelCase : Dict = max_position_embeddings
__UpperCamelCase : List[str] = initializer_range
__UpperCamelCase : Union[str, Any] = use_labels
__UpperCamelCase : Optional[Any] = scope
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Any = None
if self.use_input_mask:
__UpperCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
__UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Any = self.get_config()
return config, input_ids, input_mask, token_labels
def a_ (self ) -> Tuple:
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , )
def a_ (self ) -> Dict:
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Any = self.prepare_config_and_inputs()
__UpperCamelCase : int = True
__UpperCamelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] = BertGenerationEncoder(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : int = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
__UpperCamelCase : List[Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Any:
__UpperCamelCase : Any = True
__UpperCamelCase : Optional[Any] = BertGenerationEncoder(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : Tuple = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
__UpperCamelCase : Any = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) -> Optional[int]:
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Dict = BertGenerationDecoder(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
# first forward pass
__UpperCamelCase : Tuple = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase , )
__UpperCamelCase : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase : Any = torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase : Any = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )["hidden_states"][0]
__UpperCamelCase : str = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )["hidden_states"][0]
# select random slice
__UpperCamelCase : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase : int = output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , ) -> Optional[Any]:
__UpperCamelCase : List[Any] = BertGenerationDecoder(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ (self ) -> Dict:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : str = self.prepare_config_and_inputs()
__UpperCamelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
A = (BertGenerationDecoder,) if is_torch_available() else ()
A = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[Any] = BertGenerationEncoderTester(self )
__UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> List[Any]:
self.config_tester.run_common_tests()
def a_ (self ) -> List[str]:
__UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
__UpperCamelCase : List[Any] = "bert"
self.model_tester.create_and_check_model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def a_ (self ) -> Any:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
# This regression test was failing with PyTorch < 1.3
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
__UpperCamelCase : Optional[int] = None
self.model_tester.create_and_check_model_as_decoder(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
def a_ (self ) -> Dict:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase )
@slow
def a_ (self ) -> int:
__UpperCamelCase : Dict = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(_UpperCAmelCase )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : List[str] = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
__UpperCamelCase : List[str] = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__UpperCamelCase : Any = model(_UpperCAmelCase )[0]
__UpperCamelCase : List[Any] = torch.Size([1, 8, 1_0_2_4] )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : List[Any] = torch.tensor(
[[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : Any = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
__UpperCamelCase : str = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__UpperCamelCase : Tuple = model(_UpperCAmelCase )[0]
__UpperCamelCase : Tuple = torch.Size([1, 8, 5_0_3_5_8] )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : int = torch.tensor(
[[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 399
| 1
|
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , **_UpperCAmelCase , ):
super().__init__(features=_UpperCAmelCase , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase , **_UpperCAmelCase )
__a : List[Any] = Sql(
cache_dir=_UpperCAmelCase , features=_UpperCAmelCase , sql=_UpperCAmelCase , con=_UpperCAmelCase , **_UpperCAmelCase , )
def _lowerCamelCase ( self ):
__a : Any = None
__a : Tuple = None
__a : str = None
__a : Any = None
self.builder.download_and_prepare(
download_config=_UpperCAmelCase , download_mode=_UpperCAmelCase , verification_mode=_UpperCAmelCase , base_path=_UpperCAmelCase , )
# Build dataset for splits
__a : List[str] = self.builder.as_dataset(
split='''train''' , verification_mode=_UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , **_UpperCAmelCase , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )
__a : Dict = dataset
__a : Optional[Any] = name
__a : Tuple = con
__a : str = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
__a : Tuple = num_proc
__a : List[str] = to_sql_kwargs
def _lowerCamelCase ( self ):
__a : List[Any] = self.to_sql_kwargs.pop('''sql''' , _UpperCAmelCase )
__a : Dict = self.to_sql_kwargs.pop('''con''' , _UpperCAmelCase )
__a : Tuple = self.to_sql_kwargs.pop('''index''' , _UpperCAmelCase )
__a : List[Any] = self._write(index=_UpperCAmelCase , **self.to_sql_kwargs )
return written
def _lowerCamelCase ( self , _UpperCAmelCase ):
__a , __a , __a : Optional[Any] = args
__a : Union[str, Any] = {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
__a : Any = query_table(
table=self.dataset.data , key=slice(_UpperCAmelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
__a : Dict = batch.to_pandas()
__a : Union[str, Any] = df.to_sql(self.name , self.con , index=_UpperCAmelCase , **_UpperCAmelCase )
return num_rows or len(_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , **_UpperCAmelCase ):
__a : Optional[Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
__a , __a : List[Any] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _UpperCAmelCase , _UpperCAmelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += num_rows
return written
| 52
|
from __future__ import annotations
import time
import numpy as np
A__ = [8, 5, 9, 7]
A__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class _lowerCAmelCase :
def __init__( self : Any , __snake_case : list[int] , __snake_case : list[list[int]] , __snake_case : list[list[int]] , ):
lowerCamelCase :List[str] = claim_vector
lowerCamelCase :Tuple = allocated_resources_table
lowerCamelCase :Tuple = maximum_claim_table
def snake_case ( self : Union[str, Any] ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def snake_case ( self : Optional[int] ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def snake_case ( self : List[Any] ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__snake_case ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def snake_case ( self : List[Any] ):
return {self.__need().index(__snake_case ): i for i in self.__need()}
def snake_case ( self : Any , **__snake_case : Tuple ):
lowerCamelCase :Optional[Any] = self.__need()
lowerCamelCase :Optional[Any] = self.__allocated_resources_table
lowerCamelCase :Tuple = self.__available_resources()
lowerCamelCase :Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowerCamelCase :Dict = False
for each_need in need_list:
lowerCamelCase :Union[str, Any] = True
for index, need in enumerate(__snake_case ):
if need > available_resources[index]:
lowerCamelCase :Union[str, Any] = False
break
if execution:
lowerCamelCase :Optional[int] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowerCamelCase :int = original_need_index
print(F"Process {process_number + 1} is executing." )
# remove the process run from stack
need_list.remove(__snake_case )
# update available/freed resources stack
lowerCamelCase :Optional[Any] = np.array(__snake_case ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(__snake_case ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def snake_case ( self : List[Any] ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
F"P{self.__allocated_resources_table.index(__snake_case ) + 1}"
+ ''' '''.join(F"{it:>8}" for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
F"P{self.__maximum_claim_table.index(__snake_case ) + 1}"
+ ''' '''.join(F"{it:>8}" for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(__snake_case ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(__snake_case ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 166
| 0
|
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : str = """hf-internal-testing/tiny-random-t5"""
__lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained(lowerCAmelCase )
__lowerCAmelCase : int = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase )
__lowerCAmelCase : str = tokenizer("""This is me""" , return_tensors="""pt""" )
__lowerCAmelCase : str = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
__lowerCAmelCase : List[Any] = model.generate(**lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase )
__lowerCAmelCase : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
__lowerCAmelCase : Optional[Any] = model_reloaded.generate(**lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase ) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = """hf-internal-testing/tiny-random-t5"""
__lowerCAmelCase : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(lowerCAmelCase ):
model.save_pretrained(lowerCAmelCase )
__lowerCAmelCase : int = model.reverse_bettertransformer()
model.save_pretrained(lowerCAmelCase )
| 218
|
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def snake_case_ (__A : str = "" ) -> dict[str, float]:
__lowerCAmelCase : str = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
__lowerCAmelCase : Union[str, Any] = BeautifulSoup(requests.get(__A ).text , """html.parser""" )
__lowerCAmelCase : int = soup.find_all("""td""" , attrs="""titleColumn""" )
__lowerCAmelCase : int = soup.find_all("""td""" , class_="""ratingColumn imdbRating""" )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(__A , __A )
}
def snake_case_ (__A : str = "IMDb_Top_250_Movies.csv" ) -> None:
__lowerCAmelCase : int = get_imdb_top_aaa_movies()
with open(__A , """w""" , newline="""""" ) as out_file:
__lowerCAmelCase : Dict = csv.writer(__A )
writer.writerow(["""Movie title""", """IMDb rating"""] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 218
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Tuple = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
lowercase__ : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98
|
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class lowerCamelCase :
def __init__( self , lowercase__ , lowercase__=2 , lowercase__=3_2 , lowercase__=1_6 , lowercase__=3 , lowercase__=True , lowercase__=True , lowercase__=3_2 , lowercase__=4 , lowercase__=[0, 1, 2, 3] , lowercase__=4 , lowercase__=3_7 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=0.0_2 , lowercase__=3 , lowercase__=[1, 3_8_4, 2_4, 2_4] , lowercase__=True , lowercase__=None , ):
__UpperCAmelCase : Any = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Optional[int] = image_size
__UpperCAmelCase : Tuple = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : Optional[Any] = is_training
__UpperCAmelCase : Union[str, Any] = use_labels
__UpperCAmelCase : List[Any] = hidden_size
__UpperCAmelCase : Optional[Any] = num_hidden_layers
__UpperCAmelCase : Any = backbone_out_indices
__UpperCAmelCase : Union[str, Any] = num_attention_heads
__UpperCAmelCase : Dict = intermediate_size
__UpperCAmelCase : Dict = hidden_act
__UpperCAmelCase : Dict = hidden_dropout_prob
__UpperCAmelCase : List[Any] = attention_probs_dropout_prob
__UpperCAmelCase : List[Any] = initializer_range
__UpperCAmelCase : Union[str, Any] = num_labels
__UpperCAmelCase : List[Any] = backbone_featmap_shape
__UpperCAmelCase : Any = scope
__UpperCAmelCase : Optional[int] = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
__UpperCAmelCase : Optional[Any] = (image_size // patch_size) ** 2
__UpperCAmelCase : Any = num_patches + 1
def A( self):
__UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : List[str] = None
if self.use_labels:
__UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values, labels
def A( self):
__UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
'''num_groups''': 2,
}
return DPTConfig(
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 , backbone_out_indices=self.backbone_out_indices , 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=lowercase__ , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowercase__ , backbone_featmap_shape=self.backbone_featmap_shape , )
def A( self , lowercase__ , lowercase__ , lowercase__):
__UpperCAmelCase : List[str] = DPTModel(config=lowercase__)
model.to(lowercase__)
model.eval()
__UpperCAmelCase : Dict = model(lowercase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def A( self , lowercase__ , lowercase__ , lowercase__):
__UpperCAmelCase : List[str] = self.num_labels
__UpperCAmelCase : Optional[Any] = DPTForDepthEstimation(lowercase__)
model.to(lowercase__)
model.eval()
__UpperCAmelCase : str = model(lowercase__)
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size))
def A( self , lowercase__ , lowercase__ , lowercase__):
__UpperCAmelCase : Tuple = self.num_labels
__UpperCAmelCase : Optional[int] = DPTForSemanticSegmentation(lowercase__)
model.to(lowercase__)
model.eval()
__UpperCAmelCase : str = model(lowercase__ , labels=lowercase__)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def A( self):
__UpperCAmelCase : int = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Optional[int] = config_and_inputs
__UpperCAmelCase : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
_lowerCAmelCase : Union[str, Any] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
_lowerCAmelCase : Optional[int] = (
{
'''depth-estimation''': DPTForDepthEstimation,
'''feature-extraction''': DPTModel,
'''image-segmentation''': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_lowerCAmelCase : Any = False
_lowerCAmelCase : str = False
_lowerCAmelCase : List[Any] = False
def A( self):
__UpperCAmelCase : Any = DPTModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=lowercase__ , has_text_modality=lowercase__ , hidden_size=3_7)
def A( self):
self.config_tester.run_common_tests()
@unittest.skip(reason='''DPT does not use inputs_embeds''')
def A( self):
pass
def A( self):
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : str = model_class(lowercase__)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
__UpperCAmelCase : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ , nn.Linear))
def A( self):
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Optional[int] = model_class(lowercase__)
__UpperCAmelCase : int = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : List[str] = [*signature.parameters.keys()]
__UpperCAmelCase : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase__)
def A( self):
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__)
def A( self):
__UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*lowercase__)
def A( self):
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__)
def A( self):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__UpperCAmelCase , __UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = True
if model_class in get_values(lowercase__):
continue
__UpperCAmelCase : List[Any] = model_class(lowercase__)
model.to(lowercase__)
model.train()
__UpperCAmelCase : Any = self._prepare_for_class(lowercase__ , lowercase__ , return_labels=lowercase__)
__UpperCAmelCase : Any = model(**lowercase__).loss
loss.backward()
def A( self):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__UpperCAmelCase , __UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : str = True
if model_class in get_values(lowercase__) or not model_class.supports_gradient_checkpointing:
continue
__UpperCAmelCase : Tuple = model_class(lowercase__)
model.to(lowercase__)
model.gradient_checkpointing_enable()
model.train()
__UpperCAmelCase : Tuple = self._prepare_for_class(lowercase__ , lowercase__ , return_labels=lowercase__)
__UpperCAmelCase : Union[str, Any] = model(**lowercase__).loss
loss.backward()
def A( self):
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : List[Any] = _config_zero_init(lowercase__)
for model_class in self.all_model_classes:
__UpperCAmelCase : Tuple = model_class(config=lowercase__)
# Skip the check for the backbone
__UpperCAmelCase : List[Any] = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
__UpperCAmelCase : Optional[Any] = [F"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def A( self):
pass
@slow
def A( self):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
__UpperCAmelCase : Optional[int] = DPTModel.from_pretrained(lowercase__)
self.assertIsNotNone(lowercase__)
def A( self):
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : List[str] = '''add'''
with self.assertRaises(lowercase__):
__UpperCAmelCase : Optional[Any] = DPTForDepthEstimation(lowercase__)
def __SCREAMING_SNAKE_CASE ( ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
@slow
class lowerCamelCase ( unittest.TestCase ):
def A( self):
__UpperCAmelCase : str = DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
__UpperCAmelCase : str = DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(lowercase__)
__UpperCAmelCase : List[str] = prepare_img()
__UpperCAmelCase : Tuple = image_processor(images=lowercase__ , return_tensors='''pt''').to(lowercase__)
# forward pass
with torch.no_grad():
__UpperCAmelCase : List[Any] = model(**lowercase__)
__UpperCAmelCase : str = outputs.predicted_depth
# verify the predicted depth
__UpperCAmelCase : Union[str, Any] = torch.Size((1, 3_8_4, 3_8_4))
self.assertEqual(predicted_depth.shape , lowercase__)
__UpperCAmelCase : List[str] = torch.tensor(
[[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]).to(lowercase__)
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0 , lowercase__ , atol=1e-4))
| 462
| 0
|
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = np.inf
def set_batch_size(UpperCamelCase__ ) -> None:
nonlocal batch_size
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
snake_case_ = min(UpperCamelCase__ , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
snake_case_ = min(UpperCamelCase__ , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ) and feature.dtype == "binary":
snake_case_ = min(UpperCamelCase__ , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(UpperCamelCase__ , UpperCamelCase__ )
return None if batch_size is np.inf else batch_size
class lowercase ( lowercase_ ):
def __init__( self , snake_case , snake_case = None , snake_case = None , snake_case = None , snake_case = False , snake_case = False , snake_case = None , **snake_case , ):
super().__init__(
snake_case , split=snake_case , features=snake_case , cache_dir=snake_case , keep_in_memory=snake_case , streaming=snake_case , num_proc=snake_case , **snake_case , )
snake_case_ = path_or_paths if isinstance(snake_case , snake_case ) else {self.split: path_or_paths}
snake_case_ = _PACKAGED_DATASETS_MODULES['parquet'][1]
snake_case_ = Parquet(
cache_dir=snake_case , data_files=snake_case , features=snake_case , hash=snake_case , **snake_case , )
def a ( self ):
# Build iterable dataset
if self.streaming:
snake_case_ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
self.builder.download_and_prepare(
download_config=snake_case , download_mode=snake_case , verification_mode=snake_case , base_path=snake_case , num_proc=self.num_proc , )
snake_case_ = self.builder.as_dataset(
split=self.split , verification_mode=snake_case , in_memory=self.keep_in_memory )
return dataset
class lowercase :
def __init__( self , snake_case , snake_case , snake_case = None , **snake_case , ):
snake_case_ = dataset
snake_case_ = path_or_buf
snake_case_ = batch_size or get_writer_batch_size(dataset.features )
snake_case_ = parquet_writer_kwargs
def a ( self ):
snake_case_ = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , 'wb+' ) as buffer:
snake_case_ = self._write(file_obj=snake_case , batch_size=snake_case , **self.parquet_writer_kwargs )
else:
snake_case_ = self._write(file_obj=self.path_or_buf , batch_size=snake_case , **self.parquet_writer_kwargs )
return written
def a ( self , snake_case , snake_case , **snake_case ):
snake_case_ = 0
snake_case_ = parquet_writer_kwargs.pop('path_or_buf' , snake_case )
snake_case_ = self.dataset.features.arrow_schema
snake_case_ = pq.ParquetWriter(snake_case , schema=snake_case , **snake_case )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , snake_case ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating parquet from Arrow format' , ):
snake_case_ = query_table(
table=self.dataset._data , key=slice(snake_case , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(snake_case )
written += batch.nbytes
writer.close()
return written
| 108
|
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = MvpTokenizer
__SCREAMING_SNAKE_CASE : Optional[int] = MvpTokenizerFast
__SCREAMING_SNAKE_CASE : Optional[int] = True
__SCREAMING_SNAKE_CASE : Tuple = filter_roberta_detectors
def a ( self ):
super().setUp()
snake_case_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
snake_case_ = dict(zip(snake_case , range(len(snake_case ) ) ) )
snake_case_ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
snake_case_ = {'unk_token': '<unk>'}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(snake_case ) )
def a ( self , **snake_case ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case )
def a ( self , **snake_case ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **snake_case )
def a ( self , snake_case ):
return "lower newer", "lower newer"
@cached_property
def a ( self ):
return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )
@cached_property
def a ( self ):
return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )
@require_torch
def a ( self ):
snake_case_ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case_ = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ = tokenizer(snake_case , max_length=len(snake_case ) , padding=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
snake_case_ = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
# Test that special tokens are reset
@require_torch
def a ( self ):
snake_case_ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ = tokenizer(snake_case , padding=snake_case , return_tensors='pt' )
# check if input_ids are returned and no labels
self.assertIn('input_ids' , snake_case )
self.assertIn('attention_mask' , snake_case )
self.assertNotIn('labels' , snake_case )
self.assertNotIn('decoder_attention_mask' , snake_case )
@require_torch
def a ( self ):
snake_case_ = [
'Summary of the text.',
'Another summary.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ = tokenizer(text_target=snake_case , max_length=32 , padding='max_length' , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
@require_torch
def a ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ = tokenizer(
['I am a small frog' * 1024, 'I am a small frog'] , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def a ( self ):
snake_case_ = ['A long paragraph for summarization.']
snake_case_ = [
'Summary of the text.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
snake_case_ = tokenizer(snake_case , text_target=snake_case , return_tensors='pt' )
snake_case_ = inputs['input_ids']
snake_case_ = inputs['labels']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def a ( self ):
pass
def a ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
snake_case_ = self.tokenizer_class.from_pretrained(snake_case , **snake_case )
snake_case_ = 'A, <mask> AllenNLP sentence.'
snake_case_ = tokenizer_r.encode_plus(snake_case , add_special_tokens=snake_case , return_token_type_ids=snake_case )
snake_case_ = tokenizer_p.encode_plus(snake_case , add_special_tokens=snake_case , return_token_type_ids=snake_case )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
snake_case_ = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
snake_case_ = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
| 108
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : Tuple = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class _UpperCAmelCase ( _A ):
"""simple docstring"""
A = '''nllb-moe'''
A = ['''past_key_values''']
A = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , _lowerCAmelCase=128_112 , _lowerCAmelCase=1_024 , _lowerCAmelCase=12 , _lowerCAmelCase=4_096 , _lowerCAmelCase=16 , _lowerCAmelCase=12 , _lowerCAmelCase=4_096 , _lowerCAmelCase=16 , _lowerCAmelCase=0.05 , _lowerCAmelCase=0.05 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase="relu" , _lowerCAmelCase=1_024 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=2 , _lowerCAmelCase=True , _lowerCAmelCase=False , _lowerCAmelCase="float32" , _lowerCAmelCase=False , _lowerCAmelCase=128 , _lowerCAmelCase=64 , _lowerCAmelCase=4 , _lowerCAmelCase=4 , _lowerCAmelCase=0.001 , _lowerCAmelCase=0.001 , _lowerCAmelCase="all" , _lowerCAmelCase=False , _lowerCAmelCase=False , _lowerCAmelCase=1.0 , _lowerCAmelCase=0.2 , _lowerCAmelCase=1 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=False , **_lowerCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = vocab_size
lowerCAmelCase__ :List[Any] = max_position_embeddings
lowerCAmelCase__ :Union[str, Any] = d_model
lowerCAmelCase__ :str = encoder_ffn_dim
lowerCAmelCase__ :Tuple = encoder_layers
lowerCAmelCase__ :Union[str, Any] = encoder_attention_heads
lowerCAmelCase__ :List[Any] = decoder_ffn_dim
lowerCAmelCase__ :Optional[int] = decoder_layers
lowerCAmelCase__ :List[Any] = decoder_attention_heads
lowerCAmelCase__ :List[str] = dropout
lowerCAmelCase__ :Optional[Any] = attention_dropout
lowerCAmelCase__ :str = activation_dropout
lowerCAmelCase__ :Dict = activation_function
lowerCAmelCase__ :Optional[Any] = init_std
lowerCAmelCase__ :Optional[Any] = encoder_layerdrop
lowerCAmelCase__ :Dict = decoder_layerdrop
lowerCAmelCase__ :Union[str, Any] = use_cache
lowerCAmelCase__ :int = encoder_layers
lowerCAmelCase__ :Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
lowerCAmelCase__ :List[str] = router_z_loss_coef
lowerCAmelCase__ :List[str] = router_aux_loss_coef
lowerCAmelCase__ :Optional[int] = decoder_sparse_step
lowerCAmelCase__ :List[str] = encoder_sparse_step
lowerCAmelCase__ :Any = num_experts
lowerCAmelCase__ :str = expert_capacity
lowerCAmelCase__ :int = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
lowerCAmelCase__ :Optional[Any] = router_dtype
lowerCAmelCase__ :List[Any] = router_ignore_padding_tokens
lowerCAmelCase__ :int = batch_prioritized_routing
lowerCAmelCase__ :str = second_expert_policy
lowerCAmelCase__ :Dict = normalize_router_prob_before_dropping
lowerCAmelCase__ :int = moe_eval_capacity_token_fraction
lowerCAmelCase__ :List[Any] = moe_token_dropout
lowerCAmelCase__ :Optional[Any] = output_router_logits
super().__init__(
pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , is_encoder_decoder=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
| 145
|
from collections import Counter
from timeit import timeit
def snake_case__ ( UpperCAmelCase : str = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def snake_case__ ( UpperCAmelCase : str = "" ):
if len(UpperCAmelCase ) == 0:
return True
lowerCAmelCase__ :List[str] = input_str.replace(" " , "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
lowerCAmelCase__ :dict[str, int] = {}
for character in lower_case_input_str:
lowerCAmelCase__ :Tuple = character_freq_dict.get(UpperCAmelCase , 0 ) + 1
lowerCAmelCase__ :Dict = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def snake_case__ ( UpperCAmelCase : str = "" ):
print("\nFor string = " , UpperCAmelCase , ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()" , "\tans =" , can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
print(
"> can_string_be_rearranged_as_palindrome()" , "\tans =" , can_string_be_rearranged_as_palindrome(UpperCAmelCase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
if __name__ == "__main__":
_a : Any = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_a : Tuple = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")
| 145
| 1
|
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__UpperCAmelCase = False
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "ybelkada/fonts"
def lowerCAmelCase_ ( ):
'''simple docstring'''
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
'Pix2StructImageProcessor. Please upgrade torch.' )
def lowerCAmelCase_ ( __A : List[Any] , __A : str , __A : int ):
'''simple docstring'''
requires_backends(__A , ['torch'] )
_check_torch_version()
snake_case: str = image_tensor.unsqueeze(0 )
snake_case: Optional[int] = torch.nn.functional.unfold(__A , (patch_height, patch_width) , stride=(patch_height, patch_width) )
snake_case: List[Any] = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , __A , __A , -1 )
snake_case: List[Any] = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowerCAmelCase_ ( __A : str , __A : int = 36 , __A : str = "black" , __A : str = "white" , __A : int = 5 , __A : int = 5 , __A : int = 5 , __A : int = 5 , __A : Optional[bytes] = None , __A : Optional[str] = None , ):
'''simple docstring'''
requires_backends(__A , 'vision' )
# Add new lines so that each line is no more than 80 characters.
snake_case: List[str] = textwrap.TextWrapper(width=80 )
snake_case: Dict = wrapper.wrap(text=__A )
snake_case: List[Any] = '\n'.join(__A )
if font_bytes is not None and font_path is None:
snake_case: Optional[Any] = io.BytesIO(__A )
elif font_path is not None:
snake_case: Union[str, Any] = font_path
else:
snake_case: List[Any] = hf_hub_download(__A , 'Arial.TTF' )
snake_case: Dict = ImageFont.truetype(__A , encoding='UTF-8' , size=__A )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
snake_case: int = ImageDraw.Draw(Image.new('RGB' , (1, 1) , __A ) )
snake_case , snake_case , snake_case , snake_case: List[str] = temp_draw.textbbox((0, 0) , __A , __A )
# Create the actual image with a bit of padding around the text.
snake_case: str = text_width + left_padding + right_padding
snake_case: Union[str, Any] = text_height + top_padding + bottom_padding
snake_case: Any = Image.new('RGB' , (image_width, image_height) , __A )
snake_case: Tuple = ImageDraw.Draw(__A )
draw.text(xy=(left_padding, top_padding) , text=__A , fill=__A , font=__A )
return image
def lowerCAmelCase_ ( __A : np.ndarray , __A : str , **__A : int ):
'''simple docstring'''
requires_backends(__A , 'vision' )
# Convert to PIL image if necessary
snake_case: Optional[Any] = to_pil_image(__A )
snake_case: Tuple = render_text(__A , **__A )
snake_case: int = max(header_image.width , image.width )
snake_case: Optional[int] = int(image.height * (new_width / image.width) )
snake_case: List[Any] = int(header_image.height * (new_width / header_image.width) )
snake_case: Optional[int] = Image.new('RGB' , (new_width, new_height + new_header_height) , 'white' )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
snake_case: str = to_numpy_array(__A )
if infer_channel_dimension_format(__A ) == ChannelDimension.LAST:
snake_case: List[Any] = to_channel_dimension_format(__A , ChannelDimension.LAST )
return new_image
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = ["flattened_patches"]
def __init__( self , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 20_48 , SCREAMING_SNAKE_CASE__ = False , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
snake_case: Dict = do_normalize
snake_case: int = do_convert_rgb
snake_case: str = max_patches
snake_case: Dict = is_vqa
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
requires_backends(self.extract_flattened_patches , 'torch' )
_check_torch_version()
# convert to torch
snake_case: Optional[Any] = to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , ChannelDimension.FIRST )
snake_case: int = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
snake_case , snake_case: str = patch_size['height'], patch_size['width']
snake_case , snake_case: Union[str, Any] = get_image_size(SCREAMING_SNAKE_CASE__ )
# maximize scale s.t.
snake_case: List[Any] = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
snake_case: Tuple = max(min(math.floor(scale * image_height / patch_height ) , SCREAMING_SNAKE_CASE__ ) , 1 )
snake_case: Optional[int] = max(min(math.floor(scale * image_width / patch_width ) , SCREAMING_SNAKE_CASE__ ) , 1 )
snake_case: str = max(num_feasible_rows * patch_height , 1 )
snake_case: Union[str, Any] = max(num_feasible_cols * patch_width , 1 )
snake_case: Optional[int] = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode='bilinear' , align_corners=SCREAMING_SNAKE_CASE__ , antialias=SCREAMING_SNAKE_CASE__ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
snake_case: Optional[int] = torch_extract_patches(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Any = patches.shape
snake_case: Optional[int] = patches_shape[1]
snake_case: List[Any] = patches_shape[2]
snake_case: List[str] = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
snake_case: int = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
snake_case: List[Any] = torch.arange(SCREAMING_SNAKE_CASE__ ).reshape([rows, 1] ).repeat(1 , SCREAMING_SNAKE_CASE__ ).reshape([rows * columns, 1] )
snake_case: List[Any] = torch.arange(SCREAMING_SNAKE_CASE__ ).reshape([1, columns] ).repeat(SCREAMING_SNAKE_CASE__ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
snake_case: Dict = row_ids.to(torch.floataa )
snake_case: List[str] = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
snake_case: Any = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
snake_case: Tuple = torch.nn.functional.pad(SCREAMING_SNAKE_CASE__ , [0, 0, 0, max_patches - (rows * columns)] ).float()
snake_case: Optional[int] = to_numpy_array(SCREAMING_SNAKE_CASE__ )
return result
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if image.dtype == np.uinta:
snake_case: Union[str, Any] = image.astype(np.floataa )
# take mean across the whole `image`
snake_case: Any = np.mean(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = np.std(SCREAMING_SNAKE_CASE__ )
snake_case: int = max(SCREAMING_SNAKE_CASE__ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: str = do_normalize if do_normalize is not None else self.do_normalize
snake_case: Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case: str = patch_size if patch_size is not None else self.patch_size
snake_case: Any = max_patches if max_patches is not None else self.max_patches
snake_case: Optional[Any] = self.is_vqa
if kwargs.get('data_format' , SCREAMING_SNAKE_CASE__ ) is not None:
raise ValueError('data_format is not an accepted input as the outputs are ' )
snake_case: int = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case: str = [convert_to_rgb(SCREAMING_SNAKE_CASE__ ) for image in images]
# All transformations expect numpy arrays.
snake_case: str = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError('A header text must be provided for VQA models.' )
snake_case: Optional[Any] = kwargs.pop('font_bytes' , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = kwargs.pop('font_path' , SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: str = [header_text] * len(SCREAMING_SNAKE_CASE__ )
snake_case: int = [
render_header(SCREAMING_SNAKE_CASE__ , header_text[i] , font_bytes=SCREAMING_SNAKE_CASE__ , font_path=SCREAMING_SNAKE_CASE__ )
for i, image in enumerate(SCREAMING_SNAKE_CASE__ )
]
if do_normalize:
snake_case: Optional[Any] = [self.normalize(image=SCREAMING_SNAKE_CASE__ ) for image in images]
# convert to torch tensor and permute
snake_case: List[Any] = [
self.extract_flattened_patches(image=SCREAMING_SNAKE_CASE__ , max_patches=SCREAMING_SNAKE_CASE__ , patch_size=SCREAMING_SNAKE_CASE__ )
for image in images
]
# create attention mask in numpy
snake_case: Any = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
snake_case: List[str] = BatchFeature(
data={'flattened_patches': images, 'attention_mask': attention_masks} , tensor_type=SCREAMING_SNAKE_CASE__ )
return encoded_outputs
| 692
|
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }')
| 692
| 1
|
'''simple docstring'''
from PIL import Image
def A_( A : Image , A : int):
UpperCamelCase = (259 * (level + 255)) / (255 * (259 - level))
def contrast(A : int) -> int:
return int(128 + factor * (c - 128))
return img.point(A)
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
lowerCAmelCase : str = change_contrast(img, 1_70)
cont_img.save('image_data/lena_high_contrast.png', format='png')
| 3
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Optional[int] = {
'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = """perceiver"""
def __init__( self , A_=256 , A_=1280 , A_=768 , A_=1 , A_=26 , A_=8 , A_=8 , A_=None , A_=None , A_="kv" , A_=1 , A_=1 , A_="gelu" , A_=0.1 , A_=0.02 , A_=1e-12 , A_=True , A_=262 , A_=2048 , A_=56 , A_=[368, 496] , A_=16 , A_=1920 , A_=16 , A_=[1, 16, 224, 224] , **A_ , )-> str:
'''simple docstring'''
super().__init__(**A_ )
UpperCamelCase = num_latents
UpperCamelCase = d_latents
UpperCamelCase = d_model
UpperCamelCase = num_blocks
UpperCamelCase = num_self_attends_per_block
UpperCamelCase = num_self_attention_heads
UpperCamelCase = num_cross_attention_heads
UpperCamelCase = qk_channels
UpperCamelCase = v_channels
UpperCamelCase = cross_attention_shape_for_attention
UpperCamelCase = self_attention_widening_factor
UpperCamelCase = cross_attention_widening_factor
UpperCamelCase = hidden_act
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = use_query_residual
# masked language modeling attributes
UpperCamelCase = vocab_size
UpperCamelCase = max_position_embeddings
# image classification attributes
UpperCamelCase = image_size
# flow attributes
UpperCamelCase = train_size
# multimodal autoencoding attributes
UpperCamelCase = num_frames
UpperCamelCase = audio_samples_per_frame
UpperCamelCase = samples_per_patch
UpperCamelCase = output_shape
class SCREAMING_SNAKE_CASE__ ( snake_case_):
@property
def UpperCAmelCase_ ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
UpperCamelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def UpperCAmelCase_ ( self )-> float:
'''simple docstring'''
return 1e-4
def UpperCAmelCase_ ( self , A_ , A_ = -1 , A_ = -1 , A_ = -1 , A_ = False , A_ = None , A_ = 3 , A_ = 40 , A_ = 40 , )-> Mapping[str, Any]:
'''simple docstring'''
if isinstance(A_ , A_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCamelCase = compute_effective_axis_dimension(
A_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCamelCase = preprocessor.num_special_tokens_to_add(A_ )
UpperCamelCase = compute_effective_axis_dimension(
A_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=A_ )
# Generate dummy inputs according to compute batch and sequence
UpperCamelCase = [' '.join(['a'] ) * seq_length] * batch_size
UpperCamelCase = dict(preprocessor(A_ , return_tensors=A_ ) )
UpperCamelCase = inputs.pop('input_ids' )
return inputs
elif isinstance(A_ , A_ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCamelCase = compute_effective_axis_dimension(A_ , fixed_dimension=OnnxConfig.default_fixed_batch )
UpperCamelCase = self._generate_dummy_images(A_ , A_ , A_ , A_ )
UpperCamelCase = dict(preprocessor(images=A_ , return_tensors=A_ ) )
UpperCamelCase = inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 3
| 1
|
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def A_ ( __a : List[Any] , __a : List[str] , __a : int , __a : List[str] ):
"""simple docstring"""
a__ = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, nicht wahr?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
a__ = {
"""wmt16-en-de-dist-12-1""": [2_8.3, 2_7.5_2],
"""wmt16-en-de-dist-6-1""": [2_7.4, 2_7.1_1],
"""wmt16-en-de-12-1""": [2_6.9, 2_5.7_5],
}
a__ = F'''{src_lang}-{tgt_lang}'''
a__ = F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
'''
model_card_dir.mkdir(parents=__a , exist_ok=__a )
a__ = os.path.join(__a , """README.md""" )
print(F'''Generating {path}''' )
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(__a )
# make sure we are under the root of the project
UpperCAmelCase = Path(__file__).resolve().parent.parent.parent
UpperCAmelCase = repo_dir / """model_cards"""
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
UpperCAmelCase = model_cards_dir / """allenai""" / model_name
write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)
| 351
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
UpperCAmelCase = None
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
UpperCAmelCase = {
"""vocab_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
},
}
UpperCAmelCase = {
"""albert-base-v1""": 512,
"""albert-large-v1""": 512,
"""albert-xlarge-v1""": 512,
"""albert-xxlarge-v1""": 512,
"""albert-base-v2""": 512,
"""albert-large-v2""": 512,
"""albert-xlarge-v2""": 512,
"""albert-xxlarge-v2""": 512,
}
UpperCAmelCase = """▁"""
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
UpperCamelCase__ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : List[str] = AlbertTokenizer
def __init__( self , a_=None , a_=None , a_=True , a_=True , a_=False , a_="[CLS]" , a_="[SEP]" , a_="<unk>" , a_="[SEP]" , a_="<pad>" , a_="[CLS]" , a_="[MASK]" , **a_ , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
a__ = (
AddedToken(a_ , lstrip=a_ , rstrip=a_ , normalized=a_ )
if isinstance(a_ , a_ )
else mask_token
)
super().__init__(
a_ , tokenizer_file=a_ , do_lower_case=a_ , remove_space=a_ , keep_accents=a_ , bos_token=a_ , eos_token=a_ , unk_token=a_ , sep_token=a_ , pad_token=a_ , cls_token=a_ , mask_token=a_ , **a_ , )
a__ = do_lower_case
a__ = remove_space
a__ = keep_accents
a__ = vocab_file
a__ = False if not self.vocab_file else True
def _a ( self , a_ , a_ = None ):
a__ = [self.sep_token_id]
a__ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self , a_ , a_ = None ):
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 , a_ , a_ = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(a_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ = os.path.join(
a_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ):
copyfile(self.vocab_file , a_ )
return (out_vocab_file,)
| 351
| 1
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ : Optional[int] = logging.get_logger(__name__)
def UpperCAmelCase ( A__: Any , A__: Optional[int]=False ) -> List[str]:
__lowerCamelCase : Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__lowerCamelCase : List[str] = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def UpperCAmelCase ( A__: str , A__: str , A__: List[str]=False ) -> Any:
for i in range(config.num_hidden_layers ):
if base_model:
__lowerCamelCase : Any = ''
else:
__lowerCamelCase : Dict = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
__lowerCamelCase : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
__lowerCamelCase : List[Any] = in_proj_bias[: config.hidden_size]
__lowerCamelCase : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase : Dict = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase : List[Any] = in_proj_bias[-config.hidden_size :]
def UpperCAmelCase ( A__: Optional[Any] ) -> Optional[int]:
__lowerCamelCase : Any = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def UpperCAmelCase ( A__: Dict , A__: Optional[int] , A__: Optional[int] ) -> Tuple:
__lowerCamelCase : List[str] = dct.pop(A__ )
__lowerCamelCase : str = val
def UpperCAmelCase ( ) -> Dict:
__lowerCamelCase : Union[str, Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase : Dict = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase ( A__: Optional[int] , A__: int , A__: Optional[int]=True ) -> Any:
__lowerCamelCase : Tuple = ViTConfig()
# patch_size
if model_name[-1] == "8":
__lowerCamelCase : Union[str, Any] = 8
# set labels if required
if not base_model:
__lowerCamelCase : List[str] = 1000
__lowerCamelCase : int = 'huggingface/label-files'
__lowerCamelCase : List[Any] = 'imagenet-1k-id2label.json'
__lowerCamelCase : Union[str, Any] = json.load(open(hf_hub_download(A__ , A__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase : Tuple = {int(A__ ): v for k, v in idalabel.items()}
__lowerCamelCase : Dict = idalabel
__lowerCamelCase : Tuple = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
__lowerCamelCase : str = 384
__lowerCamelCase : Tuple = 1536
__lowerCamelCase : Tuple = 12
__lowerCamelCase : int = 6
# load original model from torch hub
__lowerCamelCase : int = torch.hub.load('facebookresearch/dino:main' , A__ )
original_model.eval()
# load state_dict of original model, remove and rename some keys
__lowerCamelCase : List[Any] = original_model.state_dict()
if base_model:
remove_classification_head_(A__ )
__lowerCamelCase : str = create_rename_keys(A__ , base_model=A__ )
for src, dest in rename_keys:
rename_key(A__ , A__ , A__ )
read_in_q_k_v(A__ , A__ , A__ )
# load HuggingFace model
if base_model:
__lowerCamelCase : Optional[int] = ViTModel(A__ , add_pooling_layer=A__ ).eval()
else:
__lowerCamelCase : Union[str, Any] = ViTForImageClassification(A__ ).eval()
model.load_state_dict(A__ )
# Check outputs on an image, prepared by ViTImageProcessor
__lowerCamelCase : Dict = ViTImageProcessor()
__lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors='pt' )
__lowerCamelCase : Dict = encoding['pixel_values']
__lowerCamelCase : int = model(A__ )
if base_model:
__lowerCamelCase : Any = original_model(A__ )
assert torch.allclose(A__ , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
__lowerCamelCase : str = original_model(A__ )
assert logits.shape == outputs.logits.shape
assert torch.allclose(A__ , outputs.logits , atol=1E-3 )
Path(A__ ).mkdir(exist_ok=A__ )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''dino_vitb16''',
type=str,
help='''Name of the model trained with DINO you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--base_model''',
action='''store_true''',
help='''Whether to only convert the base model (no projection head weights).''',
)
parser.set_defaults(base_model=True)
a_ : Optional[Any] = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 594
|
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
a_ : Dict = logging.get_logger(__name__)
a_ : Dict = Dict[str, Any]
a_ : str = List[Prediction]
@add_end_docstrings(lowercase__ )
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , *__a , **__a ):
super().__init__(*__a , **__a )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , 'vision' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def snake_case_ ( self , **__a ):
__lowerCamelCase : List[str] = {}
if "threshold" in kwargs:
__lowerCamelCase : Optional[int] = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *__a , **__a ):
return super().__call__(*__a , **__a )
def snake_case_ ( self , __a ):
__lowerCamelCase : Optional[Any] = load_image(__a )
__lowerCamelCase : Any = torch.IntTensor([[image.height, image.width]] )
__lowerCamelCase : Any = self.image_processor(images=[image] , return_tensors='pt' )
if self.tokenizer is not None:
__lowerCamelCase : List[str] = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt' )
__lowerCamelCase : Dict = target_size
return inputs
def snake_case_ ( self , __a ):
__lowerCamelCase : Union[str, Any] = model_inputs.pop('target_size' )
__lowerCamelCase : Optional[Any] = self.model(**__a )
__lowerCamelCase : Any = outputs.__class__({'target_size': target_size, **outputs} )
if self.tokenizer is not None:
__lowerCamelCase : Optional[Any] = model_inputs['bbox']
return model_outputs
def snake_case_ ( self , __a , __a=0.9 ):
__lowerCamelCase : Dict = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__lowerCamelCase , __lowerCamelCase : Dict = target_size[0].tolist()
def unnormalize(__a ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
__lowerCamelCase , __lowerCamelCase : Tuple = model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__lowerCamelCase : List[str] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__lowerCamelCase : Union[str, Any] = [unnormalize(__a ) for bbox in model_outputs['bbox'].squeeze(0 )]
__lowerCamelCase : List[str] = ['score', 'label', 'box']
__lowerCamelCase : Tuple = [dict(zip(__a , __a ) ) for vals in zip(scores.tolist() , __a , __a ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__lowerCamelCase : Optional[int] = self.image_processor.post_process_object_detection(__a , __a , __a )
__lowerCamelCase : Any = raw_annotations[0]
__lowerCamelCase : Any = raw_annotation['scores']
__lowerCamelCase : Tuple = raw_annotation['labels']
__lowerCamelCase : Union[str, Any] = raw_annotation['boxes']
__lowerCamelCase : List[str] = scores.tolist()
__lowerCamelCase : str = [self.model.config.idalabel[label.item()] for label in labels]
__lowerCamelCase : List[Any] = [self._get_bounding_box(__a ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__lowerCamelCase : int = ['score', 'label', 'box']
__lowerCamelCase : int = [
dict(zip(__a , __a ) )
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'] )
]
return annotation
def snake_case_ ( self , __a ):
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : List[Any] = box.int().tolist()
__lowerCamelCase : Dict = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 594
| 1
|
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {'''vocab_file''': '''vocab.txt'''}
UpperCAmelCase_ = {
'''vocab_file''': {
'''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
},
}
UpperCAmelCase_ = {
'''openbmb/cpm-ant-10b''': 1_0_2_4,
}
def lowerCAmelCase_ ( lowercase: List[str] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase: List[str] = collections.OrderedDict()
with open(lowercase , '''r''' , encoding='''utf-8''' ) as reader:
_UpperCamelCase: Dict = reader.readlines()
for index, token in enumerate(lowercase ):
_UpperCamelCase: Optional[int] = token.rstrip('''\n''' )
_UpperCamelCase: Optional[int] = index
return vocab
class __magic_name__ ( __a ):
"""simple docstring"""
def __init__( self : Tuple , _lowercase : List[Any] , _lowercase : Optional[Any]="<unk>" , _lowercase : Optional[Any]=200 ):
"""simple docstring"""
_UpperCamelCase: List[str] = vocab
_UpperCamelCase: Any = unk_token
_UpperCamelCase: Optional[Any] = max_input_chars_per_word
def lowerCAmelCase ( self : Dict , _lowercase : Optional[Any] ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = list(_lowercase )
if len(_lowercase ) > self.max_input_chars_per_word:
return [self.unk_token]
_UpperCamelCase: Optional[Any] = 0
_UpperCamelCase: int = []
while start < len(_lowercase ):
_UpperCamelCase: Tuple = len(_lowercase )
_UpperCamelCase: Tuple = None
while start < end:
_UpperCamelCase: int = ''''''.join(chars[start:end] )
if substr in self.vocab:
_UpperCamelCase: Tuple = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(_lowercase )
_UpperCamelCase: Dict = end
return sub_tokens
class __magic_name__ ( __a ):
"""simple docstring"""
lowerCAmelCase : str = VOCAB_FILES_NAMES
lowerCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : Union[str, Any] = ['''input_ids''', '''attention_mask''']
lowerCAmelCase : List[str] = False
def __init__( self : List[Any] , _lowercase : Tuple , _lowercase : List[str]="<d>" , _lowercase : Any="</d>" , _lowercase : str="<s>" , _lowercase : Dict="</s>" , _lowercase : Optional[Any]="<pad>" , _lowercase : Optional[int]="<unk>" , _lowercase : Optional[Any]="</n>" , _lowercase : Any="</_>" , _lowercase : List[str]="left" , **_lowercase : str , ):
"""simple docstring"""
requires_backends(self , ['''jieba'''] )
super().__init__(
bod_token=_lowercase , eod_token=_lowercase , bos_token=_lowercase , eos_token=_lowercase , pad_token=_lowercase , unk_token=_lowercase , line_token=_lowercase , space_token=_lowercase , padding_side=_lowercase , **_lowercase , )
_UpperCamelCase: Tuple = bod_token
_UpperCamelCase: Tuple = eod_token
_UpperCamelCase: Tuple = load_vocab(_lowercase )
_UpperCamelCase: Any = self.encoder[space_token]
_UpperCamelCase: Optional[int] = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
_UpperCamelCase: List[Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _lowercase : x[1] ) )
_UpperCamelCase: Optional[Any] = {v: k for k, v in self.encoder.items()}
_UpperCamelCase: Any = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
return self.encoder[self.bod_token]
@property
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
return self.encoder[self.eod_token]
@property
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
return self.encoder["\n"]
@property
def lowerCAmelCase ( self : int ):
"""simple docstring"""
return len(self.encoder )
def lowerCAmelCase ( self : str ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase ( self : List[str] , _lowercase : List[str] ):
"""simple docstring"""
_UpperCamelCase: str = []
for x in jieba.cut(_lowercase , cut_all=_lowercase ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase ) )
return output_tokens
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : Tuple , **_lowercase : List[str] ):
"""simple docstring"""
_UpperCamelCase: str = [i for i in token_ids if i >= 0]
_UpperCamelCase: Any = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(_lowercase , **_lowercase )
def lowerCAmelCase ( self : Any , _lowercase : List[Any] ):
"""simple docstring"""
return token in self.encoder
def lowerCAmelCase ( self : Optional[int] , _lowercase : List[str] ):
"""simple docstring"""
return "".join(_lowercase )
def lowerCAmelCase ( self : Optional[Any] , _lowercase : Optional[Any] ):
"""simple docstring"""
return self.encoder.get(_lowercase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase ( self : Dict , _lowercase : List[str] ):
"""simple docstring"""
return self.decoder.get(_lowercase , self.unk_token )
def lowerCAmelCase ( self : List[str] , _lowercase : str , _lowercase : Optional[str] = None ):
"""simple docstring"""
if os.path.isdir(_lowercase ):
_UpperCamelCase: int = os.path.join(
_lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
_UpperCamelCase: List[Any] = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
_UpperCamelCase: Dict = 0
if " " in self.encoder:
_UpperCamelCase: Tuple = self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
_UpperCamelCase: Dict = self.encoder['''\n''']
del self.encoder["\n"]
_UpperCamelCase: List[str] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _lowercase : x[1] ) )
with open(_lowercase , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
''' Please check that the vocabulary is not corrupted!''' )
_UpperCamelCase: str = token_index
writer.write(token + '''\n''' )
index += 1
return (vocab_file,)
def lowerCAmelCase ( self : int , _lowercase : List[int] , _lowercase : List[int] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowerCAmelCase ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase ))
return [1] + ([0] * len(_lowercase ))
| 264
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'''FlaxBlenderbotForConditionalGeneration''',
'''FlaxBlenderbotModel''',
'''FlaxBlenderbotPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264
| 1
|
"""simple docstring"""
__snake_case : Optional[Any] = 8.314_462 # Unit - J mol-1 K-1
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 293
|
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 293
| 1
|
from __future__ import annotations
from typing import Any
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case ):
'''simple docstring'''
UpperCamelCase__ = num_of_nodes
UpperCamelCase__ = []
UpperCamelCase__ = {}
def snake_case__ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def snake_case__ ( self , snake_case ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def snake_case__ ( self , snake_case ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
UpperCamelCase__ = self.find_component(snake_case )
def snake_case__ ( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
UpperCamelCase__ = v_node
component_size[v_node] += component_size[u_node]
self.set_component(snake_case )
elif component_size[u_node] >= component_size[v_node]:
UpperCamelCase__ = self.find_component(snake_case )
component_size[u_node] += component_size[v_node]
self.set_component(snake_case )
def snake_case__ ( self ):
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__ = 0
UpperCamelCase__ = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
UpperCamelCase__ = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ = edge
UpperCamelCase__ = self.m_component[u]
UpperCamelCase__ = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
UpperCamelCase__ = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(snake_case , snake_case ):
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ = edge
UpperCamelCase__ = self.m_component[u]
UpperCamelCase__ = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(snake_case , snake_case , snake_case )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
UpperCamelCase__ = [-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase_( )-> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 185
|
from __future__ import annotations
import numpy as np
def UpperCamelCase_( _A :list[float] )-> Union[str, Any]:
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 185
| 1
|
import os
from distutils.util import strtobool
def snake_case__ ( lowercase , lowercase ):
for e in env_keys:
lowerCAmelCase_: Optional[Any] = int(os.environ.get(lowercase , -1 ) )
if val >= 0:
return val
return default
def snake_case__ ( lowercase , lowercase=False ):
lowerCAmelCase_: Dict = os.environ.get(lowercase , str(lowercase ) )
return strtobool(lowercase ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case__ ( lowercase , lowercase="no" ):
lowerCAmelCase_: Tuple = os.environ.get(lowercase , str(lowercase ) )
return value
| 613
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: torch.FloatTensor
class _lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , lowerCamelCase__ = 65_536 , lowerCamelCase__ = None , lowerCamelCase__ = 2 , lowerCamelCase__ = 2 , lowerCamelCase__ = 0 , lowerCamelCase__ = "fourier" , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = 0.0 , lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , lowerCamelCase__ = "UNetMidBlock1D" , lowerCamelCase__ = None , lowerCamelCase__ = (32, 32, 64) , lowerCamelCase__ = None , lowerCamelCase__ = 8 , lowerCamelCase__ = 1 , lowerCamelCase__ = False , ):
super().__init__()
lowerCAmelCase_: Optional[Any] = sample_size
# time
if time_embedding_type == "fourier":
lowerCAmelCase_: Dict = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=lowerCamelCase__ , log=lowerCamelCase__ , flip_sin_to_cos=lowerCamelCase__ )
lowerCAmelCase_: List[str] = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
lowerCAmelCase_: Tuple = Timesteps(
block_out_channels[0] , flip_sin_to_cos=lowerCamelCase__ , downscale_freq_shift=lowerCamelCase__ )
lowerCAmelCase_: Dict = block_out_channels[0]
if use_timestep_embedding:
lowerCAmelCase_: Tuple = block_out_channels[0] * 4
lowerCAmelCase_: Any = TimestepEmbedding(
in_channels=lowerCamelCase__ , time_embed_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ , out_dim=block_out_channels[0] , )
lowerCAmelCase_: str = nn.ModuleList([] )
lowerCAmelCase_: Dict = None
lowerCAmelCase_: Optional[Any] = nn.ModuleList([] )
lowerCAmelCase_: int = None
# down
lowerCAmelCase_: List[str] = in_channels
for i, down_block_type in enumerate(lowerCamelCase__ ):
lowerCAmelCase_: Optional[int] = output_channel
lowerCAmelCase_: Optional[Any] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
lowerCAmelCase_: List[str] = i == len(lowerCamelCase__ ) - 1
lowerCAmelCase_: List[str] = get_down_block(
lowerCamelCase__ , num_layers=lowerCamelCase__ , in_channels=lowerCamelCase__ , out_channels=lowerCamelCase__ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(lowerCamelCase__ )
# mid
lowerCAmelCase_: Optional[int] = get_mid_block(
lowerCamelCase__ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=lowerCamelCase__ , add_downsample=lowerCamelCase__ , )
# up
lowerCAmelCase_: Dict = list(reversed(lowerCamelCase__ ) )
lowerCAmelCase_: Any = reversed_block_out_channels[0]
if out_block_type is None:
lowerCAmelCase_: str = out_channels
else:
lowerCAmelCase_: Any = block_out_channels[0]
for i, up_block_type in enumerate(lowerCamelCase__ ):
lowerCAmelCase_: Dict = output_channel
lowerCAmelCase_: int = (
reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__ ) - 1 else final_upsample_channels
)
lowerCAmelCase_: Optional[int] = i == len(lowerCamelCase__ ) - 1
lowerCAmelCase_: Union[str, Any] = get_up_block(
lowerCamelCase__ , num_layers=lowerCamelCase__ , in_channels=lowerCamelCase__ , out_channels=lowerCamelCase__ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(lowerCamelCase__ )
lowerCAmelCase_: str = output_channel
# out
lowerCAmelCase_: List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
lowerCAmelCase_: int = get_out_block(
out_block_type=lowerCamelCase__ , num_groups_out=lowerCamelCase__ , embed_dim=block_out_channels[0] , out_channels=lowerCamelCase__ , act_fn=lowerCamelCase__ , fc_dim=block_out_channels[-1] // 4 , )
def _a ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , ):
lowerCAmelCase_: Any = timestep
if not torch.is_tensor(lowerCamelCase__ ):
lowerCAmelCase_: Optional[Any] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
lowerCAmelCase_: List[Any] = timesteps[None].to(sample.device )
lowerCAmelCase_: Union[str, Any] = self.time_proj(lowerCamelCase__ )
if self.config.use_timestep_embedding:
lowerCAmelCase_: Any = self.time_mlp(lowerCamelCase__ )
else:
lowerCAmelCase_: Any = timestep_embed[..., None]
lowerCAmelCase_: str = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
lowerCAmelCase_: Dict = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
lowerCAmelCase_: Dict = ()
for downsample_block in self.down_blocks:
lowerCAmelCase_ , lowerCAmelCase_: Optional[int] = downsample_block(hidden_states=lowerCamelCase__ , temb=lowerCamelCase__ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
lowerCAmelCase_: int = self.mid_block(lowerCamelCase__ , lowerCamelCase__ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
lowerCAmelCase_: Any = down_block_res_samples[-1:]
lowerCAmelCase_: str = down_block_res_samples[:-1]
lowerCAmelCase_: List[str] = upsample_block(lowerCamelCase__ , res_hidden_states_tuple=lowerCamelCase__ , temb=lowerCamelCase__ )
# 5. post-process
if self.out_block:
lowerCAmelCase_: Any = self.out_block(lowerCamelCase__ , lowerCamelCase__ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=lowerCamelCase__ )
| 613
| 1
|
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class a :
"""simple docstring"""
__lowerCAmelCase = field(
default=1_2_8 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class a :
"""simple docstring"""
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Train language if it is different from the evaluation language."""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__lowerCAmelCase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def UpperCamelCase__ ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__UpperCAmelCase: Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__UpperCAmelCase: int = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , _lowercase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__UpperCAmelCase: Dict = training_args.get_process_log_level()
logger.setLevel(_lowercase )
datasets.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.set_verbosity(_lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
__UpperCAmelCase: List[str] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__UpperCAmelCase: Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
__UpperCAmelCase: Any = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
__UpperCAmelCase: Tuple = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase: Tuple = train_dataset.features["""label"""].names
if training_args.do_eval:
__UpperCAmelCase: Optional[Any] = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase: List[str] = eval_dataset.features["""label"""].names
if training_args.do_predict:
__UpperCAmelCase: int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase: List[Any] = predict_dataset.features["""label"""].names
# Labels
__UpperCAmelCase: str = len(_lowercase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__UpperCAmelCase: int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_lowercase , idalabel={str(_lowercase ): label for i, label in enumerate(_lowercase )} , labelaid={label: i for i, label in enumerate(_lowercase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase: str = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase: Dict = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
__UpperCAmelCase: str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__UpperCAmelCase: Optional[Any] = False
def preprocess_function(_lowercase : List[Any] ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=_lowercase , max_length=data_args.max_seq_length , truncation=_lowercase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
__UpperCAmelCase: List[Any] = min(len(_lowercase ) , data_args.max_train_samples )
__UpperCAmelCase: Optional[Any] = train_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
__UpperCAmelCase: Any = train_dataset.map(
_lowercase , batched=_lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(_lowercase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__UpperCAmelCase: str = min(len(_lowercase ) , data_args.max_eval_samples )
__UpperCAmelCase: int = eval_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
__UpperCAmelCase: List[str] = eval_dataset.map(
_lowercase , batched=_lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
__UpperCAmelCase: Any = min(len(_lowercase ) , data_args.max_predict_samples )
__UpperCAmelCase: int = predict_dataset.select(range(_lowercase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
__UpperCAmelCase: List[str] = predict_dataset.map(
_lowercase , batched=_lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
__UpperCAmelCase: Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_lowercase : EvalPrediction ):
__UpperCAmelCase: Tuple = p.predictions[0] if isinstance(p.predictions , _lowercase ) else p.predictions
__UpperCAmelCase: Optional[int] = np.argmax(_lowercase , axis=1 )
return metric.compute(predictions=_lowercase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__UpperCAmelCase: Optional[Any] = default_data_collator
elif training_args.fpaa:
__UpperCAmelCase: Optional[int] = DataCollatorWithPadding(_lowercase , pad_to_multiple_of=8 )
else:
__UpperCAmelCase: str = None
# Initialize our Trainer
__UpperCAmelCase: List[Any] = Trainer(
model=_lowercase , args=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_lowercase , tokenizer=_lowercase , data_collator=_lowercase , )
# Training
if training_args.do_train:
__UpperCAmelCase: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
__UpperCAmelCase: Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__UpperCAmelCase: Union[str, Any] = last_checkpoint
__UpperCAmelCase: Any = trainer.train(resume_from_checkpoint=_lowercase )
__UpperCAmelCase: List[Any] = train_result.metrics
__UpperCAmelCase: Any = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
__UpperCAmelCase: int = min(_lowercase , len(_lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , _lowercase )
trainer.save_metrics("""train""" , _lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__UpperCAmelCase: Optional[Any] = trainer.evaluate(eval_dataset=_lowercase )
__UpperCAmelCase: Any = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowercase )
__UpperCAmelCase: str = min(_lowercase , len(_lowercase ) )
trainer.log_metrics("""eval""" , _lowercase )
trainer.save_metrics("""eval""" , _lowercase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
__UpperCAmelCase: int = trainer.predict(_lowercase , metric_key_prefix="""predict""" )
__UpperCAmelCase: Optional[int] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_lowercase )
)
__UpperCAmelCase: List[str] = min(_lowercase , len(_lowercase ) )
trainer.log_metrics("""predict""" , _lowercase )
trainer.save_metrics("""predict""" , _lowercase )
__UpperCAmelCase: Union[str, Any] = np.argmax(_lowercase , axis=1 )
__UpperCAmelCase: int = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(_lowercase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(_lowercase ):
__UpperCAmelCase: Optional[Any] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 710
|
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=512 , snake_case_=16 , snake_case_=2 , snake_case_=0.0_2 , snake_case_=4 , ):
'''simple docstring'''
__UpperCAmelCase: Optional[Any] = parent
__UpperCAmelCase: List[str] = batch_size
__UpperCAmelCase: Optional[int] = seq_length
__UpperCAmelCase: Optional[Any] = is_training
__UpperCAmelCase: Any = use_attention_mask
__UpperCAmelCase: List[str] = use_token_type_ids
__UpperCAmelCase: List[str] = use_labels
__UpperCAmelCase: List[str] = vocab_size
__UpperCAmelCase: Optional[Any] = hidden_size
__UpperCAmelCase: List[Any] = num_hidden_layers
__UpperCAmelCase: List[Any] = num_attention_heads
__UpperCAmelCase: Tuple = intermediate_size
__UpperCAmelCase: Dict = hidden_act
__UpperCAmelCase: Dict = hidden_dropout_prob
__UpperCAmelCase: Tuple = attention_probs_dropout_prob
__UpperCAmelCase: List[str] = max_position_embeddings
__UpperCAmelCase: List[Any] = type_vocab_size
__UpperCAmelCase: List[Any] = type_sequence_label_size
__UpperCAmelCase: List[str] = initializer_range
__UpperCAmelCase: List[Any] = num_choices
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase: str = None
if self.use_attention_mask:
__UpperCAmelCase: Any = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase: List[Any] = None
if self.use_token_type_ids:
__UpperCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase: List[Any] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Tuple = self.prepare_config_and_inputs()
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase: List[str] = config_and_inputs
__UpperCAmelCase: List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class a ( __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def lowercase_ ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__UpperCAmelCase: str = model_class_name.from_pretrained("""albert-base-v2""" )
__UpperCAmelCase: int = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case_ )
@require_flax
class a ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: str = FlaxAlbertModel.from_pretrained("""albert-base-v2""" )
__UpperCAmelCase: Dict = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__UpperCAmelCase: int = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__UpperCAmelCase: Optional[int] = model(snake_case_ , attention_mask=snake_case_ )[0]
__UpperCAmelCase: Optional[int] = (1, 11, 768)
self.assertEqual(output.shape , snake_case_ )
__UpperCAmelCase: str = np.array(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , snake_case_ , atol=1e-4 ) )
| 466
| 0
|
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowerCamelCase_ = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] ) -> str:
_SCREAMING_SNAKE_CASE = defaultdict(__A )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(__A )
_SCREAMING_SNAKE_CASE = new_doc_list
_SCREAMING_SNAKE_CASE = [key for key, value in counts.items() if value > 1]
_SCREAMING_SNAKE_CASE = []
for duplicate_key in duplicates:
_SCREAMING_SNAKE_CASE = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(__A ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
"others." )
# Only add this once
new_doc.append({"local": duplicate_key, "title": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
_SCREAMING_SNAKE_CASE = sorted(__A , key=lambda __A : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__A ) > 1:
raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )
overview_doc.extend(__A )
# Sort
return overview_doc
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any]=False ) -> List[str]:
with open(__A , encoding="utf-8" ) as f:
_SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
_SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_SCREAMING_SNAKE_CASE = content[api_idx]["sections"]
# Then to the model doc
_SCREAMING_SNAKE_CASE = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_SCREAMING_SNAKE_CASE = api_doc[scheduler_idx]["sections"]
_SCREAMING_SNAKE_CASE = clean_doc_toc(__A )
_SCREAMING_SNAKE_CASE = False
if new_scheduler_doc != scheduler_doc:
_SCREAMING_SNAKE_CASE = True
if overwrite:
_SCREAMING_SNAKE_CASE = new_scheduler_doc
if diff:
if overwrite:
_SCREAMING_SNAKE_CASE = api_doc
with open(__A , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(__A , allow_unicode=__A ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def SCREAMING_SNAKE_CASE_ ( __A : List[str]=False ) -> List[Any]:
with open(__A , encoding="utf-8" ) as f:
_SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
_SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_SCREAMING_SNAKE_CASE = content[api_idx]["sections"]
# Then to the model doc
_SCREAMING_SNAKE_CASE = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = api_doc[pipeline_idx]["sections"]
_SCREAMING_SNAKE_CASE = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_SCREAMING_SNAKE_CASE = pipeline_doc["section"]
_SCREAMING_SNAKE_CASE = clean_doc_toc(__A )
if overwrite:
_SCREAMING_SNAKE_CASE = new_sub_pipeline_doc
new_pipeline_docs.append(__A )
# sort overall pipeline doc
_SCREAMING_SNAKE_CASE = clean_doc_toc(__A )
if new_pipeline_docs != pipeline_docs:
_SCREAMING_SNAKE_CASE = True
if overwrite:
_SCREAMING_SNAKE_CASE = new_pipeline_docs
if diff:
if overwrite:
_SCREAMING_SNAKE_CASE = api_doc
with open(__A , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(__A , allow_unicode=__A ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCamelCase_ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 418
|
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
snake_case_ = 'Wav2Vec2FeatureExtractor'
snake_case_ = 'AutoTokenizer'
def __init__( self : Tuple , a_ : Any , a_ : str ):
"""simple docstring"""
super().__init__(a_ , a_ )
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
@classmethod
def _UpperCamelCase ( cls : List[str] , a_ : Optional[Any] , **a_ : int ):
"""simple docstring"""
try:
return super().from_pretrained(a_ , **a_ )
except OSError:
warnings.warn(
F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
""" include a `tokenizer_class` attribute is deprecated and will be """
"""removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
""" attribute to either your `config.json` or `tokenizer_config.json` """
"""file to suppress this warning: """ , a_ , )
lowerCamelCase__ = WavaVecaFeatureExtractor.from_pretrained(a_ , **a_ )
lowerCamelCase__ = WavaVecaCTCTokenizer.from_pretrained(a_ , **a_ )
return cls(feature_extractor=a_ , tokenizer=a_ )
def __call__( self : List[str] , *a_ : int , **a_ : str ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*a_ , **a_ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
lowerCamelCase__ = kwargs.pop("""raw_speech""" )
else:
lowerCamelCase__ = kwargs.pop("""audio""" , a_ )
lowerCamelCase__ = kwargs.pop("""sampling_rate""" , a_ )
lowerCamelCase__ = kwargs.pop("""text""" , a_ )
if len(a_ ) > 0:
lowerCamelCase__ = args[0]
lowerCamelCase__ = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
lowerCamelCase__ = self.feature_extractor(a_ , *a_ , sampling_rate=a_ , **a_ )
if text is not None:
lowerCamelCase__ = self.tokenizer(a_ , **a_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ = encodings["""input_ids"""]
return inputs
def _UpperCamelCase ( self : int , *a_ : List[Any] , **a_ : int ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*a_ , **a_ )
lowerCamelCase__ = kwargs.pop("""input_features""" , a_ )
lowerCamelCase__ = kwargs.pop("""labels""" , a_ )
if len(a_ ) > 0:
lowerCamelCase__ = args[0]
lowerCamelCase__ = args[1:]
if input_features is not None:
lowerCamelCase__ = self.feature_extractor.pad(a_ , *a_ , **a_ )
if labels is not None:
lowerCamelCase__ = self.tokenizer.pad(a_ , **a_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
lowerCamelCase__ = labels["""input_ids"""]
return input_features
def _UpperCamelCase ( self : str , *a_ : Tuple , **a_ : Dict ):
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def _UpperCamelCase ( self : Union[str, Any] , *a_ : Dict , **a_ : str ):
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@contextmanager
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
lowerCamelCase__ = True
lowerCamelCase__ = self.tokenizer
yield
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
| 165
| 0
|
'''simple docstring'''
import requests
a : str = """YOUR API KEY"""
def __lowerCamelCase ( _lowercase , _lowercase = giphy_api_key ) -> list:
UpperCAmelCase : Union[str, Any] = """+""".join(query.split() )
UpperCAmelCase : List[Any] = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
UpperCAmelCase : List[Any] = requests.get(_lowercase ).json()["""data"""]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 672
|
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
UpperCAmelCase : str = range(3 , int(math.sqrt(_lowercase ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def __lowerCamelCase ( _lowercase , _lowercase=1 , **_lowercase ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = factor * value
UpperCAmelCase : List[Any] = value
while not is_prime(_lowercase ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_lowercase )
return value
| 672
| 1
|
'''simple docstring'''
import os
_SCREAMING_SNAKE_CASE : Optional[Any] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : List[Any] = 0
__magic_name__ : Dict = 0
while index < len(UpperCamelCase__ ) - 1:
__magic_name__ : Optional[Any] = SYMBOLS[numerals[index]]
__magic_name__ : List[Any] = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : Optional[int] = ""
__magic_name__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
__magic_name__ : Union[str, Any] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
__magic_name__ : Tuple = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _UpperCamelCase ( UpperCamelCase__ = "/p089_roman.txt" ):
"""simple docstring"""
__magic_name__ : Dict = 0
with open(os.path.dirname(UpperCamelCase__ ) + roman_numerals_filename ) as filea:
__magic_name__ : Any = filea.readlines()
for line in lines:
__magic_name__ : Optional[int] = line.strip()
__magic_name__ : List[str] = parse_roman_numerals(UpperCamelCase__ )
__magic_name__ : str = generate_roman_numerals(UpperCamelCase__ )
savings += len(UpperCamelCase__ ) - len(UpperCamelCase__ )
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 436
|
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 436
| 1
|
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ = "cpu" , lowerCamelCase__ = "openai/clip-vit-large-patch14" ):
_lowerCamelCase = device
_lowerCamelCase = CLIPTokenizerFast.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
_lowerCamelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
_lowerCamelCase = torchvision.transforms.Normalize(self.image_mean , self.image_std )
_lowerCamelCase = torchvision.transforms.Resize(2_2_4 )
_lowerCamelCase = torchvision.transforms.CenterCrop(2_2_4 )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = self.resize(lowerCamelCase__ )
_lowerCamelCase = self.center_crop(lowerCamelCase__ )
_lowerCamelCase = self.normalize(lowerCamelCase__ )
return images
def __call__( self , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ ):
_lowerCamelCase = self.tokenizer(text=lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.preprocess_img(lowerCamelCase__ )
_lowerCamelCase = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class lowerCamelCase_( nn.Module ):
'''simple docstring'''
def __init__( self , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_1 , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__="image" , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=False , ):
super().__init__()
_lowerCamelCase = None
_lowerCamelCase = device if device else get_device()
if vqgan:
_lowerCamelCase = vqgan
else:
_lowerCamelCase = load_vqgan(self.device , conf_path=lowerCamelCase__ , ckpt_path=lowerCamelCase__ )
self.vqgan.eval()
if clip:
_lowerCamelCase = clip
else:
_lowerCamelCase = CLIPModel.from_pretrained('''openai/clip-vit-base-patch32''' )
self.clip.to(self.device )
_lowerCamelCase = ProcessorGradientFlow(device=self.device )
_lowerCamelCase = iterations
_lowerCamelCase = lr
_lowerCamelCase = log
_lowerCamelCase = make_grid
_lowerCamelCase = return_val
_lowerCamelCase = quantize
_lowerCamelCase = self.vqgan.decoder.z_shape
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=5 , lowerCamelCase__=True ):
_lowerCamelCase = []
if output_path is None:
_lowerCamelCase = '''./animation.gif'''
if input_path is None:
_lowerCamelCase = self.save_path
_lowerCamelCase = sorted(glob(input_path + '''/*''' ) )
if not len(lowerCamelCase__ ):
raise ValueError(
'''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
''' function?)''' )
if len(lowerCamelCase__ ) == 1:
print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)''' )
_lowerCamelCase = total_duration / len(lowerCamelCase__ )
_lowerCamelCase = [frame_duration] * len(lowerCamelCase__ )
if extend_frames:
_lowerCamelCase = 1.5
_lowerCamelCase = 3
for file_name in paths:
if file_name.endswith('''.png''' ):
images.append(imageio.imread(lowerCamelCase__ ) )
imageio.mimsave(lowerCamelCase__ , lowerCamelCase__ , duration=lowerCamelCase__ )
print(F"""gif saved to {output_path}""" )
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None ):
if not (path or img):
raise ValueError('''Input either path or tensor''' )
if img is not None:
raise NotImplementedError
_lowerCamelCase = preprocess(Image.open(lowerCamelCase__ ) , target_image_size=2_5_6 ).to(self.device )
_lowerCamelCase = preprocess_vqgan(lowerCamelCase__ )
_lowerCamelCase , *_lowerCamelCase = self.vqgan.encode(lowerCamelCase__ )
return z
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = self.latent.detach().requires_grad_()
_lowerCamelCase = base_latent + transform_vector
if self.quantize:
_lowerCamelCase , *_lowerCamelCase = self.vqgan.quantize(lowerCamelCase__ )
else:
_lowerCamelCase = trans_latent
return self.vqgan.decode(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
_lowerCamelCase = self.clip_preprocessor(text=lowerCamelCase__ , images=lowerCamelCase__ , return_tensors='''pt''' , padding=lowerCamelCase__ )
_lowerCamelCase = self.clip(**lowerCamelCase__ )
_lowerCamelCase = clip_outputs.logits_per_image
if weights is not None:
_lowerCamelCase = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self._get_clip_similarity(pos_prompts['''prompts'''] , lowerCamelCase__ , weights=(1 / pos_prompts['''weights''']) )
if neg_prompts:
_lowerCamelCase = self._get_clip_similarity(neg_prompts['''prompts'''] , lowerCamelCase__ , weights=neg_prompts['''weights'''] )
else:
_lowerCamelCase = torch.tensor([1] , device=self.device )
_lowerCamelCase = -torch.log(lowerCamelCase__ ) + torch.log(lowerCamelCase__ )
return loss
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.randn_like(self.latent , requires_grad=lowerCamelCase__ , device=self.device )
_lowerCamelCase = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
_lowerCamelCase = self._add_vector(lowerCamelCase__ )
_lowerCamelCase = loop_post_process(lowerCamelCase__ )
_lowerCamelCase = self._get_CLIP_loss(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
print('''CLIP loss''' , lowerCamelCase__ )
if self.log:
wandb.log({'''CLIP Loss''': clip_loss} )
clip_loss.backward(retain_graph=lowerCamelCase__ )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
wandb.init(reinit=lowerCamelCase__ , project='''face-editor''' )
wandb.config.update({'''Positive Prompts''': positive_prompts} )
wandb.config.update({'''Negative Prompts''': negative_prompts} )
wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations} )
if image_path:
_lowerCamelCase = Image.open(lowerCamelCase__ )
_lowerCamelCase = image.resize((2_5_6, 2_5_6) )
wandb.log('''Original Image''' , wandb.Image(lowerCamelCase__ ) )
def snake_case__ ( self , lowerCamelCase__ ):
if not prompts:
return []
_lowerCamelCase = []
_lowerCamelCase = []
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = [prompt.strip() for prompt in prompts.split('''|''' )]
for prompt in prompts:
if isinstance(lowerCamelCase__ , (tuple, list) ):
_lowerCamelCase = prompt[0]
_lowerCamelCase = float(prompt[1] )
elif ":" in prompt:
_lowerCamelCase , _lowerCamelCase = prompt.split(''':''' )
_lowerCamelCase = float(lowerCamelCase__ )
else:
_lowerCamelCase = prompt
_lowerCamelCase = 1.0
processed_prompts.append(lowerCamelCase__ )
weights.append(lowerCamelCase__ )
return {
"prompts": processed_prompts,
"weights": torch.tensor(lowerCamelCase__ , device=self.device ),
}
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=None , ):
if image_path:
_lowerCamelCase = self._get_latent(lowerCamelCase__ )
else:
_lowerCamelCase = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCamelCase = self.process_prompts(lowerCamelCase__ )
_lowerCamelCase = self.process_prompts(lowerCamelCase__ )
if save_final and save_path is None:
_lowerCamelCase = os.path.join('''./outputs/''' , '''_'''.join(pos_prompts['''prompts'''] ) )
if not os.path.exists(lowerCamelCase__ ):
os.makedirs(lowerCamelCase__ )
else:
_lowerCamelCase = save_path + '''_''' + get_timestamp()
os.makedirs(lowerCamelCase__ )
_lowerCamelCase = save_path
_lowerCamelCase = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('''Original Image''' )
show_pil(custom_to_pil(lowerCamelCase__ ) )
_lowerCamelCase = loop_post_process(lowerCamelCase__ )
for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) ):
if show_intermediate:
show_pil(lowerCamelCase__ )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F"""iter_{iter:03d}.png""" ) )
if self.log:
wandb.log({'''Image''': wandb.Image(lowerCamelCase__ )} )
if show_final:
show_pil(lowerCamelCase__ )
if save_final:
transformed_img.save(os.path.join(self.save_path , F"""iter_{iter:03d}_final.png""" ) )
| 623
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__SCREAMING_SNAKE_CASE : Optional[Any] = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def lowerCAmelCase_( lowercase_ : List[Any] ) -> Optional[Any]:
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase_ )
def lowerCAmelCase_( lowercase_ : List[str] ) -> List[str]:
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_lowerCamelCase = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(lowercase_ , id=lowercase_ )
| 623
| 1
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase_ ( unittest.TestCase ):
def __a ( self : List[Any] ):
lowerCamelCase_ : int = 10
def __a ( self : Union[str, Any] ):
lowerCamelCase_ : Tuple = [1, 2, 3, 4]
lowerCamelCase_ : List[str] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(UpperCamelCase_ , self.block_size , 0 ) , UpperCamelCase_ )
def __a ( self : Any ):
lowerCamelCase_ : int = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
lowerCamelCase_ : Optional[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(UpperCamelCase_ , self.block_size , 0 ) , UpperCamelCase_ )
def __a ( self : Tuple ):
lowerCamelCase_ : Dict = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
lowerCamelCase_ : str = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(UpperCamelCase_ , self.block_size , 0 ) , UpperCamelCase_ )
def __a ( self : List[str] ):
lowerCamelCase_ : Dict = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
lowerCamelCase_ , lowerCamelCase_ : int = process_story(UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , [] )
def __a ( self : Optional[Any] ):
lowerCamelCase_ : Optional[Any] = ''
lowerCamelCase_ , lowerCamelCase_ : List[str] = process_story(UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , [] )
self.assertEqual(UpperCamelCase_ , [] )
def __a ( self : List[Any] ):
lowerCamelCase_ : Tuple = (
'It was the year of Our Lord one thousand seven hundred and '
'seventy-five\n\nSpiritual revelations were conceded to England '
'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
)
lowerCamelCase_ , lowerCamelCase_ : Tuple = process_story(UpperCamelCase_ )
lowerCamelCase_ : Optional[int] = [
'It was the year of Our Lord one thousand seven hundred and seventy-five.',
'Spiritual revelations were conceded to England at that favoured period, as at this.',
]
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
lowerCamelCase_ : Optional[Any] = ['It was the best of times.']
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
def __a ( self : str ):
lowerCamelCase_ : List[Any] = torch.tensor([1, 2, 3, 4] )
lowerCamelCase_ : List[Any] = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(UpperCamelCase_ , 0 ).numpy() , expected.numpy() )
def __a ( self : Any ):
lowerCamelCase_ : str = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
lowerCamelCase_ : int = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(UpperCamelCase_ , 23 ).numpy() , expected.numpy() )
def __a ( self : str ):
lowerCamelCase_ : List[str] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowerCamelCase_ : List[Any] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(UpperCamelCase_ , 1 ).numpy() , expected.numpy() )
def __a ( self : str ):
lowerCamelCase_ : Any = 1_01
lowerCamelCase_ : str = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]] )
lowerCamelCase_ : Tuple = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowerCamelCase_ : Union[str, Any] = compute_token_type_ids(UpperCamelCase_ , UpperCamelCase_ )
np.testing.assert_array_equal(UpperCamelCase_ , UpperCamelCase_ )
| 364
|
import argparse
import os
import re
import packaging.version
__a : Tuple = "examples/"
__a : Any = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
"doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__a : Tuple = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
__a : List[str] = "README.md"
def _SCREAMING_SNAKE_CASE ( __lowercase : str , __lowercase : List[str] , __lowercase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
with open(__lowercase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__A = f.read()
__A , __A = REPLACE_PATTERNS[pattern]
__A = replace.replace("""VERSION""" , __lowercase )
__A = re_pattern.sub(__lowercase , __lowercase )
with open(__lowercase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowercase )
def _SCREAMING_SNAKE_CASE ( __lowercase : List[str] ) -> Dict:
"""simple docstring"""
for folder, directories, fnames in os.walk(__lowercase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowercase , __lowercase ) , __lowercase , pattern="""examples""" )
def _SCREAMING_SNAKE_CASE ( __lowercase : Any , __lowercase : Optional[int]=False ) -> Dict:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowercase , __lowercase , __lowercase )
if not patch:
update_version_in_examples(__lowercase )
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
"""simple docstring"""
__A = """🤗 Transformers currently provides the following architectures"""
__A = """1. Want to contribute a new model?"""
with open(__lowercase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__A = f.readlines()
# Find the start of the list.
__A = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__A = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
__A = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowercase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowercase )
def _SCREAMING_SNAKE_CASE ( ) -> Dict:
"""simple docstring"""
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
__A = f.read()
__A = REPLACE_PATTERNS["""init"""][0].search(__lowercase ).groups()[0]
return packaging.version.parse(__lowercase )
def _SCREAMING_SNAKE_CASE ( __lowercase : str=False ) -> Optional[Any]:
"""simple docstring"""
__A = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
__A = default_version.base_version
elif patch:
__A = f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
else:
__A = f"{default_version.major}.{default_version.minor + 1}.0"
# Now let's ask nicely if that's the right one.
__A = input(f"Which version are you releasing? [{default_version}]" )
if len(__lowercase ) == 0:
__A = default_version
print(f"Updating version to {version}." )
global_version_update(__lowercase , patch=__lowercase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
"""simple docstring"""
__A = get_version()
__A = f"{current_version.major}.{current_version.minor + 1}.0.dev0"
__A = current_version.base_version
# Check with the user we got that right.
__A = input(f"Which version are we developing now? [{dev_version}]" )
if len(__lowercase ) == 0:
__A = dev_version
print(f"Updating version to {version}." )
global_version_update(__lowercase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__a : str = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
__a : Union[str, Any] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 637
| 0
|
def snake_case_ ( __lowercase = 5_0 ):
UpperCAmelCase_ : Optional[int] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F'{solution() = }')
| 721
|
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self : int , __snake_case : List[Any] , __snake_case : List[Any]=13 , __snake_case : str=7 , __snake_case : Union[str, Any]=True , __snake_case : List[Any]=True , __snake_case : str=True , __snake_case : Optional[int]=True , __snake_case : Optional[int]=True , __snake_case : List[str]=False , __snake_case : List[str]=False , __snake_case : Tuple=False , __snake_case : List[str]=2 , __snake_case : Optional[int]=99 , __snake_case : Tuple=0 , __snake_case : int=32 , __snake_case : Optional[int]=5 , __snake_case : str=4 , __snake_case : str=0.1 , __snake_case : Optional[int]=0.1 , __snake_case : List[str]=512 , __snake_case : Tuple=2 , __snake_case : List[Any]=0.02 , __snake_case : Any=2 , __snake_case : Optional[int]=4 , __snake_case : Optional[Any]="last" , __snake_case : Dict=True , __snake_case : Any=None , __snake_case : str=0 , ):
'''simple docstring'''
UpperCAmelCase_ : int = parent
UpperCAmelCase_ : Optional[Any] = batch_size
UpperCAmelCase_ : Union[str, Any] = seq_length
UpperCAmelCase_ : List[Any] = is_training
UpperCAmelCase_ : List[Any] = use_input_lengths
UpperCAmelCase_ : Dict = use_token_type_ids
UpperCAmelCase_ : Union[str, Any] = use_labels
UpperCAmelCase_ : int = gelu_activation
UpperCAmelCase_ : str = sinusoidal_embeddings
UpperCAmelCase_ : List[str] = causal
UpperCAmelCase_ : Tuple = asm
UpperCAmelCase_ : List[Any] = n_langs
UpperCAmelCase_ : Union[str, Any] = vocab_size
UpperCAmelCase_ : Any = n_special
UpperCAmelCase_ : Dict = hidden_size
UpperCAmelCase_ : Dict = num_hidden_layers
UpperCAmelCase_ : str = num_attention_heads
UpperCAmelCase_ : List[str] = hidden_dropout_prob
UpperCAmelCase_ : Any = attention_probs_dropout_prob
UpperCAmelCase_ : Tuple = max_position_embeddings
UpperCAmelCase_ : Union[str, Any] = type_sequence_label_size
UpperCAmelCase_ : Union[str, Any] = initializer_range
UpperCAmelCase_ : Tuple = num_labels
UpperCAmelCase_ : List[Any] = num_choices
UpperCAmelCase_ : Any = summary_type
UpperCAmelCase_ : Optional[int] = use_proj
UpperCAmelCase_ : List[str] = scope
UpperCAmelCase_ : List[str] = bos_token_id
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_ : Tuple = None
if self.use_input_lengths:
UpperCAmelCase_ : List[str] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCAmelCase_ : int = None
if self.use_token_type_ids:
UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCAmelCase_ : Optional[int] = None
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : str = None
if self.use_labels:
UpperCAmelCase_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase_ : List[Any] = ids_tensor([self.batch_size] , 2 ).float()
UpperCAmelCase_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase_ : List[Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def _lowerCamelCase ( self : Optional[Any] , __snake_case : int , __snake_case : int , __snake_case : List[Any] , __snake_case : List[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : Optional[int] , __snake_case : Union[str, Any] , __snake_case : Dict , ):
'''simple docstring'''
UpperCAmelCase_ : Any = XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : Optional[int] = model(__snake_case , lengths=__snake_case , langs=__snake_case )
UpperCAmelCase_ : Any = model(__snake_case , langs=__snake_case )
UpperCAmelCase_ : Any = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self : str , __snake_case : Dict , __snake_case : Any , __snake_case : Optional[int] , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : str , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : int , ):
'''simple docstring'''
UpperCAmelCase_ : Any = XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : Optional[Any] = model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self : Optional[int] , __snake_case : Tuple , __snake_case : Any , __snake_case : Tuple , __snake_case : List[str] , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : List[str] , ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : Optional[int] = model(__snake_case )
UpperCAmelCase_ : Any = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
UpperCAmelCase_ : Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCamelCase ( self : Any , __snake_case : int , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : str , __snake_case : Tuple , __snake_case : int , ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : Union[str, Any] = model(__snake_case )
UpperCAmelCase_ : List[str] = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
UpperCAmelCase_ : Optional[Any] = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((UpperCAmelCase_) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCAmelCase_ : Optional[int] = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((UpperCAmelCase_) , ) : str = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def _lowerCamelCase ( self : str , __snake_case : Dict , __snake_case : str , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Optional[int] , __snake_case : Dict , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : Any , ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : Union[str, Any] = model(__snake_case )
UpperCAmelCase_ : Optional[int] = model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowerCamelCase ( self : Any , __snake_case : Union[str, Any] , __snake_case : Any , __snake_case : List[str] , __snake_case : Optional[Any] , __snake_case : Tuple , __snake_case : str , __snake_case : Optional[int] , __snake_case : Tuple , __snake_case : int , ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = self.num_labels
UpperCAmelCase_ : Optional[int] = XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : List[str] = model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self : List[Any] , __snake_case : str , __snake_case : List[str] , __snake_case : Any , __snake_case : Tuple , __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : Dict , __snake_case : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase_ : int = self.num_choices
UpperCAmelCase_ : int = XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
UpperCAmelCase_ : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase_ : int = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase_ : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase_ : Any = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : Union[str, Any] = config_and_inputs
UpperCAmelCase_ : str = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
return config, inputs_dict
@require_torch
class lowerCAmelCase__( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
A_ : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ : List[str] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
A_ : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowerCamelCase ( self : str , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : Optional[int] , __snake_case : Union[str, Any] , __snake_case : Optional[Any] ):
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _lowerCamelCase ( self : Union[str, Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : str=False ):
'''simple docstring'''
UpperCAmelCase_ : int = super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCAmelCase_ : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
UpperCAmelCase_ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = XLMModelTester(self )
UpperCAmelCase_ : List[Any] = ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def _lowerCamelCase ( self : int ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def _lowerCamelCase ( self : int ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def _lowerCamelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def _lowerCamelCase ( self : str , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : List[Any] , __snake_case : str , __snake_case : Optional[int] , __snake_case : Union[str, Any]=False , __snake_case : Optional[Any]=1 ):
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
UpperCAmelCase_ : Dict = min_length + idx + 1
UpperCAmelCase_ : List[Any] = min_length + idx + 1
UpperCAmelCase_ : Optional[int] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def _lowerCamelCase ( self : List[Any] , __snake_case : List[str] , __snake_case : int , __snake_case : Optional[int] , __snake_case : Dict , __snake_case : Any , __snake_case : Optional[Any]=False , __snake_case : str=1 ):
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
UpperCAmelCase_ : str = min_length + idx + 1
UpperCAmelCase_ : int = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def _lowerCamelCase ( self : int ):
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Any = XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class lowerCAmelCase__( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''' )
model.to(__snake_case )
UpperCAmelCase_ : str = torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
UpperCAmelCase_ : Union[str, Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCAmelCase_ : Dict = model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 641
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
"""processing_mctct""": ["""MCTCTProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : str = [
"""MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MCTCTForCTC""",
"""MCTCTModel""",
"""MCTCTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
_lowerCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 438
|
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class lowerCAmelCase__ :
def __init__( self : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any]=1_3 , snake_case__ : Dict=7 , snake_case__ : Optional[int]=True , snake_case__ : Optional[int]=True , snake_case__ : int=False , snake_case__ : Optional[Any]=True , snake_case__ : Optional[int]=9_9 , snake_case__ : List[Any]=3_2 , snake_case__ : Optional[Any]=5 , snake_case__ : Union[str, Any]=4 , snake_case__ : List[Any]=3_7 , snake_case__ : List[str]="gelu" , snake_case__ : List[Any]=0.1 , snake_case__ : Tuple=0.1 , snake_case__ : Optional[Any]=5_1_2 , snake_case__ : Tuple=1_6 , snake_case__ : str=2 , snake_case__ : Optional[int]=0.02 , snake_case__ : int=3 , snake_case__ : Union[str, Any]=4 , snake_case__ : Optional[int]=None , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Dict = use_input_mask
UpperCAmelCase__ : List[Any] = use_token_type_ids
UpperCAmelCase__ : Union[str, Any] = use_labels
UpperCAmelCase__ : Union[str, Any] = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Union[str, Any] = num_hidden_layers
UpperCAmelCase__ : str = num_attention_heads
UpperCAmelCase__ : int = intermediate_size
UpperCAmelCase__ : int = hidden_act
UpperCAmelCase__ : str = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : List[Any] = max_position_embeddings
UpperCAmelCase__ : str = type_vocab_size
UpperCAmelCase__ : Union[str, Any] = type_sequence_label_size
UpperCAmelCase__ : str = initializer_range
UpperCAmelCase__ : Tuple = num_labels
UpperCAmelCase__ : Union[str, Any] = num_choices
UpperCAmelCase__ : Tuple = scope
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : Optional[int] = None
if self.use_input_mask:
UpperCAmelCase__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : Dict = None
if self.use_token_type_ids:
UpperCAmelCase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ : str = None
UpperCAmelCase__ : Optional[Any] = None
UpperCAmelCase__ : int = None
if self.use_labels:
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __a ( self : Tuple ):
'''simple docstring'''
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case__ , initializer_range=self.initializer_range , )
def __a ( self : Optional[int] , snake_case__ : Optional[int] , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : Tuple , snake_case__ : List[str] , snake_case__ : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = LlamaModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : List[str] = model(snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase__ : Any = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self : Any , snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : List[Any] , snake_case__ : Dict , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : str , snake_case__ : int , ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = True
UpperCAmelCase__ : str = LlamaModel(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Optional[Any] = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , )
UpperCAmelCase__ : str = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , )
UpperCAmelCase__ : List[str] = model(snake_case__ , attention_mask=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self : List[str] , snake_case__ : Dict , snake_case__ : Optional[int] , snake_case__ : int , snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : List[str] , snake_case__ : Union[str, Any] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = LlamaForCausalLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Optional[Any] = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : Dict , snake_case__ : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : str = True
UpperCAmelCase__ : Union[str, Any] = LlamaForCausalLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
# first forward pass
UpperCAmelCase__ : List[str] = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , use_cache=snake_case__ , )
UpperCAmelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase__ : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase__ : Optional[int] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCAmelCase__ : str = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase__ : List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCAmelCase__ : str = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , output_hidden_states=snake_case__ , )["hidden_states"][0]
UpperCAmelCase__ : str = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , past_key_values=snake_case__ , output_hidden_states=snake_case__ , )["hidden_states"][0]
# select random slice
UpperCAmelCase__ : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase__ : str = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase__ : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-3 ) )
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : int = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : Optional[Any] = config_and_inputs
UpperCAmelCase__ : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ =(LlamaForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ =(
{
'''feature-extraction''': LlamaModel,
'''text-classification''': LlamaForSequenceClassification,
'''text-generation''': LlamaForCausalLM,
'''zero-shot''': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ =False
SCREAMING_SNAKE_CASE_ =False
def __a ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = LlamaModelTester(self )
UpperCAmelCase__ : str = ConfigTester(self , config_class=snake_case__ , hidden_size=3_7 )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def __a ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase__ : List[Any] = type
self.model_tester.create_and_check_model(*snake_case__ )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : str = 3
UpperCAmelCase__ : Any = input_dict["input_ids"]
UpperCAmelCase__ : Any = input_ids.ne(1 ).to(snake_case__ )
UpperCAmelCase__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase__ : Optional[Any] = LlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Optional[int] = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[Any] = 3
UpperCAmelCase__ : Any = "single_label_classification"
UpperCAmelCase__ : Tuple = input_dict["input_ids"]
UpperCAmelCase__ : str = input_ids.ne(1 ).to(snake_case__ )
UpperCAmelCase__ : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase__ : Any = LlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : List[str] = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Dict = 3
UpperCAmelCase__ : Tuple = "multi_label_classification"
UpperCAmelCase__ : Any = input_dict["input_ids"]
UpperCAmelCase__ : str = input_ids.ne(1 ).to(snake_case__ )
UpperCAmelCase__ : Any = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCAmelCase__ : Dict = LlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : int = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("LLaMA buffers include complex numbers, which breaks this test" )
def __a ( self : str ):
'''simple docstring'''
pass
@parameterized.expand([("linear",), ("dynamic",)] )
def __a ( self : List[str] , snake_case__ : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[str] = ids_tensor([1, 1_0] , config.vocab_size )
UpperCAmelCase__ : int = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase__ : str = LlamaModel(snake_case__ )
original_model.to(snake_case__ )
original_model.eval()
UpperCAmelCase__ : List[Any] = original_model(snake_case__ ).last_hidden_state
UpperCAmelCase__ : Tuple = original_model(snake_case__ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase__ : str = {"type": scaling_type, "factor": 10.0}
UpperCAmelCase__ : Optional[int] = LlamaModel(snake_case__ )
scaled_model.to(snake_case__ )
scaled_model.eval()
UpperCAmelCase__ : Tuple = scaled_model(snake_case__ ).last_hidden_state
UpperCAmelCase__ : Union[str, Any] = scaled_model(snake_case__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
UpperCAmelCase__ : Any = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf" , device_map="auto" )
UpperCAmelCase__ : Any = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
UpperCAmelCase__ : Optional[Any] = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case__ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : Optional[int] = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , snake_case__ , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
UpperCAmelCase__ : Dict = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf" , device_map="auto" )
UpperCAmelCase__ : List[str] = model(torch.tensor(snake_case__ ) )
# Expected mean on dim = -1
UpperCAmelCase__ : str = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case__ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : str = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , snake_case__ , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
@slow
def __a ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
UpperCAmelCase__ : Optional[Any] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf" , device_map="auto" )
UpperCAmelCase__ : Dict = model(torch.tensor(snake_case__ ) )
# Expected mean on dim = -1
UpperCAmelCase__ : List[str] = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case__ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCAmelCase__ : str = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case__ , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test" )
@slow
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
UpperCAmelCase__ : Optional[int] = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf" , device_map="auto" )
UpperCAmelCase__ : List[Any] = model(torch.tensor(snake_case__ ) )
UpperCAmelCase__ : str = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case__ , atol=1e-2 , rtol=1e-2 )
# fmt: off
UpperCAmelCase__ : Any = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , snake_case__ , atol=1e-5 , rtol=1e-5 )
@unittest.skip("Model is curently gated" )
@slow
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
UpperCAmelCase__ : Any = "Simply put, the theory of relativity states that "
UpperCAmelCase__ : int = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf" )
UpperCAmelCase__ : List[Any] = tokenizer.encode(snake_case__ , return_tensors="pt" )
UpperCAmelCase__ : Union[str, Any] = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf" , device_map="sequential" , use_safetensors=snake_case__ )
# greedy generation outputs
UpperCAmelCase__ : Optional[int] = model.generate(snake_case__ , max_new_tokens=6_4 , top_p=snake_case__ , temperature=1 , do_sample=snake_case__ )
UpperCAmelCase__ : Tuple = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
| 438
| 1
|
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str]=None ) -> Tuple:
lowercase_ : Union[str, Any] = None
if token is not None:
lowercase_ : Optional[int] = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
lowercase_ : Optional[int] = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
lowercase_ : Any = requests.get(_lowerCamelCase , headers=_lowerCamelCase ).json()
lowercase_ : int = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
lowercase_ : Tuple = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(_lowerCamelCase ):
lowercase_ : List[str] = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCamelCase ).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 lowerCamelCase ( UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple=None ) -> Tuple:
lowercase_ : str = None
if token is not None:
lowercase_ : int = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
lowercase_ : int = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
lowercase_ : int = requests.get(_lowerCamelCase , headers=_lowerCamelCase ).json()
lowercase_ : Union[str, Any] = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
lowercase_ : Optional[int] = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(_lowerCamelCase ):
lowercase_ : Any = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCamelCase ).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 lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Tuple ) -> Optional[int]:
lowercase_ : Union[str, Any] = None
if token is not None:
lowercase_ : Union[str, Any] = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
lowercase_ : Union[str, Any] = requests.get(_lowerCamelCase , headers=_lowerCamelCase , allow_redirects=_lowerCamelCase )
lowercase_ : List[str] = result.headers["""Location"""]
lowercase_ : Optional[int] = requests.get(_lowerCamelCase , allow_redirects=_lowerCamelCase )
lowercase_ : List[Any] = os.path.join(_lowerCamelCase , F'''{artifact_name}.zip''' )
with open(_lowerCamelCase , """wb""" ) as fp:
fp.write(response.content )
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : int=None ) -> int:
lowercase_ : List[Any] = []
lowercase_ : Optional[Any] = []
lowercase_ : Optional[int] = None
with zipfile.ZipFile(_lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(_lowerCamelCase ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_lowerCamelCase ) as f:
for line in f:
lowercase_ : Optional[Any] = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
lowercase_ : List[str] = line[: line.index(""": """ )]
lowercase_ : Any = 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
lowercase_ : List[Any] = line[len("""FAILED """ ) :]
failed_tests.append(_lowerCamelCase )
elif filename == "job_name.txt":
lowercase_ : Union[str, Any] = line
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError(
F'''`errors` and `failed_tests` should have the same number of elements. Got {len(_lowerCamelCase )} for `errors` '''
F'''and {len(_lowerCamelCase )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
""" problem.""" )
lowercase_ : List[Any] = None
if job_name and job_links:
lowercase_ : int = job_links.get(_lowerCamelCase , _lowerCamelCase )
# A list with elements of the form (line of error, error, failed test)
lowercase_ : Optional[Any] = [x + [y] + [job_link] for x, y in zip(_lowerCamelCase , _lowerCamelCase )]
return result
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any]=None ) -> Tuple:
lowercase_ : int = []
lowercase_ : Dict = [os.path.join(_lowerCamelCase , _lowerCamelCase ) for p in os.listdir(_lowerCamelCase ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_lowerCamelCase , job_links=_lowerCamelCase ) )
return errors
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int=None ) -> Optional[Any]:
lowercase_ : Optional[int] = Counter()
counter.update([x[1] for x in logs] )
lowercase_ : Dict = counter.most_common()
lowercase_ : Any = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
lowercase_ : List[str] = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
lowercase_ : List[Any] = dict(sorted(r.items() , key=lambda UpperCAmelCase__ : item[1]["count"] , reverse=_lowerCamelCase ) )
return r
def lowerCamelCase ( UpperCAmelCase__ : Dict ) -> Any:
lowercase_ : List[Any] = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
lowercase_ : Optional[int] = test.split("""/""" )[2]
else:
lowercase_ : str = None
return test
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int=None ) -> List[Any]:
lowercase_ : Union[str, Any] = [(x[0], x[1], get_model(x[2] )) for x in logs]
lowercase_ : List[Any] = [x for x in logs if x[2] is not None]
lowercase_ : Optional[Any] = {x[2] for x in logs}
lowercase_ : List[str] = {}
for test in tests:
lowercase_ : int = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
lowercase_ : str = counter.most_common()
lowercase_ : int = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
lowercase_ : Optional[int] = sum(error_counts.values() )
if n_errors > 0:
lowercase_ : Optional[Any] = {"""count""": n_errors, """errors""": error_counts}
lowercase_ : Tuple = dict(sorted(r.items() , key=lambda UpperCAmelCase__ : item[1]["count"] , reverse=_lowerCamelCase ) )
return r
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Union[str, Any]:
lowercase_ : Optional[int] = """| no. | error | status |"""
lowercase_ : Optional[Any] = """|-:|:-|:-|"""
lowercase_ : int = [header, sep]
for error in reduced_by_error:
lowercase_ : Optional[int] = reduced_by_error[error]["""count"""]
lowercase_ : Union[str, Any] = F'''| {count} | {error[:100]} | |'''
lines.append(_lowerCamelCase )
return "\n".join(_lowerCamelCase )
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] ) -> str:
lowercase_ : Optional[Any] = """| model | no. of errors | major error | count |"""
lowercase_ : Optional[Any] = """|-:|-:|-:|-:|"""
lowercase_ : Optional[int] = [header, sep]
for model in reduced_by_model:
lowercase_ : List[str] = reduced_by_model[model]["""count"""]
lowercase_ : str = list(reduced_by_model[model]["""errors"""].items() )[0]
lowercase_ : Optional[Any] = F'''| {model} | {count} | {error[:60]} | {_count} |'''
lines.append(_lowerCamelCase )
return "\n".join(_lowerCamelCase )
if __name__ == "__main__":
_lowercase : str = 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.")
_lowercase : Union[str, Any] = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
_lowercase : int = get_job_links(args.workflow_run_id, token=args.token)
_lowercase : List[Any] = {}
# 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:
_lowercase : Union[str, Any] = k.find(" / ")
_lowercase : int = k[index + len(" / ") :]
_lowercase : Optional[int] = 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)
_lowercase : Optional[Any] = 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)
_lowercase : Union[str, Any] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
_lowercase : List[str] = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
_lowercase : Dict = 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)
_lowercase : Optional[int] = reduce_by_error(errors)
_lowercase : Union[str, Any] = reduce_by_model(errors)
_lowercase : str = make_github_table(reduced_by_error)
_lowercase : Union[str, Any] = 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)
| 718
|
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int | None = None , UpperCAmelCase__ : int | None = None ) -> None:
if start is None:
lowercase_ : Any = 0
if end is None:
lowercase_ : List[Any] = len(UpperCAmelCase__ ) - 1
if start >= end:
return
lowercase_ : Optional[int] = (start + end) // 2
slowsort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
slowsort(UpperCAmelCase__ , mid + 1 , UpperCAmelCase__ )
if sequence[end] < sequence[mid]:
lowercase_ , lowercase_ : Dict = sequence[mid], sequence[end]
slowsort(UpperCAmelCase__ , UpperCAmelCase__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 30
| 0
|
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE__ : Any = 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = 3
class snake_case ( UpperCamelCase_ ):
pass
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
for shard in shards:
for i in range(lowercase__ ):
yield {"i": i, "shard": shard}
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = int(os.environ['RANK'] )
SCREAMING_SNAKE_CASE__ : List[Any] = int(os.environ['WORLD_SIZE'] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ArgumentParser()
parser.add_argument('--streaming' , type=lowercase__ )
parser.add_argument('--local_rank' , type=lowercase__ )
parser.add_argument('--num_workers' , type=lowercase__ , default=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : List[str] = args.streaming
SCREAMING_SNAKE_CASE__ : int = args.num_workers
SCREAMING_SNAKE_CASE__ : Optional[int] = {'shards': [f'''shard_{shard_idx}''' for shard_idx in range(lowercase__ )]}
SCREAMING_SNAKE_CASE__ : Any = IterableDataset.from_generator(lowercase__ , gen_kwargs=lowercase__ )
if not streaming:
SCREAMING_SNAKE_CASE__ : int = Dataset.from_list(list(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = split_dataset_by_node(lowercase__ , rank=lowercase__ , world_size=lowercase__ )
SCREAMING_SNAKE_CASE__ : str = torch.utils.data.DataLoader(lowercase__ , num_workers=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = NUM_SHARDS * NUM_ITEMS_PER_SHARD
SCREAMING_SNAKE_CASE__ : Tuple = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
SCREAMING_SNAKE_CASE__ : List[str] = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 85
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class A ( __UpperCAmelCase ):
lowerCamelCase : Union[str, Any] = """ctrl"""
lowerCamelCase : Optional[int] = ["""past_key_values"""]
lowerCamelCase : Optional[int] = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowerCamelCase__=246_534 , lowerCamelCase__=256 , lowerCamelCase__=1_280 , lowerCamelCase__=8_192 , lowerCamelCase__=48 , lowerCamelCase__=16 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1e-6 , lowerCamelCase__=0.02 , lowerCamelCase__=True , **lowerCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ = vocab_size
lowercase__ = n_positions
lowercase__ = n_embd
lowercase__ = n_layer
lowercase__ = n_head
lowercase__ = dff
lowercase__ = resid_pdrop
lowercase__ = embd_pdrop
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
super().__init__(**lowerCamelCase__ )
| 325
| 0
|
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase = " " ):
'''simple docstring'''
_lowerCAmelCase : Dict = []
_lowerCAmelCase : List[Any] = 0
for index, char in enumerate(_lowerCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
_lowerCAmelCase : str = index + 1
elif index + 1 == len(_lowerCamelCase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 16
|
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( a__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (("num_inference_steps", 25),)
def __lowerCamelCase ( self ,**_A ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = {
'num_train_timesteps': 1000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'solver_order': 2,
'solver_type': 'bh2',
}
config.update(**_A )
return config
def __lowerCamelCase ( self ,_A=0 ,**_A ):
'''simple docstring'''
_lowerCAmelCase : Tuple = dict(self.forward_default_kwargs )
_lowerCAmelCase : int = kwargs.pop('num_inference_steps' ,_A )
_lowerCAmelCase : Optional[Any] = self.dummy_sample
_lowerCAmelCase : Union[str, Any] = 0.1 * sample
_lowerCAmelCase : Tuple = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : Optional[int] = self.get_scheduler_config(**_A )
_lowerCAmelCase : Union[str, Any] = scheduler_class(**_A )
scheduler.set_timesteps(_A )
# copy over dummy past residuals
_lowerCAmelCase : Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_A )
_lowerCAmelCase : Union[str, Any] = scheduler_class.from_pretrained(_A )
new_scheduler.set_timesteps(_A )
# copy over dummy past residuals
_lowerCAmelCase : Union[str, Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase, _lowerCAmelCase : str = sample, sample
for t in range(_A ,time_step + scheduler.config.solver_order + 1 ):
_lowerCAmelCase : Dict = scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
_lowerCAmelCase : Union[str, Any] = new_scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __lowerCamelCase ( self ,_A=0 ,**_A ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = dict(self.forward_default_kwargs )
_lowerCAmelCase : List[str] = kwargs.pop('num_inference_steps' ,_A )
_lowerCAmelCase : Union[str, Any] = self.dummy_sample
_lowerCAmelCase : Dict = 0.1 * sample
_lowerCAmelCase : str = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : Any = self.get_scheduler_config()
_lowerCAmelCase : Union[str, Any] = scheduler_class(**_A )
scheduler.set_timesteps(_A )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCAmelCase : List[str] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_A )
_lowerCAmelCase : int = scheduler_class.from_pretrained(_A )
# copy over dummy past residuals
new_scheduler.set_timesteps(_A )
# copy over dummy past residual (must be after setting timesteps)
_lowerCAmelCase : str = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase : Optional[int] = scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
_lowerCAmelCase : Union[str, Any] = new_scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __lowerCamelCase ( self ,_A=None ,**_A ):
'''simple docstring'''
if scheduler is None:
_lowerCAmelCase : int = self.scheduler_classes[0]
_lowerCAmelCase : List[str] = self.get_scheduler_config(**_A )
_lowerCAmelCase : Union[str, Any] = scheduler_class(**_A )
_lowerCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_lowerCAmelCase : Dict = self.get_scheduler_config(**_A )
_lowerCAmelCase : int = scheduler_class(**_A )
_lowerCAmelCase : List[str] = 10
_lowerCAmelCase : str = self.dummy_model()
_lowerCAmelCase : str = self.dummy_sample_deter
scheduler.set_timesteps(_A )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase : Any = model(_A ,_A )
_lowerCAmelCase : Union[str, Any] = scheduler.step(_A ,_A ,_A ).prev_sample
return sample
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = dict(self.forward_default_kwargs )
_lowerCAmelCase : Any = kwargs.pop('num_inference_steps' ,_A )
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : str = self.get_scheduler_config()
_lowerCAmelCase : List[str] = scheduler_class(**_A )
_lowerCAmelCase : Any = self.dummy_sample
_lowerCAmelCase : Tuple = 0.1 * sample
if num_inference_steps is not None and hasattr(_A ,'set_timesteps' ):
scheduler.set_timesteps(_A )
elif num_inference_steps is not None and not hasattr(_A ,'set_timesteps' ):
_lowerCAmelCase : Optional[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowerCAmelCase : Optional[Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
_lowerCAmelCase : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
_lowerCAmelCase : Any = scheduler.timesteps[5]
_lowerCAmelCase : List[str] = scheduler.timesteps[6]
_lowerCAmelCase : List[str] = scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
_lowerCAmelCase : Optional[int] = scheduler.step(_A ,_A ,_A ,**_A ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : int = UniPCMultistepScheduler(**self.get_scheduler_config() )
_lowerCAmelCase : Optional[Any] = self.full_loop(scheduler=_A )
_lowerCAmelCase : Tuple = torch.mean(torch.abs(_A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
_lowerCAmelCase : int = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_lowerCAmelCase : List[str] = DEISMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase : Any = UniPCMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase : Union[str, Any] = self.full_loop(scheduler=_A )
_lowerCAmelCase : List[str] = torch.mean(torch.abs(_A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def __lowerCamelCase ( self ):
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=_A )
def __lowerCamelCase ( self ):
'''simple docstring'''
self.check_over_configs(thresholding=_A )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_A ,prediction_type=_A ,sample_max_value=_A ,solver_order=_A ,solver_type=_A ,)
def __lowerCamelCase ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_A )
def __lowerCamelCase ( self ):
'''simple docstring'''
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_A ,solver_type=_A ,prediction_type=_A ,)
_lowerCAmelCase : List[Any] = self.full_loop(
solver_order=_A ,solver_type=_A ,prediction_type=_A ,)
assert not torch.isnan(_A ).any(), "Samples have nan numbers"
def __lowerCamelCase ( self ):
'''simple docstring'''
self.check_over_configs(lower_order_final=_A )
self.check_over_configs(lower_order_final=_A )
def __lowerCamelCase ( self ):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=_A ,time_step=0 )
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.full_loop()
_lowerCAmelCase : Tuple = torch.mean(torch.abs(_A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : str = self.full_loop(prediction_type='v_prediction' )
_lowerCAmelCase : Union[str, Any] = torch.mean(torch.abs(_A ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.scheduler_classes[0]
_lowerCAmelCase : int = self.get_scheduler_config(thresholding=_A ,dynamic_thresholding_ratio=0 )
_lowerCAmelCase : Tuple = scheduler_class(**_A )
_lowerCAmelCase : Optional[Any] = 10
_lowerCAmelCase : Union[str, Any] = self.dummy_model()
_lowerCAmelCase : Dict = self.dummy_sample_deter.half()
scheduler.set_timesteps(_A )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase : Tuple = model(_A ,_A )
_lowerCAmelCase : Dict = scheduler.step(_A ,_A ,_A ).prev_sample
assert sample.dtype == torch.floataa
def __lowerCamelCase ( self ,**_A ):
'''simple docstring'''
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : Dict = self.get_scheduler_config(**_A )
_lowerCAmelCase : str = scheduler_class(**_A )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 16
| 1
|
"""simple docstring"""
__lowercase : Any = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case, snake_case):
# Return True if there is node that has not iterated.
__snake_case = [False] * len(snake_case)
__snake_case = [s]
__snake_case = True
while queue:
__snake_case = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(snake_case)
__snake_case = True
__snake_case = u
return visited[t]
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case):
__snake_case = [-1] * (len(snake_case))
__snake_case = 0
__snake_case = []
__snake_case = [i[:] for i in graph] # Record original cut, copy.
while bfs(snake_case, snake_case, snake_case, snake_case):
__snake_case = float('''Inf''')
__snake_case = sink
while s != source:
# Find the minimum value in select path
__snake_case = min(snake_case, graph[parent[s]][s])
__snake_case = parent[s]
max_flow += path_flow
__snake_case = sink
while v != source:
__snake_case = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__snake_case = parent[v]
for i in range(len(snake_case)):
for j in range(len(graph[0])):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j))
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 564
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Union[str, Any] = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Dict = ["RemBertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : int = ["RemBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
"REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RemBertForCausalLM",
"RemBertForMaskedLM",
"RemBertForMultipleChoice",
"RemBertForQuestionAnswering",
"RemBertForSequenceClassification",
"RemBertForTokenClassification",
"RemBertLayer",
"RemBertModel",
"RemBertPreTrainedModel",
"load_tf_weights_in_rembert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Dict = [
"TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertLayer",
"TFRemBertModel",
"TFRemBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__lowercase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 564
| 1
|
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase = direct_transformers_import(PATH_TO_TRANSFORMERS)
lowerCAmelCase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
lowerCAmelCase = {
# used to compute the property `self.chunk_length`
'EncodecConfig': ['overlap'],
# used as `self.bert_model = BertModel(config, ...)`
'DPRConfig': True,
# not used in modeling files, but it's an important information
'FSMTConfig': ['langs'],
# used internally in the configuration class file
'GPTNeoConfig': ['attention_types'],
# used internally in the configuration class file
'EsmConfig': ['is_folding_model'],
# used during training (despite we don't have training script for these models yet)
'Mask2FormerConfig': ['ignore_value'],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'OneFormerConfig': ['ignore_value', 'norm'],
# used during preprocessing and collation, see `collating_graphormer.py`
'GraphormerConfig': ['spatial_pos_max'],
# used internally in the configuration class file
'T5Config': ['feed_forward_proj'],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
# used internally in the configuration class file
'LongT5Config': ['feed_forward_proj'],
# used internally in the configuration class file
'SwitchTransformersConfig': ['feed_forward_proj'],
# having default values other than `1e-5` - we can't fix them without breaking
'BioGptConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'GLPNConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'SegformerConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'CvtConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'PerceiverConfig': ['layer_norm_eps'],
# used internally to calculate the feature size
'InformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate `mlp_dim`
'SamVisionConfig': ['mlp_ratio'],
# For (head) training, but so far not implemented
'ClapAudioConfig': ['num_classes'],
# Not used, but providing useful information to users
'SpeechT5HifiGanConfig': ['sampling_rate'],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'CLIPSegConfig': True,
'DeformableDetrConfig': True,
'DetaConfig': True,
'DinatConfig': True,
'DonutSwinConfig': True,
'EfficientFormerConfig': True,
'FSMTConfig': True,
'JukeboxConfig': True,
'LayoutLMv2Config': True,
'MaskFormerSwinConfig': True,
'MT5Config': True,
'NatConfig': True,
'OneFormerConfig': True,
'PerceiverConfig': True,
'RagConfig': True,
'SpeechT5Config': True,
'SwinConfig': True,
'Swin2SRConfig': True,
'Swinv2Config': True,
'SwitchTransformersConfig': True,
'TableTransformerConfig': True,
'TapasConfig': True,
'TransfoXLConfig': True,
'UniSpeechConfig': True,
'UniSpeechSatConfig': True,
'WavLMConfig': True,
'WhisperConfig': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'JukeboxPriorConfig': True,
# TODO: @Younes (for `is_decoder`)
'Pix2StructTextConfig': True,
}
)
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f'config.{attribute}' in modeling_source
or f'getattr(config, "{attribute}"' in modeling_source
or f'getattr(self.config, "{attribute}"' in modeling_source
):
lowercase__ = True
# Deal with multi-line cases
elif (
re.search(
Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"' , SCREAMING_SNAKE_CASE , )
is not None
):
lowercase__ = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowercase__ = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowercase__ = [
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
lowercase__ = ['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
lowercase__ = True
if not attribute_used:
lowercase__ = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowercase__ = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowercase__ = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowercase__ = True
elif attribute.endswith('''_token_id''' ):
lowercase__ = True
# configuration class specific cases
if not case_allowed:
lowercase__ = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowercase__ = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = dict(inspect.signature(config_class.__init__ ).parameters )
lowercase__ = [x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
lowercase__ = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowercase__ = {}
if len(config_class.attribute_map ) > 0:
lowercase__ = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowercase__ = inspect.getsourcefile(SCREAMING_SNAKE_CASE )
lowercase__ = os.path.dirname(SCREAMING_SNAKE_CASE )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowercase__ = [os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for fn in os.listdir(SCREAMING_SNAKE_CASE ) if fn.startswith('''modeling_''' )]
# Get the source code strings
lowercase__ = []
for path in modeling_paths:
if os.path.isfile(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE ) as fp:
modeling_sources.append(fp.read() )
lowercase__ = []
for config_param, default_value in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# `attributes` here is all the variant names for `config_param`
lowercase__ = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
unused_attributes.append(attributes[0] )
return sorted(SCREAMING_SNAKE_CASE )
def _a ( ):
"""simple docstring"""
lowercase__ = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowercase__ = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda SCREAMING_SNAKE_CASE : inspect.isclass(SCREAMING_SNAKE_CASE )
and issubclass(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
and inspect.getmodule(SCREAMING_SNAKE_CASE ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowercase__ = check_config_attributes_being_used(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
lowercase__ = unused_attributes
if len(SCREAMING_SNAKE_CASE ) > 0:
lowercase__ = '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += f'{name}: {attributes}\n'
raise ValueError(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
check_config_attributes()
| 429
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
lowerCAmelCase = logging.get_logger(__name__)
class _a ( UpperCamelCase__ ):
_lowercase : Optional[int] = ['''pixel_values''']
def __init__( self: Any , UpperCamelCase_: bool = True , UpperCamelCase_: Optional[Dict[str, int]] = None , UpperCamelCase_: PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase_: bool = True , UpperCamelCase_: Dict[str, int] = None , UpperCamelCase_: bool = True , UpperCamelCase_: Union[int, float] = 1 / 255 , UpperCamelCase_: bool = True , UpperCamelCase_: Optional[Union[float, List[float]]] = None , UpperCamelCase_: Optional[Union[float, List[float]]] = None , **UpperCamelCase_: Optional[int] , ) -> None:
"""simple docstring"""
super().__init__(**UpperCamelCase_ )
lowercase__ = size if size is not None else {'''shortest_edge''': 256}
lowercase__ = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
lowercase__ = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ = get_size_dict(UpperCamelCase_ , param_name='''crop_size''' )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = resample
lowercase__ = do_center_crop
lowercase__ = crop_size
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: np.ndarray , UpperCamelCase_: Dict[str, int] , UpperCamelCase_: PILImageResampling = PILImageResampling.BICUBIC , UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_: Union[str, Any] , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
lowercase__ = get_resize_output_image_size(UpperCamelCase_ , size=size['''shortest_edge'''] , default_to_square=UpperCamelCase_ )
return resize(UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: int , UpperCamelCase_: np.ndarray , UpperCamelCase_: Dict[str, int] , UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_: Optional[int] , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(UpperCamelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(UpperCamelCase_ , size=(size['''height'''], size['''width''']) , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: np.ndarray , UpperCamelCase_: float , UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_: Tuple ) -> np.ndarray:
"""simple docstring"""
return rescale(UpperCamelCase_ , scale=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: np.ndarray , UpperCamelCase_: Union[float, List[float]] , UpperCamelCase_: Union[float, List[float]] , UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_: Dict , ) -> np.ndarray:
"""simple docstring"""
return normalize(UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: ImageInput , UpperCamelCase_: Optional[bool] = None , UpperCamelCase_: Dict[str, int] = None , UpperCamelCase_: PILImageResampling = None , UpperCamelCase_: bool = None , UpperCamelCase_: Dict[str, int] = None , UpperCamelCase_: Optional[bool] = None , UpperCamelCase_: Optional[float] = None , UpperCamelCase_: Optional[bool] = None , UpperCamelCase_: Optional[Union[float, List[float]]] = None , UpperCamelCase_: Optional[Union[float, List[float]]] = None , UpperCamelCase_: Optional[Union[str, TensorType]] = None , UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCamelCase_: Tuple , ) -> Optional[int]:
"""simple docstring"""
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ = crop_size if crop_size is not None else self.crop_size
lowercase__ = get_size_dict(UpperCamelCase_ , param_name='''crop_size''' )
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = make_list_of_images(UpperCamelCase_ )
if not valid_images(UpperCamelCase_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
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.
lowercase__ = [to_numpy_array(UpperCamelCase_ ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ ) for image in images]
if do_center_crop:
lowercase__ = [self.center_crop(image=UpperCamelCase_ , size=UpperCamelCase_ ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=UpperCamelCase_ , scale=UpperCamelCase_ ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ ) for image in images]
lowercase__ = [to_channel_dimension_format(UpperCamelCase_ , UpperCamelCase_ ) for image in images]
lowercase__ = {'''pixel_values''': images}
return BatchFeature(data=UpperCamelCase_ , tensor_type=UpperCamelCase_ )
def lowerCamelCase_ ( self: Any , UpperCamelCase_: List[str] , UpperCamelCase_: List[Tuple] = None ) -> List[str]:
"""simple docstring"""
lowercase__ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(UpperCamelCase_ ):
lowercase__ = target_sizes.numpy()
lowercase__ = []
for idx in range(len(UpperCamelCase_ ) ):
lowercase__ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=UpperCamelCase_ )
lowercase__ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCamelCase_ )
else:
lowercase__ = logits.argmax(dim=1 )
lowercase__ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 429
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 639
|
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCamelCase_ = logging.get_logger(__name__)
class a_ ( a_ ):
'''simple docstring'''
__a: Optional[int] = ['''pixel_values''']
def __init__( self , lowercase_ = True , lowercase_ = 3_2 , lowercase_=PILImageResampling.BILINEAR , lowercase_ = True , **lowercase_ , ) -> None:
'''simple docstring'''
lowerCAmelCase_ = do_resize
lowerCAmelCase_ = do_rescale
lowerCAmelCase_ = size_divisor
lowerCAmelCase_ = resample
super().__init__(**lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ = get_image_size(lowercase_ )
# Rounds the height and width down to the closest multiple of size_divisor
lowerCAmelCase_ = height // size_divisor * size_divisor
lowerCAmelCase_ = width // size_divisor * size_divisor
lowerCAmelCase_ = resize(lowercase_ , (new_h, new_w) , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
return image
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ ) -> np.ndarray:
'''simple docstring'''
return rescale(image=lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def _lowercase ( self , lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_=None , lowercase_ = None , lowercase_ = None , lowercase_ = ChannelDimension.FIRST , **lowercase_ , ) -> BatchFeature:
'''simple docstring'''
lowerCAmelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ = size_divisor if size_divisor is not None else self.size_divisor
lowerCAmelCase_ = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('size_divisor is required for resizing' )
lowerCAmelCase_ = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError('Invalid image(s)' )
# All transformations expect numpy arrays.
lowerCAmelCase_ = [to_numpy_array(lowercase_ ) for img in images]
if do_resize:
lowerCAmelCase_ = [self.resize(lowercase_ , size_divisor=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
lowerCAmelCase_ = [self.rescale(lowercase_ , scale=1 / 2_5_5 ) for image in images]
lowerCAmelCase_ = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
lowerCAmelCase_ = {'pixel_values': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 318
| 0
|
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class a :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase : Dict , lowerCamelCase : Optional[int]=99 , lowerCamelCase : Any=13 , lowerCamelCase : Dict=7 , lowerCamelCase : Tuple=9 , lowerCamelCase : int=True , lowerCamelCase : Dict=True , lowerCamelCase : Tuple=False , lowerCamelCase : Dict=32 , lowerCamelCase : List[Any]=5 , lowerCamelCase : int=4 , lowerCamelCase : Tuple=37 , lowerCamelCase : Tuple=8 , lowerCamelCase : int=0.1 , lowerCamelCase : int=0.0_02 , lowerCamelCase : int=1 , lowerCamelCase : Tuple=0 , lowerCamelCase : Dict=0 , lowerCamelCase : List[Any]=None , lowerCamelCase : Optional[int]=None , ) -> str:
__snake_case : Any = parent
__snake_case : int = batch_size
__snake_case : Dict = encoder_seq_length
__snake_case : Optional[Any] = decoder_seq_length
# For common tests
__snake_case : str = self.decoder_seq_length
__snake_case : Any = is_training
__snake_case : int = use_attention_mask
__snake_case : Any = use_labels
__snake_case : str = vocab_size
__snake_case : Union[str, Any] = hidden_size
__snake_case : Tuple = num_hidden_layers
__snake_case : str = num_attention_heads
__snake_case : List[Any] = d_ff
__snake_case : List[str] = relative_attention_num_buckets
__snake_case : str = dropout_rate
__snake_case : int = initializer_factor
__snake_case : Optional[Any] = eos_token_id
__snake_case : Tuple = pad_token_id
__snake_case : List[str] = decoder_start_token_id
__snake_case : Optional[Any] = None
__snake_case : List[str] = decoder_layers
def __snake_case ( self : Union[str, Any] ) -> Dict:
return TaConfig.from_pretrained("google/umt5-base" )
def __snake_case ( self : Any , lowerCamelCase : Optional[int] , lowerCamelCase : str , lowerCamelCase : Optional[Any] , lowerCamelCase : int=None , lowerCamelCase : Any=None , lowerCamelCase : List[Any]=None , lowerCamelCase : Any=None , lowerCamelCase : Any=None , ) -> Dict:
if attention_mask is None:
__snake_case : Any = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__snake_case : Union[str, Any] = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__snake_case : Dict = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__A )
if decoder_head_mask is None:
__snake_case : Tuple = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__A )
if cross_attn_head_mask is None:
__snake_case : Any = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__A )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def __snake_case ( self : Tuple ) -> Union[str, Any]:
__snake_case : List[Any] = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
__snake_case : Optional[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__snake_case : List[Any] = input_ids.clamp(self.pad_token_id + 1 )
__snake_case : List[str] = decoder_input_ids.clamp(self.pad_token_id + 1 )
__snake_case : Optional[Any] = self.get_config()
__snake_case : List[str] = config.num_attention_heads
__snake_case : Union[str, Any] = self.prepare_inputs_dict(__A , __A , __A )
return config, input_dict
def __snake_case ( self : Optional[int] ) -> Tuple:
__snake_case : int = self.prepare_config_and_inputs()
return config, inputs_dict
def __snake_case ( self : str ) -> Optional[int]:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __snake_case ( self : Tuple ) -> Any:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __snake_case ( self : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[str] , ) -> List[Any]:
__snake_case : List[Any] = UMTaModel(config=__A )
model.to(__A )
model.eval()
__snake_case : List[Any] = model(
input_ids=__A , decoder_input_ids=__A , attention_mask=__A , decoder_attention_mask=__A , )
__snake_case : List[str] = model(input_ids=__A , decoder_input_ids=__A )
__snake_case : int = result.last_hidden_state
__snake_case : Optional[Any] = result.past_key_values
__snake_case : Optional[Any] = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__A ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def __snake_case ( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Dict , lowerCamelCase : int , lowerCamelCase : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : Any , ) -> List[str]:
__snake_case : Optional[Any] = UMTaModel(config=__A ).get_decoder().to(__A ).eval()
# first forward pass
__snake_case : Tuple = model(__A , use_cache=__A )
__snake_case : List[str] = model(__A )
__snake_case : List[str] = model(__A , use_cache=__A )
self.parent.assertTrue(len(__A ) == len(__A ) )
self.parent.assertTrue(len(__A ) == len(__A ) + 1 )
__snake_case : Union[str, Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__snake_case : Optional[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
__snake_case : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__snake_case : Tuple = model(__A )["last_hidden_state"]
__snake_case : Tuple = model(__A , past_key_values=__A )["last_hidden_state"]
# select random slice
__snake_case : str = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__snake_case : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
__snake_case : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__A , __A , atol=1E-3 ) )
def __snake_case ( self : int , lowerCamelCase : Optional[int] , lowerCamelCase : List[str] , ) -> Any:
__snake_case : List[str] = UMTaModel(config=__A ).to(__A ).half().eval()
__snake_case : Any = model(**__A )["last_hidden_state"]
self.parent.assertFalse(torch.isnan(__A ).any().item() )
@require_torch
class a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : List[str] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__UpperCAmelCase : Dict = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__UpperCAmelCase : Union[str, Any] = (
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : Dict = False
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Dict = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__UpperCAmelCase : Dict = [0.8, 0.9]
def __snake_case ( self : int ) -> Optional[Any]:
__snake_case : Tuple = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def __snake_case ( self : Union[str, Any] ) -> Optional[int]:
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
__snake_case : Tuple = UMTaModel(config_and_inputs[0] ).to(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__A , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=__A , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def __snake_case ( self : Optional[int] ) -> str:
__snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__A )
def __snake_case ( self : Tuple ) -> Optional[Any]:
__snake_case : Union[str, Any] = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
__snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
__snake_case : List[Any] = config_and_inputs[0]
__snake_case : Optional[int] = UMTaForConditionalGeneration(__A ).eval()
model.to(__A )
__snake_case : Optional[Any] = {
"head_mask": torch.zeros(config.num_layers , config.num_heads , device=__A ),
"decoder_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=__A ),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=__A ),
}
for attn_name, (name, mask) in zip(__A , head_masking.items() ):
__snake_case : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
__snake_case : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=__A )
__snake_case : str = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=__A , return_dict_in_generate=__A , **__A , )
# We check the state of decoder_attentions and cross_attentions just from the last step
__snake_case : int = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def __snake_case ( self : List[str] ) -> Union[str, Any]:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class a (unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def __snake_case ( self : Dict ) -> Dict:
__snake_case : List[Any] = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=__A ).to(__A )
__snake_case : Dict = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=__A , legacy=__A )
__snake_case : Any = [
"Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
"No se como puedo <extra_id_0>.",
"This is the reason why we <extra_id_0> them.",
"The <extra_id_0> walks in <extra_id_1>, seats",
"A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
]
__snake_case : List[str] = tokenizer(__A , return_tensors="pt" , padding=__A ).input_ids
# fmt: off
__snake_case : Optional[int] = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(__A , __A )
__snake_case : Dict = model.generate(input_ids.to(__A ) )
__snake_case : Optional[int] = [
"<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
"<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
]
__snake_case : str = tokenizer.batch_decode(__A )
self.assertEqual(__A , __A )
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|
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
__snake_case : Dict = str(bin(__lowerCamelCase ) )
binary_number += "0" * shift_amount
return binary_number
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
__snake_case : List[str] = str(bin(__lowerCamelCase ) )[2:]
if shift_amount >= len(__lowerCamelCase ):
return "0b0"
__snake_case : Union[str, Any] = binary_number[: len(__lowerCamelCase ) - shift_amount]
return "0b" + shifted_binary_number
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if number >= 0: # Get binary representation of positive number
__snake_case : Optional[int] = "0" + str(bin(__lowerCamelCase ) ).strip("-" )[2:]
else: # Get binary (2's complement) representation of negative number
__snake_case : Tuple = len(bin(__lowerCamelCase )[3:] ) # Find 2's complement of number
__snake_case : Any = bin(abs(__lowerCamelCase ) - (1 << binary_number_length) )[3:]
__snake_case : Optional[Any] = (
"1" + "0" * (binary_number_length - len(__lowerCamelCase )) + binary_number
)
if shift_amount >= len(__lowerCamelCase ):
return "0b" + binary_number[0] * len(__lowerCamelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__lowerCamelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
a = StableDiffusionSAGPipeline
a = TEXT_TO_IMAGE_PARAMS
a = TEXT_TO_IMAGE_BATCH_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
a = False
def _lowerCamelCase ( self ):
torch.manual_seed(0 )
A_ : Dict = 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 , )
A_ : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=a__ , set_alpha_to_one=a__ , )
torch.manual_seed(0 )
A_ : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
A_ : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
A_ : Optional[int] = CLIPTextModel(a__ )
A_ : List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A_ : Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _lowerCamelCase ( self , a__ , a__=0 ):
if str(a__ ).startswith("""mps""" ):
A_ : Union[str, Any] = torch.manual_seed(a__ )
else:
A_ : Optional[int] = torch.Generator(device=a__ ).manual_seed(a__ )
A_ : List[Any] = {
"""prompt""": """.""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 1.0,
"""sag_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def _lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self ):
A_ : Optional[int] = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
A_ : Tuple = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : Optional[Any] = """."""
A_ : Optional[Any] = torch.manual_seed(0 )
A_ : str = sag_pipe(
[prompt] , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
A_ : Tuple = output.images
A_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : List[Any] = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def _lowerCamelCase ( self ):
A_ : Dict = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : List[str] = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : List[str] = """."""
A_ : List[Any] = torch.manual_seed(0 )
A_ : List[str] = sag_pipe(
[prompt] , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
A_ : Union[str, Any] = output.images
A_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : str = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def _lowerCamelCase ( self ):
A_ : Optional[Any] = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : Tuple = sag_pipe.to(a__ )
sag_pipe.set_progress_bar_config(disable=a__ )
A_ : Optional[Any] = """."""
A_ : Any = torch.manual_seed(0 )
A_ : Optional[int] = sag_pipe(
[prompt] , width=768 , height=512 , generator=a__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" , )
A_ : Optional[int] = output.images
assert image.shape == (1, 512, 768, 3)
| 569
|
from ...configuration_utils import PretrainedConfig
class _UpperCAmelCase ( _lowerCamelCase ):
a = '''bert-generation'''
def __init__( self , a__=50358 , a__=1024 , a__=24 , a__=16 , a__=4096 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=0.02 , a__=1E-12 , a__=0 , a__=2 , a__=1 , a__="absolute" , a__=True , **a__ , ):
super().__init__(pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ , **a__ )
A_ : List[str] = vocab_size
A_ : int = hidden_size
A_ : List[str] = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : Optional[int] = hidden_act
A_ : Optional[int] = intermediate_size
A_ : List[Any] = hidden_dropout_prob
A_ : int = attention_probs_dropout_prob
A_ : List[str] = max_position_embeddings
A_ : Optional[Any] = initializer_range
A_ : str = layer_norm_eps
A_ : str = position_embedding_type
A_ : List[Any] = use_cache
| 569
| 1
|
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
UpperCamelCase = {
"""tokenizer_file""": {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
},
}
UpperCamelCase = {
"""gpt-neox-20b""": 20_48,
}
class _a ( lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_="<|endoftext|>" , UpperCAmelCase_="<|endoftext|>" , UpperCAmelCase_="<|endoftext|>" , UpperCAmelCase_=False , **UpperCAmelCase_ , ) -> Any:
'''simple docstring'''
super().__init__(
UpperCAmelCase_ , UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowercase__: Union[str, Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , UpperCAmelCase_) != add_prefix_space:
lowercase__: str = getattr(UpperCAmelCase_ , pre_tok_state.pop("type"))
lowercase__: int = add_prefix_space
lowercase__: List[Any] = pre_tok_class(**UpperCAmelCase_)
lowercase__: str = add_prefix_space
def __lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ = None) -> Tuple[str]:
'''simple docstring'''
lowercase__: Optional[Any] = self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_)
return tuple(UpperCAmelCase_)
def __lowercase ( self , UpperCAmelCase_) -> List[int]:
'''simple docstring'''
lowercase__: List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_) + [self.eos_token_id])
if len(UpperCAmelCase_) > self.model_max_length:
lowercase__: int = input_ids[-self.model_max_length :]
return input_ids
| 120
|
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class _a ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_=7 , UpperCAmelCase_=3 , UpperCAmelCase_=18 , UpperCAmelCase_=30 , UpperCAmelCase_=400 , UpperCAmelCase_=True , UpperCAmelCase_=None , UpperCAmelCase_=True , UpperCAmelCase_=None , UpperCAmelCase_=True , UpperCAmelCase_=[0.5, 0.5, 0.5] , UpperCAmelCase_=[0.5, 0.5, 0.5] , UpperCAmelCase_=False , ) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = size if size is not None else {"height": 20, "width": 20}
lowercase__: Tuple = crop_size if crop_size is not None else {"height": 18, "width": 18}
lowercase__: Optional[int] = parent
lowercase__: Optional[int] = batch_size
lowercase__: Union[str, Any] = num_channels
lowercase__: Any = image_size
lowercase__: List[Any] = min_resolution
lowercase__: Optional[Any] = max_resolution
lowercase__: Dict = do_resize
lowercase__: str = size
lowercase__: str = do_center_crop
lowercase__: List[Any] = crop_size
lowercase__: List[Any] = do_normalize
lowercase__: Any = image_mean
lowercase__: Any = image_std
lowercase__: List[str] = do_reduce_labels
def __lowercase ( self) -> Tuple:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def A( ):
"""simple docstring"""
lowercase__: Tuple = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
lowercase__: List[str] = Image.open(dataset[0]["file"] )
lowercase__: str = Image.open(dataset[1]["file"] )
return image, map
def A( ):
"""simple docstring"""
lowercase__: Optional[Any] = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
lowercase__: Any = Image.open(ds[0]["file"] )
lowercase__: List[Any] = Image.open(ds[1]["file"] )
lowercase__: Optional[Any] = Image.open(ds[2]["file"] )
lowercase__: Tuple = Image.open(ds[3]["file"] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class _a ( lowercase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ = BeitImageProcessor if is_vision_available() else None
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
lowercase__: str = BeitImageProcessingTester(self)
@property
def __lowercase ( self) -> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__: List[str] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCAmelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCAmelCase_ , "size"))
self.assertTrue(hasattr(UpperCAmelCase_ , "do_center_crop"))
self.assertTrue(hasattr(UpperCAmelCase_ , "center_crop"))
self.assertTrue(hasattr(UpperCAmelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCAmelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCAmelCase_ , "image_std"))
def __lowercase ( self) -> Tuple:
'''simple docstring'''
lowercase__: Tuple = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"height": 20, "width": 20})
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18})
self.assertEqual(image_processor.do_reduce_labels , UpperCAmelCase_)
lowercase__: Dict = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=UpperCAmelCase_)
self.assertEqual(image_processor.size , {"height": 42, "width": 42})
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84})
self.assertEqual(image_processor.do_reduce_labels , UpperCAmelCase_)
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
pass
def __lowercase ( self) -> int:
'''simple docstring'''
lowercase__: int = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
lowercase__: List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , Image.Image)
# Test not batched input
lowercase__: int = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowercase__: Optional[int] = image_processing(UpperCAmelCase_ , 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 __lowercase ( self) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
lowercase__: Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , numpify=UpperCAmelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , np.ndarray)
# Test not batched input
lowercase__: Any = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowercase__: Any = image_processing(UpperCAmelCase_ , 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 __lowercase ( self) -> str:
'''simple docstring'''
lowercase__: List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
lowercase__: List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor)
# Test not batched input
lowercase__: List[str] = 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
lowercase__: Tuple = image_processing(UpperCAmelCase_ , 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 __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
lowercase__: Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_)
lowercase__: Optional[Any] = []
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor)
maps.append(torch.zeros(image.shape[-2:]).long())
# Test not batched input
lowercase__: Any = image_processing(image_inputs[0] , maps[0] , return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(
encoding["labels"].shape , (
1,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(encoding["labels"].dtype , torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test batched
lowercase__: Tuple = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(
encoding["labels"].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(encoding["labels"].dtype , torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test not batched input (PIL images)
lowercase__ , lowercase__: List[str] = prepare_semantic_single_inputs()
lowercase__: int = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(
encoding["labels"].shape , (
1,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(encoding["labels"].dtype , torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Test batched input (PIL images)
lowercase__ , lowercase__: List[Any] = prepare_semantic_batch_inputs()
lowercase__: List[str] = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors="pt")
self.assertEqual(
encoding["pixel_values"].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(
encoding["labels"].shape , (
2,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
self.assertEqual(encoding["labels"].dtype , torch.long)
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__: Tuple = self.image_processing_class(**self.image_processor_dict)
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
lowercase__ , lowercase__: List[Any] = prepare_semantic_single_inputs()
lowercase__: List[str] = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 150)
lowercase__: Dict = True
lowercase__: Dict = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
| 120
| 1
|
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
SCREAMING_SNAKE_CASE : Optional[Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
SCREAMING_SNAKE_CASE : Dict = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
SCREAMING_SNAKE_CASE : Tuple = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
return float((preds == labels).mean() )
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any]="binary" ):
UpperCamelCase_ : str = simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Union[str, Any] = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average=_SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ):
UpperCamelCase_ : int = {}
for id_pred, label in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : Union[str, Any] = f'''{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'''
UpperCamelCase_ : Dict = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
UpperCamelCase_ : int = [(pred, label)]
UpperCamelCase_,UpperCamelCase_ : str = [], []
for question, preds_labels in question_map.items():
UpperCamelCase_,UpperCamelCase_ : Any = zip(*_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Any = fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average="""macro""" )
fas.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Dict = int(sum(pred == label for pred, label in preds_labels ) == len(_SCREAMING_SNAKE_CASE ) )
ems.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Any = float(sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ : Tuple = sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Union[str, Any] = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
def A_ (self ) -> Optional[int]:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def A_ (self ) -> Any:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def A_ (self , __UpperCamelCase , __UpperCamelCase ) -> int:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(__UpperCamelCase , __UpperCamelCase )}
elif self.config_name == "cb":
return acc_and_fa(__UpperCamelCase , __UpperCamelCase , fa_avg="""macro""" )
elif self.config_name == "record":
UpperCamelCase_ : Any = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
UpperCamelCase_ : Tuple = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(__UpperCamelCase , __UpperCamelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(__UpperCamelCase , __UpperCamelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(__UpperCamelCase , __UpperCamelCase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 635
|
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
SCREAMING_SNAKE_CASE : Union[str, Any] = {"UserAgent": UserAgent().random}
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Dict ):
UpperCamelCase_ : Tuple = script.contents[0]
UpperCamelCase_ : List[Any] = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase :
def __init__(self , __UpperCamelCase ) -> List[str]:
UpperCamelCase_ : Union[str, Any] = f'''https://www.instagram.com/{username}/'''
UpperCamelCase_ : Optional[int] = self.get_json()
def A_ (self ) -> dict:
UpperCamelCase_ : Optional[Any] = requests.get(self.url , headers=__UpperCamelCase ).text
UpperCamelCase_ : Any = BeautifulSoup(__UpperCamelCase , """html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__(self ) -> str:
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__(self ) -> str:
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def A_ (self ) -> str:
return self.user_data["username"]
@property
def A_ (self ) -> str:
return self.user_data["full_name"]
@property
def A_ (self ) -> str:
return self.user_data["biography"]
@property
def A_ (self ) -> str:
return self.user_data["business_email"]
@property
def A_ (self ) -> str:
return self.user_data["external_url"]
@property
def A_ (self ) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def A_ (self ) -> int:
return self.user_data["edge_follow"]["count"]
@property
def A_ (self ) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def A_ (self ) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def A_ (self ) -> bool:
return self.user_data["is_verified"]
@property
def A_ (self ) -> bool:
return self.user_data["is_private"]
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : str = "github" ):
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
UpperCamelCase_ : Any = InstagramUser(_SCREAMING_SNAKE_CASE )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , _SCREAMING_SNAKE_CASE )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 12_0000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Dict = InstagramUser("github")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 635
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger('transformers.models.encodec')
lowerCAmelCase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowerCAmelCase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowerCAmelCase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowerCAmelCase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowerCAmelCase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowerCAmelCase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowerCAmelCase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowerCAmelCase__ = []
lowerCAmelCase__ = []
def _lowerCamelCase ( __a, __a, __a, __a, __a ):
for attribute in key.split('''.''' ):
SCREAMING_SNAKE_CASE_ = getattr(__a, __a )
if weight_type is not None:
SCREAMING_SNAKE_CASE_ = getattr(__a, __a ).shape
else:
SCREAMING_SNAKE_CASE_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "running_mean":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "running_var":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "num_batches_tracked":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_ih_l0":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_hh_l0":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "bias_ih_l0":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "bias_hh_l0":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_ih_l1":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "weight_hh_l1":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "bias_ih_l1":
SCREAMING_SNAKE_CASE_ = value
elif weight_type == "bias_hh_l1":
SCREAMING_SNAKE_CASE_ = value
else:
SCREAMING_SNAKE_CASE_ = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def _lowerCamelCase ( __a, __a ):
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _lowerCamelCase ( __a, __a, __a ):
SCREAMING_SNAKE_CASE_ = []
if model_name == "encodec_24khz" or "encodec_32khz":
SCREAMING_SNAKE_CASE_ = MAPPING_24K
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE_ = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(__a, __a ):
logger.info(F'{name} was ignored' )
continue
SCREAMING_SNAKE_CASE_ = False
for key, mapped_key in MAPPING.items():
if "*" in key:
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = key.split('''.*.''' )
if prefix in name and suffix in name:
SCREAMING_SNAKE_CASE_ = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ):
continue
SCREAMING_SNAKE_CASE_ = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE_ = name.split(__a )[0].split('''.''' )[-2]
SCREAMING_SNAKE_CASE_ = mapped_key.replace('''*''', __a )
if "weight_g" in name:
SCREAMING_SNAKE_CASE_ = '''weight_g'''
elif "weight_v" in name:
SCREAMING_SNAKE_CASE_ = '''weight_v'''
elif "weight_ih_l0" in name:
SCREAMING_SNAKE_CASE_ = '''weight_ih_l0'''
elif "weight_hh_l0" in name:
SCREAMING_SNAKE_CASE_ = '''weight_hh_l0'''
elif "bias_ih_l0" in name:
SCREAMING_SNAKE_CASE_ = '''bias_ih_l0'''
elif "bias_hh_l0" in name:
SCREAMING_SNAKE_CASE_ = '''bias_hh_l0'''
elif "weight_ih_l1" in name:
SCREAMING_SNAKE_CASE_ = '''weight_ih_l1'''
elif "weight_hh_l1" in name:
SCREAMING_SNAKE_CASE_ = '''weight_hh_l1'''
elif "bias_ih_l1" in name:
SCREAMING_SNAKE_CASE_ = '''bias_ih_l1'''
elif "bias_hh_l1" in name:
SCREAMING_SNAKE_CASE_ = '''bias_hh_l1'''
elif "bias" in name:
SCREAMING_SNAKE_CASE_ = '''bias'''
elif "weight" in name:
SCREAMING_SNAKE_CASE_ = '''weight'''
elif "running_mean" in name:
SCREAMING_SNAKE_CASE_ = '''running_mean'''
elif "running_var" in name:
SCREAMING_SNAKE_CASE_ = '''running_var'''
elif "num_batches_tracked" in name:
SCREAMING_SNAKE_CASE_ = '''num_batches_tracked'''
else:
SCREAMING_SNAKE_CASE_ = None
set_recursively(__a, __a, __a, __a, __a )
continue
if not is_used:
unused_weights.append(__a )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def _lowerCamelCase ( __a, __a, __a, __a=None, __a=None, ):
if config_path is not None:
SCREAMING_SNAKE_CASE_ = EncodecConfig.from_pretrained(__a )
else:
SCREAMING_SNAKE_CASE_ = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
SCREAMING_SNAKE_CASE_ = [8, 5, 4, 4]
SCREAMING_SNAKE_CASE_ = [2.2]
SCREAMING_SNAKE_CASE_ = 64
SCREAMING_SNAKE_CASE_ = 32_000
SCREAMING_SNAKE_CASE_ = 2_048
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE_ = [8, 5, 4, 2]
SCREAMING_SNAKE_CASE_ = [3.0, 6.0, 1_2.0, 2_4.0]
SCREAMING_SNAKE_CASE_ = 48_000
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = '''time_group_norm'''
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = 1.0
SCREAMING_SNAKE_CASE_ = 0.0_1
else:
raise ValueError(F'Unknown model name: {model_name}' )
SCREAMING_SNAKE_CASE_ = EncodecModel(__a )
SCREAMING_SNAKE_CASE_ = EncodecFeatureExtractor(
feature_size=config.audio_channels, sampling_rate=config.sampling_rate, chunk_length_s=config.chunk_length_s, overlap=config.overlap, )
feature_extractor.save_pretrained(__a )
SCREAMING_SNAKE_CASE_ = torch.load(__a )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
SCREAMING_SNAKE_CASE_ = original_checkpoint['''best_state''']
recursively_load_weights(__a, __a, __a )
model.save_pretrained(__a )
if repo_id:
print('''Pushing to the hub...''' )
feature_extractor.push_to_hub(__a )
model.push_to_hub(__a )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowerCAmelCase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 628
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class snake_case ( __lowercase , unittest.TestCase ):
UpperCAmelCase__ = KandinskyVaaControlnetPipeline
UpperCAmelCase__ = ['''image_embeds''', '''negative_image_embeds''', '''hint''']
UpperCAmelCase__ = ['''image_embeds''', '''negative_image_embeds''', '''hint''']
UpperCAmelCase__ = [
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
UpperCAmelCase__ = False
@property
def _lowercase (self ):
"""simple docstring"""
return 32
@property
def _lowercase (self ):
"""simple docstring"""
return 32
@property
def _lowercase (self ):
"""simple docstring"""
return self.time_input_dim
@property
def _lowercase (self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def _lowercase (self ):
"""simple docstring"""
return 1_00
@property
def _lowercase (self ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE_ = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def _lowercase (self ):
"""simple docstring"""
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _lowercase (self ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = VQModel(**self.dummy_movq_kwargs )
return model
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.dummy_unet
SCREAMING_SNAKE_CASE_ = self.dummy_movq
SCREAMING_SNAKE_CASE_ = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=SCREAMING_SNAKE_CASE_ , )
SCREAMING_SNAKE_CASE_ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _lowercase (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
SCREAMING_SNAKE_CASE_ )
# create hint
SCREAMING_SNAKE_CASE_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE_ = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
SCREAMING_SNAKE_CASE_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''cpu'''
SCREAMING_SNAKE_CASE_ = self.get_dummy_components()
SCREAMING_SNAKE_CASE_ = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
SCREAMING_SNAKE_CASE_ = output.images
SCREAMING_SNAKE_CASE_ = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
SCREAMING_SNAKE_CASE_ = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE_ = np.array(
[0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def _lowercase (self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )
SCREAMING_SNAKE_CASE_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
SCREAMING_SNAKE_CASE_ = torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_ ) ).float() / 2_55.0
SCREAMING_SNAKE_CASE_ = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
SCREAMING_SNAKE_CASE_ = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = KandinskyVaaControlnetPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE_ = pipeline.to(SCREAMING_SNAKE_CASE_ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = '''A robot, 4k photo'''
SCREAMING_SNAKE_CASE_ = torch.Generator(device='''cuda''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = pipe_prior(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE_ = torch.Generator(device='''cuda''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE_ = pipeline(
image_embeds=SCREAMING_SNAKE_CASE_ , negative_image_embeds=SCREAMING_SNAKE_CASE_ , hint=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=1_00 , output_type='''np''' , )
SCREAMING_SNAKE_CASE_ = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 628
| 1
|
from __future__ import annotations
_lowerCamelCase : str = tuple[int, int, int]
_lowerCamelCase : Dict = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_lowerCamelCase : List[Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- default selection --------------------------
# rotors --------------------------
_lowerCamelCase : List[str] = """EGZWVONAHDCLFQMSIPJBYUKXTR"""
_lowerCamelCase : Union[str, Any] = """FOBHMDKEXQNRAULPGSJVTYICZW"""
_lowerCamelCase : List[str] = """ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
_lowerCamelCase : Optional[Any] = {
"""A""": """N""",
"""N""": """A""",
"""B""": """O""",
"""O""": """B""",
"""C""": """P""",
"""P""": """C""",
"""D""": """Q""",
"""Q""": """D""",
"""E""": """R""",
"""R""": """E""",
"""F""": """S""",
"""S""": """F""",
"""G""": """T""",
"""T""": """G""",
"""H""": """U""",
"""U""": """H""",
"""I""": """V""",
"""V""": """I""",
"""J""": """W""",
"""W""": """J""",
"""K""": """X""",
"""X""": """K""",
"""L""": """Y""",
"""Y""": """L""",
"""M""": """Z""",
"""Z""": """M""",
}
# -------------------------- extra rotors --------------------------
_lowerCamelCase : Union[str, Any] = """RMDJXFUWGISLHVTCQNKYPBEZOA"""
_lowerCamelCase : str = """SGLCPQWZHKXAREONTFBVIYJUDM"""
_lowerCamelCase : Optional[int] = """HVSICLTYKQUBXDWAJZOMFGPREN"""
_lowerCamelCase : Union[str, Any] = """RZWQHFMVDBKICJLNTUXAGYPSOE"""
_lowerCamelCase : Union[str, Any] = """LFKIJODBEGAMQPXVUHYSTCZRWN"""
_lowerCamelCase : Dict = """KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def __a ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(__lowerCAmelCase ) )) < 3:
SCREAMING_SNAKE_CASE : Any = F'''Please use 3 unique rotors (not {unique_rotsel})'''
raise Exception(__lowerCAmelCase )
# Checks if rotor positions are valid
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = rotpos
if not 0 < rotorposa <= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Any = F'''First rotor position is not within range of 1..26 ({rotorposa}'''
raise ValueError(__lowerCAmelCase )
if not 0 < rotorposa <= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(__lowerCAmelCase )
if not 0 < rotorposa <= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : str = F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(__lowerCAmelCase )
# Validates string and returns dict
SCREAMING_SNAKE_CASE : Union[str, Any] = _plugboard(__lowerCAmelCase )
return rotpos, rotsel, pbdict
def __a ( __lowerCAmelCase ) -> dict[str, str]:
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Optional[Any] = F'''Plugboard setting isn\'t type string ({type(__lowerCAmelCase )})'''
raise TypeError(__lowerCAmelCase )
elif len(__lowerCAmelCase ) % 2 != 0:
SCREAMING_SNAKE_CASE : Dict = F'''Odd number of symbols ({len(__lowerCAmelCase )})'''
raise Exception(__lowerCAmelCase )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
SCREAMING_SNAKE_CASE : List[str] = set()
for i in pbstring:
if i not in abc:
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''\'{i}\' not in list of symbols'''
raise Exception(__lowerCAmelCase )
elif i in tmppbl:
SCREAMING_SNAKE_CASE : Optional[int] = F'''Duplicate symbol ({i})'''
raise Exception(__lowerCAmelCase )
else:
tmppbl.add(__lowerCAmelCase )
del tmppbl
# Created the dictionary
SCREAMING_SNAKE_CASE : str = {}
for j in range(0 , len(__lowerCAmelCase ) - 1 , 2 ):
SCREAMING_SNAKE_CASE : Any = pbstring[j + 1]
SCREAMING_SNAKE_CASE : int = pbstring[j]
return pb
def __a ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = (rotora, rotora, rotora) , __lowerCAmelCase = "" , ) -> str:
SCREAMING_SNAKE_CASE : Optional[Any] = text.upper()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = _validator(
__lowerCAmelCase , __lowerCAmelCase , plugb.upper() )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = rotor_position
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
SCREAMING_SNAKE_CASE : Tuple = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
SCREAMING_SNAKE_CASE : int = plugboard[symbol]
# rotor ra --------------------------
SCREAMING_SNAKE_CASE : Any = abc.index(__lowerCAmelCase ) + rotorposa
SCREAMING_SNAKE_CASE : List[str] = rotora[index % len(__lowerCAmelCase )]
# rotor rb --------------------------
SCREAMING_SNAKE_CASE : List[str] = abc.index(__lowerCAmelCase ) + rotorposa
SCREAMING_SNAKE_CASE : List[str] = rotora[index % len(__lowerCAmelCase )]
# rotor rc --------------------------
SCREAMING_SNAKE_CASE : Any = abc.index(__lowerCAmelCase ) + rotorposa
SCREAMING_SNAKE_CASE : Union[str, Any] = rotora[index % len(__lowerCAmelCase )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
SCREAMING_SNAKE_CASE : str = reflector[symbol]
# 2nd rotors
SCREAMING_SNAKE_CASE : str = abc[rotora.index(__lowerCAmelCase ) - rotorposa]
SCREAMING_SNAKE_CASE : str = abc[rotora.index(__lowerCAmelCase ) - rotorposa]
SCREAMING_SNAKE_CASE : List[str] = abc[rotora.index(__lowerCAmelCase ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
SCREAMING_SNAKE_CASE : str = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Optional[int] = 0
rotorposa += 1
if rotorposa >= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : int = 0
rotorposa += 1
if rotorposa >= len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : List[Any] = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(__lowerCAmelCase )
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : int = """This is my Python script that emulates the Enigma machine from WWII."""
_lowerCamelCase : List[str] = (1, 1, 1)
_lowerCamelCase : Any = """pictures"""
_lowerCamelCase : List[Any] = (rotora, rotora, rotora)
_lowerCamelCase : Tuple = enigma(message, rotor_pos, rotor_sel, pb)
print("""Encrypted message:""", en)
print("""Decrypted message:""", enigma(en, rotor_pos, rotor_sel, pb))
| 352
|
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowercase :
'''simple docstring'''
@staticmethod
def lowerCamelCase_ ( *snake_case : Dict , **snake_case : List[Any] ):
'''simple docstring'''
pass
@is_pipeline_test
@require_torch
@require_vision
class lowercase ( unittest.TestCase):
'''simple docstring'''
UpperCAmelCase : Any = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowerCamelCase_ ( self : List[Any] , snake_case : List[str] , snake_case : int , snake_case : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
SCREAMING_SNAKE_CASE : Any = [
{
'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'question': 'How many cats are there?',
},
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'question': 'How many cats are there?',
},
]
return vqa_pipeline, examples
def lowerCamelCase_ ( self : int , snake_case : int , snake_case : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = vqa_pipeline(snake_case , top_k=1 )
self.assertEqual(
snake_case , [
[{'score': ANY(snake_case ), 'answer': ANY(snake_case )}],
[{'score': ANY(snake_case ), 'answer': ANY(snake_case )}],
] , )
@require_torch
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
SCREAMING_SNAKE_CASE : List[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE : Any = 'How many cats are there?'
SCREAMING_SNAKE_CASE : Optional[Any] = vqa_pipeline(image=snake_case , question='How many cats are there?' , top_k=2 )
self.assertEqual(
snake_case , [{'score': ANY(snake_case ), 'answer': ANY(snake_case )}, {'score': ANY(snake_case ), 'answer': ANY(snake_case )}] )
SCREAMING_SNAKE_CASE : int = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
snake_case , [{'score': ANY(snake_case ), 'answer': ANY(snake_case )}, {'score': ANY(snake_case ), 'answer': ANY(snake_case )}] )
@slow
@require_torch
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = pipeline('visual-question-answering' , model='dandelin/vilt-b32-finetuned-vqa' )
SCREAMING_SNAKE_CASE : int = './tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE : Dict = 'How many cats are there?'
SCREAMING_SNAKE_CASE : int = vqa_pipeline(image=snake_case , question=snake_case , top_k=2 )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
SCREAMING_SNAKE_CASE : List[str] = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
SCREAMING_SNAKE_CASE : Any = vqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(snake_case , decimals=4 ) , [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2 , )
@require_tf
@unittest.skip('Visual question answering not implemented in TF' )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
pass
| 352
| 1
|
import os
import sys
import unittest
__lowercase : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase : Optional[int] = os.path.join(git_repo_path, '''src''', '''transformers''')
__lowercase : Any = '''
{0} = None
'''
__lowercase : Union[str, Any] = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
'''
__lowercase : Optional[int] = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = find_backend(""" _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")""" )
self.assertIsNone(SCREAMING_SNAKE_CASE_ )
snake_case : Any = find_backend(""" if not is_tokenizers_available():""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""tokenizers""" )
snake_case : List[Any] = find_backend(""" if not is_tensorflow_text_available():""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""tensorflow_text""" )
snake_case : int = find_backend(""" if not (is_sentencepiece_available() and is_tokenizers_available()):""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""sentencepiece_and_tokenizers""" )
snake_case : Any = find_backend(
""" if not (is_sentencepiece_available() and is_tensorflow_text_available()):""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""sentencepiece_and_tensorflow_text""" )
snake_case : Tuple = find_backend(
""" if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""sentencepiece_and_tokenizers_and_vision""" )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : List[str] = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""" ,SCREAMING_SNAKE_CASE_ )
self.assertIn("""tensorflow_text""" ,SCREAMING_SNAKE_CASE_ )
self.assertIn("""sentencepiece_and_tokenizers""" ,SCREAMING_SNAKE_CASE_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""BertModel""" ,objects["""torch"""] )
self.assertIn("""TFBertModel""" ,objects["""tf"""] )
self.assertIn("""FlaxBertModel""" ,objects["""flax"""] )
self.assertIn("""BertModel""" ,objects["""torch"""] )
self.assertIn("""TFBertTokenizer""" ,objects["""tensorflow_text"""] )
self.assertIn("""convert_slow_tokenizer""" ,objects["""sentencepiece_and_tokenizers"""] )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = create_dummy_object("""CONSTANT""" ,"""'torch'""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,"""\nCONSTANT = None\n""" )
snake_case : str = create_dummy_object("""function""" ,"""'torch'""" )
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
snake_case : Dict = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
"""
snake_case : Any = create_dummy_object("""FakeClass""" ,"""'torch'""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Dict = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
"""
snake_case : int = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""] ,SCREAMING_SNAKE_CASE_ )
| 315
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : Optional[int] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ChineseCLIPConfig''',
'''ChineseCLIPOnnxConfig''',
'''ChineseCLIPTextConfig''',
'''ChineseCLIPVisionConfig''',
],
'''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[Any] = ['''ChineseCLIPFeatureExtractor''']
__lowercase : int = ['''ChineseCLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Dict = [
'''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ChineseCLIPModel''',
'''ChineseCLIPPreTrainedModel''',
'''ChineseCLIPTextModel''',
'''ChineseCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
__lowercase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class a ( datasets.BuilderConfig ):
"""simple docstring"""
__UpperCAmelCase = None
class a ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
__UpperCAmelCase = PandasConfig
def __magic_name__ ( self : int ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def __magic_name__ ( self : List[Any] , snake_case_ : Union[str, Any] ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
snake_case__ : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case_ , (str, list, tuple) ):
snake_case__ : Optional[Any] = data_files
if isinstance(snake_case_ , snake_case_ ):
snake_case__ : List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case__ : Dict = [dl_manager.iter_files(snake_case_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
snake_case__ : List[Any] = []
for split_name, files in data_files.items():
if isinstance(snake_case_ , snake_case_ ):
snake_case__ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case__ : List[str] = [dl_manager.iter_files(snake_case_ ) for file in files]
splits.append(datasets.SplitGenerator(name=snake_case_ , gen_kwargs={'''files''': files} ) )
return splits
def __magic_name__ ( self : Any , snake_case_ : pa.Table ):
'''simple docstring'''
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
snake_case__ : List[str] = table_cast(snake_case_ , self.config.features.arrow_schema )
return pa_table
def __magic_name__ ( self : Union[str, Any] , snake_case_ : Any ):
'''simple docstring'''
for i, file in enumerate(itertools.chain.from_iterable(snake_case_ ) ):
with open(snake_case_ , '''rb''' ) as f:
snake_case__ : List[str] = pa.Table.from_pandas(pd.read_pickle(snake_case_ ) )
yield i, self._cast_table(snake_case_ )
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'''simple docstring'''
from ... import PretrainedConfig
lowerCAmelCase__ : Dict = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__UpperCAmelCase = """nezha"""
def __init__( self : List[Any] , snake_case_ : List[Any]=2_1_1_2_8 , snake_case_ : Any=7_6_8 , snake_case_ : int=1_2 , snake_case_ : Tuple=1_2 , snake_case_ : Union[str, Any]=3_0_7_2 , snake_case_ : Optional[Any]="gelu" , snake_case_ : str=0.1 , snake_case_ : Dict=0.1 , snake_case_ : Dict=5_1_2 , snake_case_ : Dict=6_4 , snake_case_ : Any=2 , snake_case_ : List[str]=0.0_2 , snake_case_ : Optional[int]=1e-12 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : List[Any]=0 , snake_case_ : str=2 , snake_case_ : int=3 , snake_case_ : Union[str, Any]=True , **snake_case_ : List[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
snake_case__ : Any = vocab_size
snake_case__ : Optional[int] = hidden_size
snake_case__ : int = num_hidden_layers
snake_case__ : Any = num_attention_heads
snake_case__ : Any = hidden_act
snake_case__ : str = intermediate_size
snake_case__ : str = hidden_dropout_prob
snake_case__ : str = attention_probs_dropout_prob
snake_case__ : int = max_position_embeddings
snake_case__ : Dict = max_relative_position
snake_case__ : Any = type_vocab_size
snake_case__ : str = initializer_range
snake_case__ : str = layer_norm_eps
snake_case__ : Optional[int] = classifier_dropout
snake_case__ : int = use_cache
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'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = torch.device('cpu')
def UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
A_ : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Optional[int] = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02] )
def UpperCAmelCase ( a_ , a_ , a_ ) -> Dict:
"""simple docstring"""
A_ : str = dct.pop(__SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = val
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
A_ : List[str] = []
for k in state_dict.keys():
A_ : Any = k
if ".pwconv" in k:
A_ : Optional[int] = k_new.replace(""".pwconv""" , """.point_wise_conv""" )
if ".dwconv" in k:
A_ : int = k_new.replace(""".dwconv""" , """.depth_wise_conv""" )
if ".Proj." in k:
A_ : Tuple = k_new.replace(""".Proj.""" , """.proj.""" )
if "patch_embed" in k_new:
A_ : Dict = k_new.replace("""patch_embed""" , """swiftformer.patch_embed.patch_embedding""" )
if "network" in k_new:
A_ : Tuple = k_new.split(""".""" )
if ls[2].isdigit():
A_ : List[str] = """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:] )
else:
A_ : Any = k_new.replace("""network""" , """swiftformer.encoder.network""" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
A_ : Optional[Any] = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
A_ : Any = 1_0_0_0
A_ : Optional[int] = """huggingface/label-files"""
A_ : Optional[Any] = """imagenet-1k-id2label.json"""
A_ : int = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
A_ : Any = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
A_ : str = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
A_ : str = [3, 3, 6, 4]
A_ : List[str] = [4_8, 5_6, 1_1_2, 2_2_0]
elif swiftformer_name == "swiftformer_s":
A_ : Dict = [3, 3, 9, 6]
A_ : Any = [4_8, 6_4, 1_6_8, 2_2_4]
elif swiftformer_name == "swiftformer_l1":
A_ : Union[str, Any] = [4, 3, 1_0, 5]
A_ : Tuple = [4_8, 9_6, 1_9_2, 3_8_4]
elif swiftformer_name == "swiftformer_l3":
A_ : Any = [4, 4, 1_2, 6]
A_ : Tuple = [6_4, 1_2_8, 3_2_0, 5_1_2]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("""https""" ):
A_ : Tuple = torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE , map_location="""cpu""" , check_hash=__SCREAMING_SNAKE_CASE )
else:
A_ : Dict = torch.load(__SCREAMING_SNAKE_CASE , map_location="""cpu""" )
A_ : List[str] = checkpoint
A_ : Any = create_rename_keys(__SCREAMING_SNAKE_CASE )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load HuggingFace model
A_ : List[Any] = SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )
# prepare test inputs
A_ : Optional[Any] = prepare_img()
A_ : int = ViTImageProcessor.from_pretrained("""preprocessor_config""" )
A_ : Optional[int] = processor(images=__SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
# compare outputs from both models
A_ : Dict = get_expected_output(__SCREAMING_SNAKE_CASE )
A_ : List[str] = hf_model(inputs["""pixel_values"""] ).logits
assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
assert torch.allclose(hf_logits[0, 0:5] , __SCREAMING_SNAKE_CASE , atol=1E-3 )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}" )
hf_model.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCamelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swiftformer_name',
default='swiftformer_xs',
choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
type=str,
help='Name of the SwiftFormer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='./converted_outputs/',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')
UpperCamelCase__ : Tuple = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
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'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = ''''''
lowerCamelCase = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
lowerCamelCase = None # compression type in fsspec. ex: "gzip"
lowerCamelCase = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self , _lowerCamelCase = "" , _lowerCamelCase = None , _lowerCamelCase = None , **_lowerCamelCase ) -> Optional[int]:
super().__init__(self , **_lowerCamelCase )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
A_ : Tuple = fsspec.open(
_lowerCamelCase , mode="""rb""" , protocol=_lowerCamelCase , compression=self.compression , client_kwargs={
"""requote_redirect_url""": False, # see https://github.com/huggingface/datasets/pull/5459
"""trust_env""": True, # Enable reading proxy env variables.
**(target_options or {}).pop("""client_kwargs""" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
A_ : Tuple = os.path.basename(self.file.path.split("""::""" )[0] )
A_ : str = (
self.compressed_name[: self.compressed_name.rindex(""".""" )]
if """.""" in self.compressed_name
else self.compressed_name
)
A_ : Any = None
@classmethod
def UpperCAmelCase_ ( cls , _lowerCamelCase ) -> int:
# compressed file paths are always relative to the archive root
return super()._strip_protocol(_lowerCamelCase ).lstrip("""/""" )
def UpperCAmelCase_ ( self ) -> List[str]:
if self.dir_cache is None:
A_ : Union[str, Any] = {**self.file.fs.info(self.file.path ), """name""": self.uncompressed_name}
A_ : List[Any] = {f["""name"""]: f}
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Tuple:
return self.file.open().read()
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase = "rb" , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ) -> Any:
A_ : Union[str, Any] = self._strip_protocol(_lowerCamelCase )
if mode != "rb":
raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'" )
return self.file.open()
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''bz2'''
lowerCamelCase = '''bz2'''
lowerCamelCase = '''.bz2'''
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''gzip'''
lowerCamelCase = '''gzip'''
lowerCamelCase = '''.gz'''
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''lz4'''
lowerCamelCase = '''lz4'''
lowerCamelCase = '''.lz4'''
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''xz'''
lowerCamelCase = '''xz'''
lowerCamelCase = '''.xz'''
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''zstd'''
lowerCamelCase = '''zstd'''
lowerCamelCase = '''.zst'''
def __init__( self , _lowerCamelCase , _lowerCamelCase = "rb" , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = DEFAULT_BLOCK_SIZE , **_lowerCamelCase , ) -> List[str]:
super().__init__(
fo=_lowerCamelCase , mode=_lowerCamelCase , target_protocol=_lowerCamelCase , target_options=_lowerCamelCase , block_size=_lowerCamelCase , **_lowerCamelCase , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
A_ : Any = self.file.__enter__
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Optional[int]:
A_ : List[str] = file_
def __enter__( self ) -> List[Any]:
self._file.__enter__()
return self
def __exit__( self , *_lowerCamelCase , **_lowerCamelCase ) -> Tuple:
self._file.__exit__(*_lowerCamelCase , **_lowerCamelCase )
def __iter__( self ) -> str:
return iter(self._file )
def UpperCAmelCase_ ( self ) -> List[Any]:
return next(self._file )
def __getattr__( self , _lowerCamelCase ) -> Optional[Any]:
return getattr(self._file , _lowerCamelCase )
def fixed_enter(*_lowerCamelCase , **_lowerCamelCase ):
return WrappedFile(_enter(*_lowerCamelCase , **_lowerCamelCase ) )
A_ : str = fixed_enter
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from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowercase : Union[str, Any] = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = ['''DPTFeatureExtractor''']
__lowercase : Dict = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[Any] = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
__lowercase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _lowerCAmelCase :
def __init__( self : Any , __snake_case : int ):
lowerCamelCase :Union[str, Any] = data
lowerCamelCase :Optional[int] = [0X67_45_23_01, 0Xef_cd_ab_89, 0X98_ba_dc_fe, 0X10_32_54_76, 0Xc3_d2_e1_f0]
@staticmethod
def snake_case ( __snake_case : List[str] , __snake_case : List[Any] ):
return ((n << b) | (n >> (32 - b))) & 0Xff_ff_ff_ff
def snake_case ( self : Optional[int] ):
lowerCamelCase :Union[str, Any] = B'''\x80''' + B'''\x00''' * (63 - (len(self.data ) + 8) % 64)
lowerCamelCase :List[Any] = self.data + padding + struct.pack('''>Q''' , 8 * len(self.data ) )
return padded_data
def snake_case ( self : Optional[Any] ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def snake_case ( self : Optional[Any] , __snake_case : str ):
lowerCamelCase :Union[str, Any] = list(struct.unpack('''>16L''' , __snake_case ) ) + [0] * 64
for i in range(16 , 80 ):
lowerCamelCase :int = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def snake_case ( self : int ):
lowerCamelCase :Optional[Any] = self.padding()
lowerCamelCase :str = self.split_blocks()
for block in self.blocks:
lowerCamelCase :int = self.expand_block(__snake_case )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase :List[Any] = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
lowerCamelCase :int = (b & c) | ((~b) & d)
lowerCamelCase :Any = 0X5a_82_79_99
elif 20 <= i < 40:
lowerCamelCase :Optional[Any] = b ^ c ^ d
lowerCamelCase :Optional[Any] = 0X6e_d9_eb_a1
elif 40 <= i < 60:
lowerCamelCase :List[Any] = (b & c) | (b & d) | (c & d)
lowerCamelCase :List[str] = 0X8f_1b_bc_dc
elif 60 <= i < 80:
lowerCamelCase :Optional[Any] = b ^ c ^ d
lowerCamelCase :Dict = 0Xca_62_c1_d6
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase :Any = (
self.rotate(__snake_case , 5 ) + f + e + k + expanded_block[i] & 0Xff_ff_ff_ff,
a,
self.rotate(__snake_case , 30 ),
c,
d,
)
lowerCamelCase :List[str] = (
self.h[0] + a & 0Xff_ff_ff_ff,
self.h[1] + b & 0Xff_ff_ff_ff,
self.h[2] + c & 0Xff_ff_ff_ff,
self.h[3] + d & 0Xff_ff_ff_ff,
self.h[4] + e & 0Xff_ff_ff_ff,
)
return ("{:08x}" * 5).format(*self.h )
def _lowerCamelCase ( ):
lowerCamelCase :Any = B'''Test String'''
assert SHAaHash(a_).final_hash() == hashlib.shaa(a_).hexdigest() # noqa: S324
def _lowerCamelCase ( ):
lowerCamelCase :str = argparse.ArgumentParser(description='''Process some strings or files''')
parser.add_argument(
'''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument('''--file''' , dest='''input_file''' , help='''Hash contents of a file''')
lowerCamelCase :Optional[Any] = parser.parse_args()
lowerCamelCase :Union[str, Any] = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''') as f:
lowerCamelCase :Tuple = f.read()
else:
lowerCamelCase :Optional[Any] = bytes(a_ , '''utf-8''')
print(SHAaHash(a_).final_hash())
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 166
| 0
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'''simple docstring'''
import sys
from pathlib import Path
UpperCamelCase__ : Dict = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
UpperCamelCase__ : Optional[int] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
UpperCamelCase__ : Union[str, Any] = 'zero2'
UpperCamelCase__ : Union[str, Any] = 'zero3'
UpperCamelCase__ : str = [ZEROa, ZEROa]
def lowerCAmelCase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: Dict , _lowerCamelCase: List[Any] ):
__SCREAMING_SNAKE_CASE : Any = parameterized.to_safe_name("""_""".join(str(lowerCAmelCase_ ) for x in param.args ) )
return F"{func.__name__}_{param_based_name}"
# Cartesian-product of zero stages with models to test
UpperCamelCase__ : List[str] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class _UpperCamelCase ( a__ ):
'''simple docstring'''
@parameterized.expand(lowercase__ , name_func=lowercase__ )
def UpperCamelCase__ ( self : Optional[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
self.run_and_check(
stage=lowercase__ , model=lowercase__ , distributed=lowercase__ , fpaa=lowercase__ , )
@require_torch_multi_gpu
@parameterized.expand(lowercase__ , name_func=lowercase__ )
def UpperCamelCase__ ( self : Union[str, Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
self.run_and_check(
stage=lowercase__ , model=lowercase__ , distributed=lowercase__ , fpaa=lowercase__ , )
@parameterized.expand(lowercase__ , name_func=lowercase__ )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
self.run_and_check(
stage=lowercase__ , model=lowercase__ , distributed=lowercase__ , fpaa=lowercase__ , )
@require_torch_multi_gpu
@parameterized.expand(lowercase__ , name_func=lowercase__ )
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
self.run_and_check(
stage=lowercase__ , model=lowercase__ , distributed=lowercase__ , fpaa=lowercase__ , )
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : Dict ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] = 1_0 , lowerCAmelCase__ : Tuple = True , lowerCAmelCase__ : List[str] = True , lowerCAmelCase__ : Dict = True , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = models[model]
__SCREAMING_SNAKE_CASE : int = self.run_trainer(
stage=lowercase__ , model_name=lowercase__ , eval_steps=lowercase__ , num_train_epochs=1 , distributed=lowercase__ , fpaa=lowercase__ , )
self.do_checks(lowercase__ )
return output_dir
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Union[str, Any] = 1_0 , lowerCAmelCase__ : Optional[int] = 1 , lowerCAmelCase__ : Dict = True , lowerCAmelCase__ : List[str] = True , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = self.get_auto_remove_tmp_dir("""./xxx""" , after=lowercase__ )
__SCREAMING_SNAKE_CASE : str = F"\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(lowercase__ )}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n ".split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__SCREAMING_SNAKE_CASE : Dict = F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
__SCREAMING_SNAKE_CASE : Any = [F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
__SCREAMING_SNAKE_CASE : Tuple = self.get_launcher(lowercase__ )
__SCREAMING_SNAKE_CASE : List[Any] = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(lowercase__ , env=self.get_env() )
return output_dir
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Union[str, Any]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = min(2 , get_gpu_count() ) if distributed else 1
return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()
| 703
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase_ ( _lowerCamelCase: Tuple ):
__SCREAMING_SNAKE_CASE : List[Any] = SwinvaConfig()
__SCREAMING_SNAKE_CASE : List[Any] = swinva_name.split("""_""" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = name_split[1]
if "to" in name_split[3]:
__SCREAMING_SNAKE_CASE : Dict = int(name_split[3][-3:] )
else:
__SCREAMING_SNAKE_CASE : str = int(name_split[3] )
if "to" in name_split[2]:
__SCREAMING_SNAKE_CASE : Optional[Any] = int(name_split[2][-2:] )
else:
__SCREAMING_SNAKE_CASE : Optional[int] = int(name_split[2][6:] )
if model_size == "tiny":
__SCREAMING_SNAKE_CASE : Dict = 96
__SCREAMING_SNAKE_CASE : List[str] = (2, 2, 6, 2)
__SCREAMING_SNAKE_CASE : List[Any] = (3, 6, 12, 24)
elif model_size == "small":
__SCREAMING_SNAKE_CASE : List[str] = 96
__SCREAMING_SNAKE_CASE : int = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE : int = (3, 6, 12, 24)
elif model_size == "base":
__SCREAMING_SNAKE_CASE : int = 1_28
__SCREAMING_SNAKE_CASE : str = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE : Optional[int] = (4, 8, 16, 32)
else:
__SCREAMING_SNAKE_CASE : List[str] = 1_92
__SCREAMING_SNAKE_CASE : Union[str, Any] = (2, 2, 18, 2)
__SCREAMING_SNAKE_CASE : Dict = (6, 12, 24, 48)
if "to" in swinva_name:
__SCREAMING_SNAKE_CASE : int = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__SCREAMING_SNAKE_CASE : int = 2_18_41
__SCREAMING_SNAKE_CASE : str = """huggingface/label-files"""
__SCREAMING_SNAKE_CASE : List[str] = """imagenet-22k-id2label.json"""
__SCREAMING_SNAKE_CASE : List[str] = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__SCREAMING_SNAKE_CASE : List[Any] = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE : Optional[int] = idalabel
__SCREAMING_SNAKE_CASE : str = {v: k for k, v in idalabel.items()}
else:
__SCREAMING_SNAKE_CASE : str = 10_00
__SCREAMING_SNAKE_CASE : Optional[int] = """huggingface/label-files"""
__SCREAMING_SNAKE_CASE : Any = """imagenet-1k-id2label.json"""
__SCREAMING_SNAKE_CASE : Optional[int] = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__SCREAMING_SNAKE_CASE : int = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE : Optional[int] = idalabel
__SCREAMING_SNAKE_CASE : Dict = {v: k for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE : Any = img_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_classes
__SCREAMING_SNAKE_CASE : int = embed_dim
__SCREAMING_SNAKE_CASE : Dict = depths
__SCREAMING_SNAKE_CASE : str = num_heads
__SCREAMING_SNAKE_CASE : int = window_size
return config
def lowerCAmelCase_ ( _lowerCamelCase: int ):
if "patch_embed.proj" in name:
__SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
__SCREAMING_SNAKE_CASE : Optional[int] = """encoder.""" + name
if "attn.proj" in name:
__SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__SCREAMING_SNAKE_CASE : Any = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__SCREAMING_SNAKE_CASE : Optional[int] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__SCREAMING_SNAKE_CASE : Dict = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__SCREAMING_SNAKE_CASE : List[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
__SCREAMING_SNAKE_CASE : Tuple = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
__SCREAMING_SNAKE_CASE : Optional[int] = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
__SCREAMING_SNAKE_CASE : List[str] = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
__SCREAMING_SNAKE_CASE : str = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if name == "norm.weight":
__SCREAMING_SNAKE_CASE : Tuple = """layernorm.weight"""
if name == "norm.bias":
__SCREAMING_SNAKE_CASE : Optional[int] = """layernorm.bias"""
if "head" in name:
__SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""head""" , """classifier""" )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = """swinv2.""" + name
return name
def lowerCAmelCase_ ( _lowerCamelCase: int , _lowerCamelCase: Optional[Any] ):
for key in orig_state_dict.copy().keys():
__SCREAMING_SNAKE_CASE : Optional[Any] = orig_state_dict.pop(_lowerCamelCase )
if "mask" in key:
continue
elif "qkv" in key:
__SCREAMING_SNAKE_CASE : Union[str, Any] = key.split(""".""" )
__SCREAMING_SNAKE_CASE : List[str] = int(key_split[1] )
__SCREAMING_SNAKE_CASE : Dict = int(key_split[3] )
__SCREAMING_SNAKE_CASE : str = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__SCREAMING_SNAKE_CASE : Optional[int] = val[:dim, :]
__SCREAMING_SNAKE_CASE : str = val[dim : dim * 2, :]
__SCREAMING_SNAKE_CASE : Dict = val[-dim:, :]
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = val[:dim]
__SCREAMING_SNAKE_CASE : int = val[
dim : dim * 2
]
__SCREAMING_SNAKE_CASE : int = val[-dim:]
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = val
return orig_state_dict
def lowerCAmelCase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = timm.create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
__SCREAMING_SNAKE_CASE : int = get_swinva_config(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = SwinvaForImageClassification(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[int] = convert_state_dict(timm_model.state_dict() , _lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_""" , """-""" ) ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processor(images=_lowerCamelCase , return_tensors="""pt""" )
__SCREAMING_SNAKE_CASE : int = timm_model(inputs["""pixel_values"""] )
__SCREAMING_SNAKE_CASE : Dict = model(**_lowerCamelCase ).logits
assert torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1E-3 )
print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCamelCase )
model.push_to_hub(
repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase ) , organization="""nandwalritik""" , commit_message="""Add model""" , )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
UpperCamelCase__ : Optional[int] = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 178
| 0
|
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCAmelCase : str ='src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase : int =direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase : int =re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCAmelCase : Optional[Any] =re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase : Optional[int] =re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase : Tuple =[
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , __snake_case )
return [m.group(0 ) for m in matches]
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : List[str] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
__SCREAMING_SNAKE_CASE : List[str] = {
config.replace('''Config''' , '''''' ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
__SCREAMING_SNAKE_CASE : List[str] = collections.defaultdict(__snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = collections.defaultdict(__snake_case )
__SCREAMING_SNAKE_CASE : Optional[int] = collections.defaultdict(__snake_case )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(__snake_case ):
__SCREAMING_SNAKE_CASE : List[str] = None
if _re_tf_models.match(__snake_case ) is not None:
__SCREAMING_SNAKE_CASE : str = tf_models
__SCREAMING_SNAKE_CASE : Tuple = _re_tf_models.match(__snake_case ).groups()[0]
elif _re_flax_models.match(__snake_case ) is not None:
__SCREAMING_SNAKE_CASE : str = flax_models
__SCREAMING_SNAKE_CASE : Optional[Any] = _re_flax_models.match(__snake_case ).groups()[0]
elif _re_pt_models.match(__snake_case ) is not None:
__SCREAMING_SNAKE_CASE : Dict = pt_models
__SCREAMING_SNAKE_CASE : int = _re_pt_models.match(__snake_case ).groups()[0]
if lookup_dict is not None:
while len(__snake_case ) > 0:
if attr_name in model_prefix_to_model_type:
__SCREAMING_SNAKE_CASE : List[Any] = True
break
# Try again after removing the last word in the name
__SCREAMING_SNAKE_CASE : str = ''''''.join(camel_case_split(__snake_case )[:-1] )
__SCREAMING_SNAKE_CASE : List[str] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
__SCREAMING_SNAKE_CASE : List[str] = list(__snake_case )
all_models.sort()
__SCREAMING_SNAKE_CASE : List[Any] = {'''model_type''': all_models}
__SCREAMING_SNAKE_CASE : Union[str, Any] = [pt_models[t] for t in all_models]
__SCREAMING_SNAKE_CASE : Dict = [tf_models[t] for t in all_models]
__SCREAMING_SNAKE_CASE : int = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
__SCREAMING_SNAKE_CASE : Dict = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
__SCREAMING_SNAKE_CASE : Optional[int] = '''AutoProcessor'''
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
__SCREAMING_SNAKE_CASE : int = '''AutoTokenizer'''
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
__SCREAMING_SNAKE_CASE : Optional[int] = '''AutoFeatureExtractor'''
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
__SCREAMING_SNAKE_CASE : Dict = '''AutoTokenizer'''
__SCREAMING_SNAKE_CASE : Any = [processors[t] for t in all_models]
return pd.DataFrame(__snake_case )
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : List[Any] = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
__SCREAMING_SNAKE_CASE : Dict = [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
__SCREAMING_SNAKE_CASE : int = [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
# Loop through all three frameworks
for module, cls, mapping in zip(__snake_case , __snake_case , __snake_case ):
# The type of pipeline may not exist in this framework
if not hasattr(__snake_case , __snake_case ):
continue
# First extract all model_names
__SCREAMING_SNAKE_CASE : Tuple = []
for name in getattr(__snake_case , __snake_case ).values():
if isinstance(__snake_case , __snake_case ):
model_names.append(__snake_case )
else:
model_names.extend(list(__snake_case ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[Any] = get_frameworks_table()
__SCREAMING_SNAKE_CASE : int = Dataset.from_pandas(__snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = hf_hub_download(
'''huggingface/transformers-metadata''' , '''pipeline_tags.json''' , repo_type='''dataset''' , token=__snake_case )
__SCREAMING_SNAKE_CASE : str = Dataset.from_json(__snake_case )
__SCREAMING_SNAKE_CASE : Dict = {
tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
for i in range(len(__snake_case ) )
}
__SCREAMING_SNAKE_CASE : int = update_pipeline_and_auto_class_table(__snake_case )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
__SCREAMING_SNAKE_CASE : Optional[Any] = sorted(table.keys() )
__SCREAMING_SNAKE_CASE : str = pd.DataFrame(
{
'''model_class''': model_classes,
'''pipeline_tag''': [table[m][0] for m in model_classes],
'''auto_class''': [table[m][1] for m in model_classes],
} )
__SCREAMING_SNAKE_CASE : Any = Dataset.from_pandas(__snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(__snake_case , '''frameworks.json''' ) )
tags_dataset.to_json(os.path.join(__snake_case , '''pipeline_tags.json''' ) )
if commit_sha is not None:
__SCREAMING_SNAKE_CASE : Tuple = (
F'''Update with commit {commit_sha}\n\nSee: '''
F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
)
else:
__SCREAMING_SNAKE_CASE : str = '''Update'''
upload_folder(
repo_id='''huggingface/transformers-metadata''' , folder_path=__snake_case , repo_type='''dataset''' , token=__snake_case , commit_message=__snake_case , )
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : Dict = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
__SCREAMING_SNAKE_CASE : int = transformers_module.pipelines.SUPPORTED_TASKS
__SCREAMING_SNAKE_CASE : Dict = []
for key in pipeline_tasks:
if key not in in_table:
__SCREAMING_SNAKE_CASE : int = pipeline_tasks[key]['''pt''']
if isinstance(__snake_case , (list, tuple) ):
__SCREAMING_SNAKE_CASE : int = model[0]
__SCREAMING_SNAKE_CASE : int = model.__name__
if model not in in_table.values():
missing.append(__snake_case )
if len(__snake_case ) > 0:
__SCREAMING_SNAKE_CASE : List[Any] = ''', '''.join(__snake_case )
raise ValueError(
'''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
F'''`utils/update_metadata.py`: {msg}. Please add them!''' )
if __name__ == "__main__":
__lowerCAmelCase : int =argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
__lowerCAmelCase : Tuple =parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 696
|
from ...configuration_utils import PretrainedConfig
class UpperCamelCase ( snake_case__ ):
__UpperCamelCase = """bert-generation"""
def __init__( self : Tuple ,_lowerCAmelCase : Union[str, Any]=50_358 ,_lowerCAmelCase : List[Any]=1_024 ,_lowerCAmelCase : str=24 ,_lowerCAmelCase : Any=16 ,_lowerCAmelCase : Any=4_096 ,_lowerCAmelCase : Any="gelu" ,_lowerCAmelCase : Optional[Any]=0.1 ,_lowerCAmelCase : Optional[Any]=0.1 ,_lowerCAmelCase : Optional[Any]=512 ,_lowerCAmelCase : Optional[Any]=0.0_2 ,_lowerCAmelCase : Union[str, Any]=1E-12 ,_lowerCAmelCase : Optional[int]=0 ,_lowerCAmelCase : Optional[int]=2 ,_lowerCAmelCase : Optional[Any]=1 ,_lowerCAmelCase : Any="absolute" ,_lowerCAmelCase : str=True ,**_lowerCAmelCase : List[Any] ,):
"""simple docstring"""
super().__init__(pad_token_id=_lowerCAmelCase ,bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = hidden_act
__snake_case = intermediate_size
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = initializer_range
__snake_case = layer_norm_eps
__snake_case = position_embedding_type
__snake_case = use_cache
| 524
| 0
|
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
__lowerCamelCase : Tuple = True
from torch.cuda.amp import autocast
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class a__ :
A = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
A = field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
A = field(
default=A__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
A = field(
default=A__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
A = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
A = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
A = field(
default=0.99_9995 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def _snake_case ( lowerCAmelCase : ModelArguments , lowerCAmelCase : TrainingArguments ):
"""simple docstring"""
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
SCREAMING_SNAKE_CASE_ : Optional[int] = logging.WARNING
if model_args.verbose_logging:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
SCREAMING_SNAKE_CASE_ : Any = logging.INFO
logger.setLevel(lowerCAmelCase )
@dataclass
class a__ :
A = field(
default=A__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
A = field(
default=A__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
A = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
A = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
A = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
A = field(
default=A__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
A = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
A = field(
default=A__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
A = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class a__ :
A = 42
A = 42
A = "longest"
A = None
A = None
def __call__( self : int,_A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.feature_extractor.pad(
_A,max_length=self.max_length,padding=self.padding,pad_to_multiple_of=self.pad_to_multiple_of,return_tensors="pt",)
SCREAMING_SNAKE_CASE_ : str = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
SCREAMING_SNAKE_CASE_ : List[Any] = batch["input_values"].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
SCREAMING_SNAKE_CASE_ : List[Any] = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
SCREAMING_SNAKE_CASE_ : Any = torch.zeros(
(batch_size, mask_indices_seq_length),dtype=torch.long,device=batch["input_values"].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
SCREAMING_SNAKE_CASE_ : List[str] = 1
SCREAMING_SNAKE_CASE_ : Union[str, Any] = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
SCREAMING_SNAKE_CASE_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length),self.model.config.mask_time_prob,self.model.config.mask_time_length,attention_mask=_A,min_masks=2,)
return batch
class a__ ( A__ ):
def __init__( self : List[str],*_A : Tuple,_A : Optional[Any]=1,_A : str=0,_A : List[str]=1.0,**_A : Union[str, Any] ):
"""simple docstring"""
super().__init__(*_A,**_A )
SCREAMING_SNAKE_CASE_ : List[Any] = 0
SCREAMING_SNAKE_CASE_ : str = max_gumbel_temp
SCREAMING_SNAKE_CASE_ : str = min_gumbel_temp
SCREAMING_SNAKE_CASE_ : List[str] = gumbel_temp_decay
def __UpperCamelCase ( self : Optional[Any],_A : nn.Module,_A : Dict[str, Union[torch.Tensor, Any]] ):
"""simple docstring"""
model.train()
SCREAMING_SNAKE_CASE_ : int = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
SCREAMING_SNAKE_CASE_ : int = self.compute_loss(_A,_A )
else:
SCREAMING_SNAKE_CASE_ : str = self.compute_loss(_A,_A )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
SCREAMING_SNAKE_CASE_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
SCREAMING_SNAKE_CASE_ : Any = loss.sum() / (inputs["mask_time_indices"]).sum()
else:
raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
SCREAMING_SNAKE_CASE_ : Tuple = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,self.min_gumbel_temp ) )
return loss.detach()
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = parser.parse_args_into_dataclasses()
configure_logger(lowerCAmelCase , lowerCAmelCase )
# Downloading and loading a dataset from the hub.
SCREAMING_SNAKE_CASE_ : Union[str, Any] = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
SCREAMING_SNAKE_CASE_ : int = DatasetDict()
SCREAMING_SNAKE_CASE_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]' , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE_ : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
SCREAMING_SNAKE_CASE_ : int = DatasetDict()
SCREAMING_SNAKE_CASE_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
SCREAMING_SNAKE_CASE_ : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=lowerCAmelCase )
def prepare_dataset(lowerCAmelCase : Union[str, Any] ):
# check that all files have the correct sampling rate
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
SCREAMING_SNAKE_CASE_ : List[str] = datasets.map(
lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names )
# filter audio files that are too long
SCREAMING_SNAKE_CASE_ : List[Any] = vectorized_datasets.filter(
lambda lowerCAmelCase : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(lowerCAmelCase : Union[str, Any] ):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
SCREAMING_SNAKE_CASE_ : int = vectorized_datasets.map(
lowerCAmelCase , batched=lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["train"].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
SCREAMING_SNAKE_CASE_ : Optional[int] = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
" ``config.feat_extract_norm='layer'" )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = WavaVecaForPreTraining(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = DataCollatorForWavaVecaPretraining(model=lowerCAmelCase , feature_extractor=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = WavaVecaPreTrainer(
model=lowerCAmelCase , data_collator=lowerCAmelCase , args=lowerCAmelCase , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=lowerCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 316
|
from __future__ import annotations
import math
import random
from typing import Any
class a__ :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : list[Any] = []
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : int = 0
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
return self.head == self.tail
def __UpperCamelCase ( self : Any,_A : Any ):
"""simple docstring"""
self.data.append(_A )
SCREAMING_SNAKE_CASE_ : int = self.tail + 1
def __UpperCamelCase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = self.data[self.head]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.head + 1
return ret
def __UpperCamelCase ( self : int ):
"""simple docstring"""
return self.tail - self.head
def __UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
print(self.data )
print("**************" )
print(self.data[self.head : self.tail] )
class a__ :
def __init__( self : Dict,_A : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = data
SCREAMING_SNAKE_CASE_ : MyNode | None = None
SCREAMING_SNAKE_CASE_ : MyNode | None = None
SCREAMING_SNAKE_CASE_ : int = 1
def __UpperCamelCase ( self : str ):
"""simple docstring"""
return self.data
def __UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
return self.left
def __UpperCamelCase ( self : Dict ):
"""simple docstring"""
return self.right
def __UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
return self.height
def __UpperCamelCase ( self : Dict,_A : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = data
def __UpperCamelCase ( self : int,_A : MyNode | None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = node
def __UpperCamelCase ( self : int,_A : MyNode | None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = node
def __UpperCamelCase ( self : Dict,_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = height
def _snake_case ( lowerCAmelCase : MyNode | None ):
"""simple docstring"""
if node is None:
return 0
return node.get_height()
def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
if a > b:
return a
return b
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
print("left rotation node:" , node.get_data() )
SCREAMING_SNAKE_CASE_ : Optional[Any] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(lowerCAmelCase )
return ret
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
print("right rotation node:" , node.get_data() )
SCREAMING_SNAKE_CASE_ : Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(lowerCAmelCase )
return ret
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = node.get_left()
assert left_child is not None
node.set_left(left_rotation(lowerCAmelCase ) )
return right_rotation(lowerCAmelCase )
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = node.get_right()
assert right_child is not None
node.set_right(right_rotation(lowerCAmelCase ) )
return left_rotation(lowerCAmelCase )
def _snake_case ( lowerCAmelCase : MyNode | None , lowerCAmelCase : Any ):
"""simple docstring"""
if node is None:
return MyNode(lowerCAmelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , lowerCAmelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
SCREAMING_SNAKE_CASE_ : str = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
SCREAMING_SNAKE_CASE_ : Optional[Any] = right_rotation(lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ : Tuple = lr_rotation(lowerCAmelCase )
else:
node.set_right(insert_node(node.get_right() , lowerCAmelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
SCREAMING_SNAKE_CASE_ : Optional[Any] = node.get_right()
assert right_child is not None
if data < right_child.get_data():
SCREAMING_SNAKE_CASE_ : Optional[Any] = rl_rotation(lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ : List[Any] = left_rotation(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(lowerCAmelCase )
return node
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
while True:
SCREAMING_SNAKE_CASE_ : List[str] = root.get_right()
if right_child is None:
break
SCREAMING_SNAKE_CASE_ : Any = right_child
return root.get_data()
def _snake_case ( lowerCAmelCase : MyNode ):
"""simple docstring"""
while True:
SCREAMING_SNAKE_CASE_ : List[Any] = root.get_left()
if left_child is None:
break
SCREAMING_SNAKE_CASE_ : List[Any] = left_child
return root.get_data()
def _snake_case ( lowerCAmelCase : MyNode , lowerCAmelCase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = root.get_left()
SCREAMING_SNAKE_CASE_ : str = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
SCREAMING_SNAKE_CASE_ : Tuple = get_left_most(lowerCAmelCase )
root.set_data(lowerCAmelCase )
root.set_right(del_node(lowerCAmelCase , lowerCAmelCase ) )
elif left_child is not None:
SCREAMING_SNAKE_CASE_ : str = left_child
elif right_child is not None:
SCREAMING_SNAKE_CASE_ : Optional[int] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("No such data" )
return root
else:
root.set_left(del_node(lowerCAmelCase , lowerCAmelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(lowerCAmelCase , lowerCAmelCase ) )
if get_height(lowerCAmelCase ) - get_height(lowerCAmelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
SCREAMING_SNAKE_CASE_ : str = left_rotation(lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ : int = rl_rotation(lowerCAmelCase )
elif get_height(lowerCAmelCase ) - get_height(lowerCAmelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
SCREAMING_SNAKE_CASE_ : Optional[int] = right_rotation(lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ : Tuple = lr_rotation(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : str = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(lowerCAmelCase )
return root
class a__ :
def __init__( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : MyNode | None = None
def __UpperCamelCase ( self : Dict ):
"""simple docstring"""
return get_height(self.root )
def __UpperCamelCase ( self : Optional[int],_A : Any ):
"""simple docstring"""
print("insert:" + str(_A ) )
SCREAMING_SNAKE_CASE_ : Optional[int] = insert_node(self.root,_A )
def __UpperCamelCase ( self : Any,_A : Any ):
"""simple docstring"""
print("delete:" + str(_A ) )
if self.root is None:
print("Tree is empty!" )
return
SCREAMING_SNAKE_CASE_ : str = del_node(self.root,_A )
def __str__( self : int,): # a level traversale, gives a more intuitive look on the tree
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ""
SCREAMING_SNAKE_CASE_ : str = MyQueue()
q.push(self.root )
SCREAMING_SNAKE_CASE_ : str = self.get_height()
if layer == 0:
return output
SCREAMING_SNAKE_CASE_ : Any = 0
while not q.is_empty():
SCREAMING_SNAKE_CASE_ : str = q.pop()
SCREAMING_SNAKE_CASE_ : List[Any] = " " * int(math.pow(2,layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(_A )
q.push(_A )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
SCREAMING_SNAKE_CASE_ : Optional[Any] = cnt + 1
for i in range(100 ):
if cnt == math.pow(2,_A ) - 1:
SCREAMING_SNAKE_CASE_ : List[Any] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def _snake_case ( ):
"""simple docstring"""
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__lowerCamelCase : Union[str, Any] = AVLtree()
__lowerCamelCase : List[str] = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 316
| 1
|
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
if not len(_lowercase ) == len(_lowercase ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('''Both a & b of two equations can\'t be zero.''' )
# Extract the coefficients
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : int = equationa
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : str = equationa
# Calculate the determinants of the matrices
UpperCAmelCase_ : Union[str, Any] = aa * ba - aa * ba
UpperCAmelCase_ : Tuple = ca * ba - ca * ba
UpperCAmelCase_ : Tuple = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('''Infinite solutions. (Consistent system)''' )
else:
raise ValueError('''No solution. (Inconsistent system)''' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
UpperCAmelCase_ : Optional[int] = determinant_x / determinant
UpperCAmelCase_ : Tuple = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 30
|
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_lowercase )
UpperCAmelCase_ : Any = ''''''.join(bin(_lowercase )[2:].zfill(8 ) for byte in data )
UpperCAmelCase_ : Any = len(_lowercase ) % 6 != 0
if padding_needed:
# The padding that will be added later
UpperCAmelCase_ : Union[str, Any] = B'''=''' * ((6 - len(_lowercase ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_lowercase ) % 6)
else:
UpperCAmelCase_ : int = B''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_lowercase ) , 6 ) ).encode()
+ padding
)
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ) and not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Tuple = (
'''argument should be a bytes-like object or ASCII string, '''
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_lowercase )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_lowercase , _lowercase ):
try:
UpperCAmelCase_ : Any = encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
UpperCAmelCase_ : str = encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_lowercase ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
UpperCAmelCase_ : List[Any] = encoded_data[:-padding]
UpperCAmelCase_ : List[Any] = ''''''.join(
bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
UpperCAmelCase_ : Tuple = ''''''.join(
bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )
UpperCAmelCase_ : str = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_lowercase ) , 8 )
]
return bytes(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 1
|
"""simple docstring"""
import re
import string
import numpy as np
import datasets
lowerCAmelCase : Optional[Any] = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
lowerCAmelCase : Tuple = """
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
25.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
50.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
75.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results[\"exact_match\"], 1))
100.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
>>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
33.3
"""
lowerCAmelCase : Dict = """
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , reference_urls=[] , )
def _lowerCAmelCase ( self , _a , _a , _a=None , _a=False , _a=False , _a=False , ):
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
lowerCamelCase = np.array([re.sub(_a , """""" , _a ) for x in predictions] )
lowerCamelCase = np.array([re.sub(_a , """""" , _a ) for x in references] )
else:
lowerCamelCase = np.asarray(_a )
lowerCamelCase = np.asarray(_a )
if ignore_case:
lowerCamelCase = np.char.lower(_a )
lowerCamelCase = np.char.lower(_a )
if ignore_punctuation:
lowerCamelCase = string.punctuation.maketrans("""""" , """""" , string.punctuation )
lowerCamelCase = np.char.translate(_a , table=_a )
lowerCamelCase = np.char.translate(_a , table=_a )
if ignore_numbers:
lowerCamelCase = string.digits.maketrans("""""" , """""" , string.digits )
lowerCamelCase = np.char.translate(_a , table=_a )
lowerCamelCase = np.char.translate(_a , table=_a )
lowerCamelCase = predictions == references
return {"exact_match": np.mean(_a ) * 100}
| 705
|
"""simple docstring"""
def a__ ( snake_case__ = 1_00_00_00 ) -> int:
lowerCamelCase = 1
lowerCamelCase = 1
lowerCamelCase = {1: 1}
for inputa in range(2 , snake_case__ ):
lowerCamelCase = 0
lowerCamelCase = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
lowerCamelCase = (3 * number) + 1
counter += 1
if inputa not in counters:
lowerCamelCase = counter
if counter > pre_counter:
lowerCamelCase = inputa
lowerCamelCase = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 533
| 0
|
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCAmelCase__ ( _a : int ):
snake_case_ : Optional[Any] = os.path.join(args.tf_model_dir , "parameters.json" )
snake_case_ : Optional[int] = json.loads(open(_a ).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" ):
snake_case_ : int = args.output + ".pt"
snake_case_ : Optional[int] = OrderedDict()
with tf.device("/CPU:0" ):
snake_case_ : int = tf.train.load_checkpoint(args.tf_model_dir )
snake_case_ : List[Any] = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
snake_case_ : Union[str, Any] = reader.get_tensor(_a ).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" ):
snake_case_ : str = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
snake_case_ : str = 8
snake_case_ : Union[str, Any] = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
snake_case_ : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Union[str, Any] = torch.tensor(_a )
elif key_name.startswith("model/moe" ):
snake_case_ : List[str] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
snake_case_ : int = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
snake_case_ : Optional[int] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Tuple = torch.tensor(_a )
elif key_name.endswith("/softmlp/kernel" ):
snake_case_ : Any = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
snake_case_ : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Dict = torch.tensor(_a )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
snake_case_ : List[str] = key_name[-9:-7]
for i in range(16 ):
snake_case_ : Union[str, Any] = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
snake_case_ : Any = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
snake_case_ : Tuple = torch.tensor(_a )
elif key_name.startswith("model/mlp" ):
snake_case_ : str = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
snake_case_ : int = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
snake_case_ : List[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : List[Any] = torch.tensor(_a )
elif key_name.endswith("/p1/bias" ):
snake_case_ : Optional[Any] = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
snake_case_ : Dict = vnp.copy() # same because it is one dimensional
snake_case_ : Optional[int] = torch.tensor(_a )
elif key_name.endswith("/p2/kernel" ):
snake_case_ : int = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
snake_case_ : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Tuple = torch.tensor(_a )
elif key_name.endswith("/p2/bias" ):
snake_case_ : str = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
snake_case_ : Optional[Any] = vnp.copy() # same because it is one dimensional
snake_case_ : Union[str, Any] = torch.tensor(_a )
elif key_name.startswith("model/ln" ):
snake_case_ : List[Any] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
snake_case_ : List[str] = "model.blocks.%d.feed_forward.norm.bias" % player
snake_case_ : List[Any] = vnp.copy() # same because it is one dimensional
snake_case_ : List[str] = torch.tensor(_a )
elif key_name.endswith("/g" ):
snake_case_ : List[Any] = "model.blocks.%d.feed_forward.norm.weight" % player
snake_case_ : Union[str, Any] = vnp.copy() # same because it is one dimensional
snake_case_ : List[Any] = torch.tensor(_a )
elif key_name.startswith("model/att" ):
snake_case_ : List[Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
snake_case_ : Dict = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
snake_case_ : Any = state[:, 0, :, :]
snake_case_ : List[str] = state[:, 1, :, :]
snake_case_ : str = state[:, 2, :, :]
snake_case_ : int = (
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
snake_case_ : int = (
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
snake_case_ : Any = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ : str = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
snake_case_ : int = torch.tensor(_a )
snake_case_ : Any = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
snake_case_ : Tuple = torch.tensor(_a )
snake_case_ : List[Any] = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
snake_case_ : Tuple = torch.tensor(_a )
elif key_name.endswith("/o/kernel" ):
snake_case_ : List[Any] = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
snake_case_ : int = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Dict = torch.tensor(_a )
elif key_name.startswith("model/an" ):
snake_case_ : Optional[Any] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
snake_case_ : Dict = "model.blocks.%d.self_attn.norm.bias" % player
snake_case_ : str = vnp.copy() # same because it is one dimensional
snake_case_ : Union[str, Any] = torch.tensor(_a )
elif key_name.endswith("/g" ):
snake_case_ : Union[str, Any] = "model.blocks.%d.self_attn.norm.weight" % player
snake_case_ : int = vnp.copy() # same because it is one dimensional
snake_case_ : Union[str, Any] = torch.tensor(_a )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
snake_case_ : Union[str, Any] = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
snake_case_ : Optional[Any] = "model.%s.weight" % nlayer
snake_case_ : Dict = vnp.copy() # same in embedded
snake_case_ : str = torch.tensor(_a )
if key_name.startswith("model/wte" ):
snake_case_ : str = "lm_head.weight"
snake_case_ : Optional[int] = vnp.copy() # same in embedded
snake_case_ : Any = torch.tensor(_a )
elif key_name.startswith("model/wob" ):
snake_case_ : Tuple = "final_logits_bias"
snake_case_ : Tuple = vnp.copy() # same in embedded
snake_case_ : Optional[Any] = state.reshape((1, -1) )
snake_case_ : Dict = torch.tensor(_a )
elif key_name == "model/dense/kernel":
snake_case_ : str = "model.last_project.weight"
snake_case_ : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ : Any = torch.tensor(_a )
elif key_name == "model/dense_1/bias":
snake_case_ : List[Any] = "model.last_project.bias"
snake_case_ : Dict = vnp.copy() # same because it is one dimensional
snake_case_ : Dict = torch.tensor(_a )
torch.save(_a , args.output )
if __name__ == "__main__":
lowercase : str = 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''')
lowercase : Optional[int] = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 568
|
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
def A_ ( self , snake_case ):
'''simple docstring'''
with open(snake_case , encoding="utf-8" ) as input_file:
UpperCAmelCase : Dict = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
UpperCAmelCase : Tuple = input_file.read()
UpperCAmelCase : List[Any] = regexp.search(snake_case )
return match
def A_ ( self , snake_case ):
'''simple docstring'''
with open(snake_case , encoding="utf-8" ) as input_file:
UpperCAmelCase : List[str] = re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
UpperCAmelCase : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
UpperCAmelCase : str = regexp.finditer(snake_case )
UpperCAmelCase : Union[str, Any] = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = Path("./datasets" )
UpperCAmelCase : Optional[int] = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(snake_case ) ):
raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}" )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = Path("./datasets" )
UpperCAmelCase : Any = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(snake_case ) ):
raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead." )
| 679
| 0
|
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__A : List[Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__A : List[Any] = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def _lowercase ( self: List[Any], _lowercase: Any):
'''simple docstring'''
return FSMTTokenizer.from_pretrained(_lowercase)
def _lowercase ( self: int, _lowercase: str):
'''simple docstring'''
__lowerCAmelCase = FSMTForConditionalGeneration.from_pretrained(_lowercase).to(_lowercase)
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
])
@slow
def _lowercase ( self: Any, _lowercase: Union[str, Any], _lowercase: Dict):
'''simple docstring'''
__lowerCAmelCase = f'''facebook/wmt19-{pair}'''
__lowerCAmelCase = self.get_tokenizer(_lowercase)
__lowerCAmelCase = self.get_model(_lowercase)
__lowerCAmelCase = bleu_data[pair]["""src"""]
__lowerCAmelCase = bleu_data[pair]["""tgt"""]
__lowerCAmelCase = tokenizer(_lowercase, return_tensors="""pt""", truncation=_lowercase, padding="""longest""").to(_lowercase)
__lowerCAmelCase = model.generate(
input_ids=batch.input_ids, num_beams=8, )
__lowerCAmelCase = tokenizer.batch_decode(
_lowercase, skip_special_tokens=_lowercase, clean_up_tokenization_spaces=_lowercase)
__lowerCAmelCase = calculate_bleu(_lowercase, _lowercase)
print(_lowercase)
self.assertGreaterEqual(scores["""bleu"""], _lowercase)
| 334
|
from __future__ import annotations
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> bool:
'''simple docstring'''
if len(UpperCamelCase__ ) == 0:
return False
__lowerCAmelCase = len(UpperCamelCase__ ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , UpperCamelCase__ )
else:
return binary_search(a_list[midpoint + 1 :] , UpperCamelCase__ )
if __name__ == "__main__":
__A : Union[str, Any] = input("Enter numbers separated by comma:\n").strip()
__A : Optional[int] = [int(item.strip()) for item in user_input.split(",")]
__A : Tuple = int(input("Enter the number to be found in the list:\n").strip())
__A : Dict = "" if binary_search(sequence, target) else "not "
print(f"""{target} was {not_str}found in {sequence}""")
| 334
| 1
|
'''simple docstring'''
__A : List[Any] = 8.3_14_45_98
def UpperCAmelCase ( lowerCamelCase_ :float , lowerCamelCase_ :float ):
'''simple docstring'''
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol""" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
__A : Union[str, Any] = 300
__A : Optional[Any] = 28
__A : Union[str, Any] = rms_speed_of_molecule(temperature, molar_mass)
print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')
| 334
|
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :Any , lowerCamelCase_ :Dict=None ):
'''simple docstring'''
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
snake_case_ : List[str] = nn.Parameter(lowerCamelCase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
snake_case_ : Optional[Any] = nn.Parameter(lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :Tuple , lowerCamelCase_ :Any , lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
# set torch weights for 1-to-1 comparison
snake_case_ : List[str] = np.asarray(weights[0] )
snake_case_ : Dict = np.asarray(weights[1] )
snake_case_ : Any = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase_ ).view(-1 , lowerCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Union[str, Any] ):
'''simple docstring'''
# set torch weights for 1-to-1 comparison
snake_case_ : Tuple = np.asarray(weights[0] )
snake_case_ : List[Any] = np.asarray(weights[1] )
snake_case_ : Dict = np.asarray(weights[2] )
snake_case_ : Optional[Any] = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase_ ).view(-1 , lowerCamelCase_ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int ):
'''simple docstring'''
# layernorm 1
snake_case_ : str = weights[0][0][0]
snake_case_ : Tuple = np.asarray(layer_norm_a[0] )
snake_case_ : Optional[int] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# lsh weights + output
snake_case_ : Dict = weights[0][1]
if len(lowerCamelCase_ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase_ , torch_block.attention , lowerCamelCase_ )
else:
set_layer_weights_in_torch_local(lowerCamelCase_ , torch_block.attention , lowerCamelCase_ )
# intermediate weighs
snake_case_ : Tuple = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase_ ) == 4:
snake_case_ : Dict = intermediate_weights[2]
# layernorm 2
snake_case_ : Tuple = np.asarray(intermediate_weights[0][0] )
snake_case_ : List[str] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# intermediate dense
snake_case_ : Optional[Any] = np.asarray(intermediate_weights[1][0] )
snake_case_ : str = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
# intermediate out
snake_case_ : Optional[int] = np.asarray(intermediate_weights[4][0] )
snake_case_ : List[Any] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Union[str, Any] ):
'''simple docstring'''
# reformer model
snake_case_ : List[Any] = torch_model.reformer
# word embeds
snake_case_ : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase_ ) , )
if isinstance(weights[3] , lowerCamelCase_ ):
snake_case_ : Tuple = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
snake_case_ : List[Any] = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
snake_case_ : Any = nn.Parameter(torch.tensor(lowerCamelCase_ ) )
snake_case_ : Tuple = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
snake_case_ : Optional[int] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# output layer norm
snake_case_ : str = np.asarray(weights[7][0] )
snake_case_ : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase_ ) , torch.tensor(lowerCamelCase_ ) , )
# output embeddings
snake_case_ : Dict = np.asarray(weights[9][0] )
snake_case_ : Optional[int] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase_ ) , )
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int ):
'''simple docstring'''
# Initialise PyTorch model
snake_case_ : Dict = ReformerConfig.from_json_file(lowerCamelCase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case_ : Dict = ReformerModelWithLMHead(lowerCamelCase_ )
with open(lowerCamelCase_ , """rb""" ) as f:
snake_case_ : Tuple = pickle.load(lowerCamelCase_ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase_ , lowerCamelCase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowerCamelCase_ )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained Reformer 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 : Dict = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 334
| 1
|
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_snake_case : Optional[Any] = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_snake_case : List[str] = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
_snake_case : Optional[Any] = BeautifulSoup(res.text, """html.parser""")
_snake_case : Tuple = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"https://google.com{link.get('href')}")
| 714
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a ( _SCREAMING_SNAKE_CASE : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(_SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
_SCREAMING_SNAKE_CASE = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
_SCREAMING_SNAKE_CASE = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(_SCREAMING_SNAKE_CASE ) )
# The ratio of the area for circle to square is pi/4.
_SCREAMING_SNAKE_CASE = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def _a ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Callable[[float], float] , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 , ):
return mean(
function_to_integrate(uniform(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) for _ in range(_SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)
def _a ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ):
def identity_function(_SCREAMING_SNAKE_CASE : float ) -> float:
return x
_SCREAMING_SNAKE_CASE = area_under_curve_estimator(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("******************" )
def _a ( _SCREAMING_SNAKE_CASE : int ):
def function_to_integrate(_SCREAMING_SNAKE_CASE : float ) -> float:
return sqrt(4.0 - x * x )
_SCREAMING_SNAKE_CASE = area_under_curve_estimator(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 0.0 , 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 493
| 0
|
'''simple docstring'''
def snake_case_ ( __snake_case : Tuple = 100) -> Union[str, Any]:
lowerCAmelCase_ = (n * (n + 1) // 2) ** 2
lowerCAmelCase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 274
|
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10
| 0
|
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
#
########################################################################
UpperCAmelCase = 16
UpperCAmelCase = 32
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 16 )-> List[Any]:
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('''bert-base-cased''' )
snake_case_ = DatasetDict(
{
'''train''': dataset['''train'''].select(SCREAMING_SNAKE_CASE ),
'''validation''': dataset['''train'''].select(SCREAMING_SNAKE_CASE ),
'''test''': dataset['''validation'''],
} )
def tokenize_function(SCREAMING_SNAKE_CASE ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ = datasets.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(SCREAMING_SNAKE_CASE ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ = 16
elif accelerator.mixed_precision != "no":
snake_case_ = 8
else:
snake_case_ = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
# Instantiate dataloaders.
snake_case_ = DataLoader(
tokenized_datasets['''train'''] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
snake_case_ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
snake_case_ = DataLoader(
tokenized_datasets['''test'''] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader, test_dataloader
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> Optional[int]:
"""simple docstring"""
snake_case_ = []
# Download the dataset
snake_case_ = load_dataset('''glue''' , '''mrpc''' )
# Create our splits
snake_case_ = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
snake_case_ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ = config['''lr''']
snake_case_ = int(config['''num_epochs'''] )
snake_case_ = int(config['''seed'''] )
snake_case_ = int(config['''batch_size'''] )
snake_case_ = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
snake_case_ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
snake_case_ = batch_size // MAX_GPU_BATCH_SIZE
snake_case_ = MAX_GPU_BATCH_SIZE
set_seed(SCREAMING_SNAKE_CASE )
# New Code #
# Create our folds:
snake_case_ = kfold.split(np.zeros(datasets['''train'''].num_rows ) , datasets['''train''']['''label'''] )
snake_case_ = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(SCREAMING_SNAKE_CASE ):
snake_case_ , snake_case_ , snake_case_ = get_fold_dataloaders(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE )
# Instantiate scheduler
snake_case_ = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE ) * 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.
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = accelerator.prepare(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ = model(**SCREAMING_SNAKE_CASE )
snake_case_ = outputs.loss
snake_case_ = loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ = model(**SCREAMING_SNAKE_CASE )
snake_case_ = outputs.logits.argmax(dim=-1 )
snake_case_ , snake_case_ = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE , )
snake_case_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , SCREAMING_SNAKE_CASE )
# New Code #
# We also run predictions on the test set at the very end
snake_case_ = []
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ = model(**SCREAMING_SNAKE_CASE )
snake_case_ = outputs.logits
snake_case_ , snake_case_ = 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(SCREAMING_SNAKE_CASE , 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:
snake_case_ = torch.cat(SCREAMING_SNAKE_CASE , dim=0 )
snake_case_ = torch.stack(SCREAMING_SNAKE_CASE , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
snake_case_ = metric.compute(predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE )
accelerator.print('''Average test metrics from all folds:''' , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ()-> int:
"""simple docstring"""
snake_case_ = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE , default=3 , help='''The number of splits to perform across the dataset''' )
snake_case_ = parser.parse_args()
snake_case_ = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 531
|
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __lowerCAmelCase (SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )-> Tuple:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE )
@dataclass
class lowerCAmelCase_ :
'''simple docstring'''
__snake_case = field(
metadata={"help": "The csv file to plot."} , )
__snake_case = field(
default=lowerCamelCase__ , metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."} , )
__snake_case = field(
default=lowerCamelCase__ , metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."} , )
__snake_case = field(
default=lowerCamelCase__ , metadata={"help": "Disable logarithmic scale when plotting"} , )
__snake_case = field(
default=lowerCamelCase__ , metadata={
"help": "Whether the csv file has training results or inference results. Defaults to inference results."
} , )
__snake_case = field(
default=lowerCamelCase__ , metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."} , )
__snake_case = list_field(
default=lowerCamelCase__ , metadata={"help": "List of model names that are used instead of the ones in the csv file."} )
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Optional[Any]:
"""simple docstring"""
try:
int(SCREAMING_SNAKE_CASE )
return True
except ValueError:
return False
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Tuple:
"""simple docstring"""
try:
float(SCREAMING_SNAKE_CASE )
return True
except ValueError:
return False
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
snake_case_ = args
snake_case_ = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='''''' ) as csv_file:
snake_case_ = csv.DictReader(_UpperCAmelCase )
for row in reader:
snake_case_ = row['''model''']
self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
if can_convert_to_int(row['''result'''] ):
# value is not None
snake_case_ = int(row['''result'''] )
elif can_convert_to_float(row['''result'''] ):
# value is not None
snake_case_ = float(row['''result'''] )
def UpperCamelCase__ ( self ):
snake_case_ , snake_case_ = plt.subplots()
snake_case_ = '''Time usage''' if self.args.is_time else '''Memory usage'''
snake_case_ = title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('''log''' )
ax.set_yscale('''log''' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
snake_case_ = sorted(set(self.result_dict[model_name]['''bsz'''] ) )
snake_case_ = sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
snake_case_ = self.result_dict[model_name]['''result''']
((snake_case_) , (snake_case_)) = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
snake_case_ = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
snake_case_ = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=_UpperCAmelCase , )
else:
snake_case_ = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((snake_case_) , (snake_case_)) = (
('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
)
snake_case_ = np.asarray(_UpperCAmelCase , _UpperCAmelCase )[: len(_UpperCAmelCase )]
plt.scatter(
_UpperCAmelCase , _UpperCAmelCase , label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(_UpperCAmelCase , _UpperCAmelCase , '''--''' )
title_str += F''' {label_model_name} vs.'''
snake_case_ = title_str[:-4]
snake_case_ = '''Time in s''' if self.args.is_time else '''Memory in MB'''
# plot
plt.title(_UpperCAmelCase )
plt.xlabel(_UpperCAmelCase )
plt.ylabel(_UpperCAmelCase )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def __lowerCAmelCase ()-> int:
"""simple docstring"""
snake_case_ = HfArgumentParser(SCREAMING_SNAKE_CASE )
snake_case_ = parser.parse_args_into_dataclasses()[0]
snake_case_ = Plot(args=SCREAMING_SNAKE_CASE )
plot.plot()
if __name__ == "__main__":
main()
| 531
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class A_ ( unittest.TestCase ):
def __init__( self: List[Any] ,__lowerCAmelCase: int ,__lowerCAmelCase: List[Any]=13 ,__lowerCAmelCase: Dict=7 ,__lowerCAmelCase: str=True ,__lowerCAmelCase: Dict=True ,__lowerCAmelCase: Any=True ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: int=99 ,__lowerCAmelCase: str=32 ,__lowerCAmelCase: Optional[int]=5 ,__lowerCAmelCase: Optional[Any]=4 ,__lowerCAmelCase: List[Any]=37 ,__lowerCAmelCase: Dict="gelu" ,__lowerCAmelCase: List[str]=0.1 ,__lowerCAmelCase: List[str]=0.1 ,__lowerCAmelCase: str=512 ,__lowerCAmelCase: List[str]=16 ,__lowerCAmelCase: Union[str, Any]=2 ,__lowerCAmelCase: List[Any]=0.02 ,__lowerCAmelCase: Optional[int]=4 ,):
'''simple docstring'''
_lowerCamelCase : List[str] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Union[str, Any] = seq_length
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = use_attention_mask
_lowerCamelCase : List[Any] = use_token_type_ids
_lowerCamelCase : Optional[Any] = use_labels
_lowerCamelCase : int = vocab_size
_lowerCamelCase : int = hidden_size
_lowerCamelCase : List[Any] = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : Optional[int] = intermediate_size
_lowerCamelCase : Tuple = hidden_act
_lowerCamelCase : Union[str, Any] = hidden_dropout_prob
_lowerCamelCase : int = attention_probs_dropout_prob
_lowerCamelCase : str = max_position_embeddings
_lowerCamelCase : List[Any] = type_vocab_size
_lowerCamelCase : Optional[int] = type_sequence_label_size
_lowerCamelCase : Union[str, Any] = initializer_range
_lowerCamelCase : Any = num_choices
def _lowercase ( self: str ):
'''simple docstring'''
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowerCamelCase : int = None
if self.use_attention_mask:
_lowerCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Tuple = None
if self.use_token_type_ids:
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_lowerCamelCase : Any = RobertaConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__lowerCAmelCase ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Dict = self.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : str = config_and_inputs
_lowerCamelCase : Any = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def _lowercase ( self: List[str] ):
'''simple docstring'''
_lowerCamelCase : Dict = self.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : List[str] = config_and_inputs
_lowerCamelCase : int = True
_lowerCamelCase : Optional[int] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A_ ( _a , unittest.TestCase ):
lowerCAmelCase__ = True
lowerCAmelCase__ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowercase ( self: Dict ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = FlaxRobertaModelTester(self )
@slow
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
_lowerCamelCase : List[Any] = model_class_name.from_pretrained("roberta-base" ,from_pt=__lowerCAmelCase )
_lowerCamelCase : int = model(np.ones((1, 1) ) )
self.assertIsNotNone(__lowerCAmelCase )
| 46
|
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
UpperCAmelCase__ , UpperCAmelCase__: int = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
UpperCAmelCase__: str = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,(h - t) )
# Recursively sort last 2/3 elements
stooge(SCREAMING_SNAKE_CASE ,i + t ,(SCREAMING_SNAKE_CASE) )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,(h - t) )
if __name__ == "__main__":
_lowerCAmelCase : List[str] =input("""Enter numbers separated by a comma:\n""").strip()
_lowerCAmelCase : Any =[int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted))
| 113
| 0
|
"""simple docstring"""
def lowercase ( A_ )-> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
a : Union[str, Any] = sum(A_ ) / len(A_ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 135
|
"""simple docstring"""
import os
def lowercase ( )-> Optional[Any]:
'''simple docstring'''
a : Optional[int] = os.path.join(os.path.dirname(A_ ) , "num.txt" )
with open(A_ ) as file_hand:
return str(sum(int(A_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 135
| 1
|
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