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import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 lowerCamelCase_ ( lowerCamelCase , unittest.TestCase ):
a__ = KandinskyVaaPipeline
a__ = [
'''image_embeds''',
'''negative_image_embeds''',
]
a__ = ['''image_embeds''', '''negative_image_embeds''']
a__ = [
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
a__ = False
@property
def A ( self ):
"""simple docstring"""
return 3_2
@property
def A ( self ):
"""simple docstring"""
return 3_2
@property
def A ( self ):
"""simple docstring"""
return self.time_input_dim
@property
def A ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def A ( self ):
"""simple docstring"""
return 1_0_0
@property
def A ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__magic_name__ :str = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__magic_name__ :Optional[int] = UNetaDConditionModel(**__lowerCAmelCase )
return model
@property
def A ( self ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__magic_name__ :int = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self ):
"""simple docstring"""
__magic_name__ :Dict = self.dummy_unet
__magic_name__ :str = self.dummy_movq
__magic_name__ :Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.00085 , beta_end=0.012 , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , steps_offset=1 , prediction_type='''epsilon''' , thresholding=__lowerCAmelCase , )
__magic_name__ :Dict = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self , __lowerCAmelCase , __lowerCAmelCase=0 ):
"""simple docstring"""
__magic_name__ :int = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
__magic_name__ :Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowerCAmelCase )
if str(__lowerCAmelCase ).startswith('''mps''' ):
__magic_name__ :Dict = torch.manual_seed(__lowerCAmelCase )
else:
__magic_name__ :Any = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
__magic_name__ :Union[str, Any] = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def A ( self ):
"""simple docstring"""
__magic_name__ :Dict = '''cpu'''
__magic_name__ :int = self.get_dummy_components()
__magic_name__ :List[Any] = self.pipeline_class(**__lowerCAmelCase )
__magic_name__ :Optional[int] = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ :Optional[int] = pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
__magic_name__ :Optional[int] = output.images
__magic_name__ :Optional[int] = pipe(
**self.get_dummy_inputs(__lowerCAmelCase ) , return_dict=__lowerCAmelCase , )[0]
__magic_name__ :List[Any] = image[0, -3:, -3:, -1]
__magic_name__ :Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__magic_name__ :str = np.array(
[0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046] )
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 lowerCamelCase_ ( unittest.TestCase ):
def A ( self ):
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self ):
"""simple docstring"""
__magic_name__ :Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
__magic_name__ :Dict = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCAmelCase )
__magic_name__ :Any = KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
__magic_name__ :List[str] = pipeline.to(__lowerCAmelCase )
pipeline.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ :int = '''red cat, 4k photo'''
__magic_name__ :int = torch.Generator(device='''cuda''' ).manual_seed(0 )
__magic_name__ , __magic_name__ :int = pipe_prior(
__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__magic_name__ :int = torch.Generator(device='''cuda''' ).manual_seed(0 )
__magic_name__ :Optional[Any] = pipeline(
image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=1_0_0 , output_type='''np''' , )
__magic_name__ :Union[str, Any] = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
| 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=24 , _UpperCamelCase=2 , _UpperCamelCase=6 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=3 , _UpperCamelCase=None , _UpperCamelCase=1000 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def UpperCamelCase( self ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_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 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase( self ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
__A : Dict = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__A : Optional[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = False
__A : Optional[int] = False
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return True
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def UpperCamelCase( self ):
self.config_tester.run_common_tests()
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def UpperCamelCase( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
_UpperCAmelCase = torch.Size([1, 2, 768] )
_UpperCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) ) | 32 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__snake_case = logging.get_logger(__name__)
def _A ( _lowercase ) -> List[int]:
"""simple docstring"""
if isinstance(_lowercase , np.ndarray ):
return list(tensor.shape )
__UpperCamelCase = tf.shape(_lowercase )
if tensor.shape == tf.TensorShape(_lowercase ):
return dynamic
__UpperCamelCase = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_lowercase )]
def _A ( _lowercase , _lowercase = None , _lowercase = None ) -> tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=_lowercase , name=_lowercase )
def _A ( _lowercase , _lowercase , _lowercase , _lowercase=1e-5 , _lowercase=-1 ) -> str:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_lowercase , _lowercase ):
raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.' )
# Get mean and variance on the axis to be normalized
__UpperCamelCase, __UpperCamelCase = tf.nn.moments(_lowercase , axes=[axis] , keepdims=_lowercase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__UpperCamelCase = [1] * inputs.shape.rank
__UpperCamelCase = shape_list(_lowercase )[axis]
__UpperCamelCase = tf.reshape(_lowercase , _lowercase )
__UpperCamelCase = tf.reshape(_lowercase , _lowercase )
# Compute layer normalization using the batch_normalization
# function.
__UpperCamelCase = tf.nn.batch_normalization(
_lowercase , _lowercase , _lowercase , offset=_lowercase , scale=_lowercase , variance_epsilon=_lowercase , )
return outputs
def _A ( _lowercase , _lowercase=0 , _lowercase=-1 ) -> Tuple:
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__UpperCamelCase = tf.shape(_lowercase )
__UpperCamelCase = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__UpperCamelCase = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(_lowercase , _lowercase )
def _A ( _lowercase ) -> tf.Tensor:
"""simple docstring"""
if not isinstance(_lowercase , tf.Tensor ):
__UpperCamelCase = tf.convert_to_tensor(_lowercase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__UpperCamelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__UpperCamelCase = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__UpperCamelCase = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _A ( _lowercase , _lowercase , _lowercase = "input_ids" ) -> None:
"""simple docstring"""
tf.debugging.assert_less(
_lowercase , tf.cast(_lowercase , dtype=tensor.dtype ) , message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(_lowercase )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def _A ( _lowercase , _lowercase , _lowercase ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__UpperCamelCase = [x for x in data if len(_lowercase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'The following attributes cannot be saved to HDF5 file because '
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
__UpperCamelCase = np.asarray(_lowercase )
__UpperCamelCase = 1
__UpperCamelCase = np.array_split(_lowercase , _lowercase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__UpperCamelCase = np.array_split(_lowercase , _lowercase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_lowercase ):
__UpperCamelCase = chunk_data
else:
__UpperCamelCase = data
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
if name in group.attrs:
__UpperCamelCase = [n.decode('utf8' ) if hasattr(_lowercase , 'decode' ) else n for n in group.attrs[name]]
else:
__UpperCamelCase = []
__UpperCamelCase = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8' ) if hasattr(_lowercase , 'decode' ) else n for n in group.attrs['%s%d' % (name, chunk_id)]] )
chunk_id += 1
return data
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
def _expand_single_ad_tensor(_lowercase ):
if isinstance(_lowercase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_lowercase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , _lowercase )
| 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Tuple = """rwkv"""
__A : Any = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _UpperCamelCase=50277 , _UpperCamelCase=1024 , _UpperCamelCase=4096 , _UpperCamelCase=32 , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , _UpperCamelCase=0 , _UpperCamelCase=6 , _UpperCamelCase=False , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class lowerCamelCase__ ( _A , _A , unittest.TestCase):
"""simple docstring"""
a__ : List[str] = IFPipeline
a__ : Optional[Any] = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
a__ : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
a__ : Optional[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def snake_case_ ( self : Dict ) -> Optional[Any]:
return self._get_dummy_components()
def snake_case_ ( self : Any , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any]=0 ) -> List[str]:
if str(__lowerCAmelCase ).startswith('''mps''' ):
_A = torch.manual_seed(__lowerCAmelCase )
else:
_A = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
_A = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def snake_case_ ( self : str ) -> Tuple:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def snake_case_ ( self : Union[str, Any] ) -> Any:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def snake_case_ ( self : Union[str, Any] ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Union[str, Any]:
self._test_save_load_local()
def snake_case_ ( self : Dict ) -> List[Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def snake_case_ ( self : Dict ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : List[Any] ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self : Tuple ) -> str:
# if
_A = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa )
_A = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa , text_encoder=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''' )
_A , _A = pipe_a.encode_prompt('''anime turtle''' , device='''cuda''' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_A = None
_A = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_A = IFImgaImgPipeline(**pipe_a.components )
_A = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_A = IFInpaintingPipeline(**pipe_a.components )
_A = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] ) -> Tuple:
# pipeline 1
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , num_inference_steps=2 , generator=__lowerCAmelCase , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , image=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , image=__lowerCAmelCase , num_inference_steps=2 , generator=__lowerCAmelCase , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , image=__lowerCAmelCase , original_image=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ) -> Tuple:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__lowerCAmelCase )
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , image=__lowerCAmelCase , mask_image=__lowerCAmelCase , num_inference_steps=2 , generator=__lowerCAmelCase , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device='''cpu''' ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(__lowerCAmelCase )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(__lowerCAmelCase )
_A = pipe_a(
prompt_embeds=__lowerCAmelCase , negative_prompt_embeds=__lowerCAmelCase , image=__lowerCAmelCase , mask_image=__lowerCAmelCase , original_image=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=2 , output_type='''np''' , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE_ ( ) -> List[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 2 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
'''simple docstring'''
import operator as op
def A_( A : Union[str, Any]):
UpperCamelCase = []
UpperCamelCase = lambda A , A: int(x / y) # noqa: E731 integer division operation
UpperCamelCase = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8) , 'Action'.center(12) , 'Stack' , sep=' | ')
print('-' * (30 + len(A)))
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(A) # append x to stack
# output in tabular format
print(x.rjust(8) , ('push(' + x + ')').ljust(12) , ','.join(A) , sep=' | ')
else:
UpperCamelCase = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8) , ('pop(' + b + ')').ljust(12) , ','.join(A) , sep=' | ')
UpperCamelCase = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8) , ('pop(' + a + ')').ljust(12) , ','.join(A) , sep=' | ')
stack.append(
str(opr[x](int(A) , int(A)))) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8) , ('push(' + a + x + b + ')').ljust(12) , ','.join(A) , sep=' | ' , )
return int(stack[0])
if __name__ == "__main__":
lowerCAmelCase : Any = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 3 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Dict = """falcon"""
__A : Any = ["""past_key_values"""]
def __init__( self , _UpperCamelCase=65024 , _UpperCamelCase=4544 , _UpperCamelCase=32 , _UpperCamelCase=71 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=11 , _UpperCamelCase=11 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop('''n_embed''' , _UpperCamelCase )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCAmelCase = alibi
_UpperCAmelCase = new_decoder_architecture
_UpperCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_UpperCAmelCase = parallel_attn
_UpperCAmelCase = bias
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
return self.hidden_size // self.num_attention_heads
@property
def UpperCamelCase( self ):
return not self.alibi | 32 | 0 |
"""simple docstring"""
from collections import deque
class a :
def __init__( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = process_name # process name
lowerCAmelCase = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
lowerCAmelCase = arrival_time
lowerCAmelCase = burst_time # remaining burst time
lowerCAmelCase = 0 # total time of the process wait in ready queue
lowerCAmelCase = 0 # time from arrival time to completion time
class a :
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = number_of_queues
# time slice of queues that round robin algorithm applied
lowerCAmelCase = time_slices
# unfinished process is in this ready_queue
lowerCAmelCase = queue
# current time
lowerCAmelCase = current_time
# finished process is in this sequence queue
lowerCAmelCase = deque()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = []
for i in range(len(_snake_case ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = []
for i in range(len(_snake_case ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = []
for i in range(len(_snake_case ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
return [q.burst_time for q in queue]
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = deque() # sequence deque of finished process
while len(_snake_case ) != 0:
lowerCAmelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(_snake_case )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
lowerCAmelCase = 0
# set the process's turnaround time because it is finished
lowerCAmelCase = self.current_time - cp.arrival_time
# set the completion time
lowerCAmelCase = self.current_time
# add the process to queue that has finished queue
finished.append(_snake_case )
self.finish_queue.extend(_snake_case ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(_snake_case ) ):
lowerCAmelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(_snake_case )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
lowerCAmelCase = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(_snake_case )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
lowerCAmelCase = 0
# set the finish time
lowerCAmelCase = self.current_time
# update the process' turnaround time because it is finished
lowerCAmelCase = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(_snake_case )
self.finish_queue.extend(_snake_case ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def UpperCamelCase__ ( self ):
"""simple docstring"""
for i in range(self.number_of_queues - 1 ):
lowerCAmelCase ,lowerCAmelCase = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
__UpperCamelCase : Tuple = Process('''P1''', 0, 53)
__UpperCamelCase : List[Any] = Process('''P2''', 0, 17)
__UpperCamelCase : Tuple = Process('''P3''', 0, 68)
__UpperCamelCase : List[Any] = Process('''P4''', 0, 24)
__UpperCamelCase : Union[str, Any] = 3
__UpperCamelCase : str = [17, 25]
__UpperCamelCase : str = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})
__UpperCamelCase : Union[str, Any] = Process('''P1''', 0, 53)
__UpperCamelCase : Any = Process('''P2''', 0, 17)
__UpperCamelCase : Optional[Any] = Process('''P3''', 0, 68)
__UpperCamelCase : List[Any] = Process('''P4''', 0, 24)
__UpperCamelCase : List[Any] = 3
__UpperCamelCase : Any = [17, 25]
__UpperCamelCase : int = deque([Pa, Pa, Pa, Pa])
__UpperCamelCase : Union[str, Any] = MLFQ(number_of_queues, time_slices, queue, 0)
__UpperCamelCase : Optional[Any] = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
f'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
f'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 4 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
_lowercase = None
_lowercase = logging.get_logger(__name__)
_lowercase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
_lowercase = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
},
}
_lowercase = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
_lowercase = """▁"""
# Segments (not really needed)
_lowercase = 0
_lowercase = 1
_lowercase = 2
_lowercase = 3
_lowercase = 4
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = VOCAB_FILES_NAMES
_lowercase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : str = '''left'''
_lowercase : Optional[int] = XLNetTokenizer
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=True , _lowercase=False , _lowercase="<s>" , _lowercase="</s>" , _lowercase="<unk>" , _lowercase="<sep>" , _lowercase="<pad>" , _lowercase="<cls>" , _lowercase="<mask>" , _lowercase=["<eop>", "<eod>"] , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
_lowerCAmelCase = 3
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = remove_space
_lowerCAmelCase = keep_accents
_lowerCAmelCase = vocab_file
_lowerCAmelCase = False if not self.vocab_file else True
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 5 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 0:
return False
_UpperCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ ( UpperCamelCase__ ):
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 :Optional[int] , __A :str = "" , __A :Optional[str] = None , __A :Optional[dict] = None , **__A :List[str] ) -> Any:
"""simple docstring"""
super().__init__(self , **__A )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
SCREAMING_SNAKE_CASE__ = fsspec.open(
__A , mode="""rb""" , protocol=__A , 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 {}) , )
SCREAMING_SNAKE_CASE__ = os.path.basename(self.file.path.split("""::""" )[0] )
SCREAMING_SNAKE_CASE__ = (
self.compressed_name[: self.compressed_name.rindex(""".""" )]
if """.""" in self.compressed_name
else self.compressed_name
)
SCREAMING_SNAKE_CASE__ = None
@classmethod
def _snake_case ( cls :Any , __A :Tuple ) -> List[str]:
"""simple docstring"""
return super()._strip_protocol(__A ).lstrip("""/""" )
def _snake_case ( self :Union[str, Any] ) -> Tuple:
"""simple docstring"""
if self.dir_cache is None:
SCREAMING_SNAKE_CASE__ = {**self.file.fs.info(self.file.path ), """name""": self.uncompressed_name}
SCREAMING_SNAKE_CASE__ = {f["""name"""]: f}
def _snake_case ( self :Optional[int] , __A :str ) -> str:
"""simple docstring"""
return self.file.open().read()
def _snake_case ( self :List[str] , __A :str , __A :str = "rb" , __A :int=None , __A :List[str]=True , __A :Optional[Any]=None , **__A :Union[str, Any] , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self._strip_protocol(__A )
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 UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = "bz2"
lowerCamelCase_ = "bz2"
lowerCamelCase_ = ".bz2"
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = "gzip"
lowerCamelCase_ = "gzip"
lowerCamelCase_ = ".gz"
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = "lz4"
lowerCamelCase_ = "lz4"
lowerCamelCase_ = ".lz4"
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = "xz"
lowerCamelCase_ = "xz"
lowerCamelCase_ = ".xz"
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = "zstd"
lowerCamelCase_ = "zstd"
lowerCamelCase_ = ".zst"
def __init__( self :List[Any] , __A :str , __A :str = "rb" , __A :Optional[str] = None , __A :Optional[dict] = None , __A :int = DEFAULT_BLOCK_SIZE , **__A :Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
fo=__A , mode=__A , target_protocol=__A , target_options=__A , block_size=__A , **__A , )
# 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
SCREAMING_SNAKE_CASE__ = self.file.__enter__
class UpperCamelCase_ :
def __init__( self :int , __A :Tuple ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = file_
def __enter__( self :Optional[Any] ) -> Optional[int]:
"""simple docstring"""
self._file.__enter__()
return self
def __exit__( self :Optional[Any] , *__A :List[Any] , **__A :int ) -> Tuple:
"""simple docstring"""
self._file.__exit__(*__A , **__A )
def __iter__( self :Any ) -> Optional[int]:
"""simple docstring"""
return iter(self._file )
def _snake_case ( self :Dict ) -> Dict:
"""simple docstring"""
return next(self._file )
def __getattr__( self :Union[str, Any] , __A :List[str] ) -> Optional[Any]:
"""simple docstring"""
return getattr(self._file , __A )
def fixed_enter(*__A :List[Any] , **__A :Optional[int] ):
return WrappedFile(_enter(*__A , **__A ) )
SCREAMING_SNAKE_CASE__ = fixed_enter | 6 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__A : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__A : ClassVar[Features] = Features({} )
__A : str = "text"
@property
def UpperCamelCase( self ):
return {self.text_column: "text"} | 32 | 0 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=13_37 , num_examples=42 , dataset_name='my_dataset' )} ),
SplitDict({'train': SplitInfo(name='train' , num_bytes=13_37 , num_examples=42 )} ),
SplitDict({'train': SplitInfo()} ),
] , )
def _snake_case ( _snake_case : SplitDict ) -> Optional[Any]:
'''simple docstring'''
_A = split_dict._to_yaml_list()
assert len(_snake_case ) == len(_snake_case )
_A = SplitDict._from_yaml_list(_snake_case )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
_A = None
# the split name of split_dict takes over the name of the split info object
_A = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'split_info' , [SplitInfo(), SplitInfo(dataset_name=_snake_case ), SplitInfo(dataset_name='my_dataset' )] )
def _snake_case ( _snake_case : Any ) -> Dict:
'''simple docstring'''
_A = asdict(SplitDict({'train': split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 7 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"vocab_file": "spiece.model"}
UpperCAmelCase_ = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
UpperCAmelCase_ = "▁"
class __UpperCamelCase ( A__ ):
__A : Any = VOCAB_FILES_NAMES
__A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , _UpperCamelCase , _UpperCamelCase="</s>" , _UpperCamelCase="<unk>" , _UpperCamelCase="<pad>" , _UpperCamelCase=100 , _UpperCamelCase=None , _UpperCamelCase = None , _UpperCamelCase=True , **_UpperCamelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase = len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) ) , _UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
_UpperCAmelCase = legacy
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , extra_ids=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = extra_ids
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@staticmethod
def UpperCamelCase( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_UpperCAmelCase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCamelCase , )
return max_model_length
@property
def UpperCamelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCamelCase( self ):
_UpperCAmelCase = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_UpperCamelCase )) + [1]
return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def UpperCamelCase( self ):
return list(
set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase( self ):
return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCamelCase( self , _UpperCamelCase ):
if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _UpperCamelCase ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_UpperCAmelCase = SPIECE_UNDERLINE + text.replace(_UpperCamelCase , ''' ''' )
return super().tokenize(_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
if not self.legacy:
_UpperCAmelCase = text.startswith(_UpperCamelCase )
if is_first:
_UpperCAmelCase = text[1:]
_UpperCAmelCase = self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
_UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCamelCase( self , _UpperCamelCase ):
if token.startswith('''<extra_id_''' ):
_UpperCAmelCase = re.match(R'''<extra_id_(\d+)>''' , _UpperCamelCase )
_UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase ):
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(_UpperCamelCase )
else:
_UpperCAmelCase = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(_UpperCamelCase )
_UpperCAmelCase = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,) | 32 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowercase__ : Optional[int] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
lowercase__ : Dict = parser.parse_args()
if args.model_type == "bert":
lowercase__ : Dict = BertForMaskedLM.from_pretrained(args.model_name)
lowercase__ : int = '''bert'''
else:
raise ValueError('''args.model_type should be "bert".''')
lowercase__ : List[Any] = model.state_dict()
lowercase__ : Optional[Any] = {}
for w in ["word_embeddings", "position_embeddings"]:
lowercase__ : Dict = state_dict[f"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
lowercase__ : Any = state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]
lowercase__ : List[Any] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
lowercase__ : Optional[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
lowercase__ : str = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
lowercase__ : Optional[int] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
lowercase__ : Dict = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
lowercase__ : List[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
lowercase__ : Dict = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
lowercase__ : List[str] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
lowercase__ : List[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
lowercase__ : int = state_dict['''cls.predictions.decoder.weight''']
lowercase__ : List[Any] = state_dict['''cls.predictions.bias''']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowercase__ : Optional[Any] = state_dict[f"""cls.predictions.transform.dense.{w}"""]
lowercase__ : Union[str, Any] = state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint) | 8 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
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 __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : str = ["image_processor", "tokenizer"]
A__ : str = "LayoutLMv3ImageProcessor"
A__ : Tuple = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
def __init__( self : Optional[int] , _snake_case : Optional[int]=None , _snake_case : Optional[int]=None , **_snake_case : int ):
"""simple docstring"""
A__ = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _snake_case , )
A__ = kwargs.pop('feature_extractor' )
A__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(_snake_case , _snake_case )
def __call__( self : Optional[int] , _snake_case : List[str] , _snake_case : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _snake_case : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , _snake_case : Union[List[List[int]], List[List[List[int]]]] = None , _snake_case : Optional[Union[List[int], List[List[int]]]] = None , _snake_case : bool = True , _snake_case : Union[bool, str, PaddingStrategy] = False , _snake_case : Union[bool, str, TruncationStrategy] = None , _snake_case : Optional[int] = None , _snake_case : int = 0 , _snake_case : Optional[int] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = True , _snake_case : Optional[Union[str, TensorType]] = None , **_snake_case : Optional[Any] , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
A__ = self.image_processor(images=_snake_case , return_tensors=_snake_case )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(_snake_case , _snake_case ):
A__ = [text] # add batch dimension (as the image processor always adds a batch dimension)
A__ = features['words']
A__ = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=_snake_case , add_special_tokens=_snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , stride=_snake_case , pad_to_multiple_of=_snake_case , return_token_type_ids=_snake_case , return_attention_mask=_snake_case , return_overflowing_tokens=_snake_case , return_special_tokens_mask=_snake_case , return_offsets_mapping=_snake_case , return_length=_snake_case , verbose=_snake_case , return_tensors=_snake_case , **_snake_case , )
# add pixel values
A__ = features.pop('pixel_values' )
if return_overflowing_tokens is True:
A__ = self.get_overflowing_images(_snake_case , encoded_inputs['overflow_to_sample_mapping'] )
A__ = images
return encoded_inputs
def _a ( self : int , _snake_case : List[str] , _snake_case : Union[str, Any] ):
"""simple docstring"""
A__ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(_snake_case ) != len(_snake_case ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(_snake_case )} and {len(_snake_case )}''' )
return images_with_overflow
def _a ( self : Union[str, Any] , *_snake_case : List[Any] , **_snake_case : Any ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def _a ( self : List[str] , *_snake_case : Optional[int] , **_snake_case : int ):
"""simple docstring"""
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def _a ( self : Any ):
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _snake_case , )
return self.image_processor_class
@property
def _a ( self : int ):
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _snake_case , )
return self.image_processor
| 9 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "unispeech-sat"
def __init__( self : List[Any] , _A : Dict=32 , _A : int=768 , _A : str=12 , _A : str=12 , _A : Any=3072 , _A : List[str]="gelu" , _A : Any=0.1 , _A : Union[str, Any]=0.1 , _A : Optional[int]=0.1 , _A : Optional[Any]=0.0 , _A : List[str]=0.0 , _A : Optional[Any]=0.1 , _A : str=0.1 , _A : List[str]=0.02 , _A : Optional[int]=1e-5 , _A : Dict="group" , _A : str="gelu" , _A : List[str]=(512, 512, 512, 512, 512, 512, 512) , _A : Any=(5, 2, 2, 2, 2, 2, 2) , _A : Dict=(10, 3, 3, 3, 3, 2, 2) , _A : Union[str, Any]=False , _A : str=128 , _A : Tuple=16 , _A : Optional[int]=False , _A : Dict=True , _A : Optional[Any]=0.05 , _A : Any=10 , _A : str=2 , _A : Dict=0.0 , _A : List[str]=10 , _A : Union[str, Any]=0 , _A : List[str]=320 , _A : List[Any]=2 , _A : Optional[Any]=0.1 , _A : Optional[Any]=100 , _A : List[str]=256 , _A : Any=256 , _A : List[Any]=0.1 , _A : Dict="mean" , _A : Dict=False , _A : List[str]=False , _A : List[Any]=256 , _A : Any=(512, 512, 512, 512, 1500) , _A : Any=(5, 3, 3, 1, 1) , _A : Dict=(1, 2, 3, 1, 1) , _A : str=512 , _A : Dict=0 , _A : List[str]=1 , _A : Tuple=2 , _A : Optional[Any]=504 , **_A : int , ):
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = conv_bias
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim )
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = vocab_size
_UpperCamelCase = num_clusters
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
_UpperCamelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = feat_quantizer_dropout
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = list(_A )
_UpperCamelCase = xvector_output_dim
@property
def UpperCamelCase_ ( self : List[str] ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 10 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
lowercase_ = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
lowercase_ = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = set()
_a = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
_a = char
_a = set(__A)
return pairs
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self , A , A , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , **A , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , **A , )
_a = vocab_file
_a = merges_file
_a = {}
_a = 0
_a = 1
_a = 2
_a = 3
self.add_from_file(A )
_a = {v: k for k, v in self.encoder.items()}
with open(A , encoding='''utf-8''' ) as merges_handle:
_a = merges_handle.read().split('''\n''' )[:-1]
_a = [tuple(merge.split()[:-1] ) for merge in merges]
_a = dict(zip(A , range(len(A ) ) ) )
_a = {}
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a = [self.cls_token_id]
_a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def a__ (self , A , A = None , A = False ) -> List[int]:
"""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 )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return len(self.encoder )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_a = tuple(A )
_a = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
_a = get_pairs(A )
if not pairs:
return token
while True:
_a = min(A , key=lambda A : self.bpe_ranks.get(A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_a , _a = bigram
_a = []
_a = 0
while i < len(A ):
try:
_a = word.index(A , A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_a = j
if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_a = tuple(A )
_a = new_word
if len(A ) == 1:
break
else:
_a = get_pairs(A )
_a = '''@@ '''.join(A )
_a = word[:-4]
_a = word
return word
def a__ (self , A ) -> str:
"""simple docstring"""
_a = []
_a = re.findall(R'''\S+\n?''' , A )
for token in words:
split_tokens.extend(list(self.bpe(A ).split(''' ''' ) ) )
return split_tokens
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.encoder.get(A , self.encoder.get(self.unk_token ) )
def a__ (self , A ) -> Any:
"""simple docstring"""
return self.decoder.get(A , self.unk_token )
def a__ (self , A ) -> int:
"""simple docstring"""
_a = ''' '''.join(A ).replace('''@@ ''' , '''''' ).strip()
return out_string
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
if os.path.abspath(self.merges_file ) != os.path.abspath(A ):
copyfile(self.merges_file , A )
return out_vocab_file, out_merge_file
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
if isinstance(A , A ):
try:
with open(A , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(A )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
_a = f.readlines()
for lineTmp in lines:
_a = lineTmp.strip()
_a = line.rfind(''' ''' )
if idx == -1:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
_a = line[:idx]
_a = len(self.encoder )
| 11 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( A__ , unittest.TestCase ):
__A : Any = DanceDiffusionPipeline
__A : Any = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__A : Tuple = PipelineTesterMixin.required_optional_params - {
"""callback""",
"""latents""",
"""callback_steps""",
"""output_type""",
"""num_images_per_prompt""",
}
__A : Tuple = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__A : List[str] = False
__A : str = False
def UpperCamelCase( self ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_UpperCamelCase , use_timestep_embedding=_UpperCamelCase , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
_UpperCAmelCase = IPNDMScheduler()
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=0 ):
if str(_UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(_UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def UpperCamelCase( self ):
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = DanceDiffusionPipeline(**_UpperCamelCase )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(_UpperCamelCase )
_UpperCAmelCase = pipe(**_UpperCamelCase )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_local()
@skip_mps
def UpperCamelCase( self ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase( self ):
return super().test_attention_slicing_forward_pass()
def UpperCamelCase( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2 | 32 | 0 |
from __future__ import annotations
def UpperCamelCase ( lowercase_ , lowercase_ ) -> list[int]:
'''simple docstring'''
lowercase__ : List[str] = 0
lowercase__ : List[Any] = len(lowercase_ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ : Optional[Any] = i + 1
else:
lowercase__ : int = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')
| 12 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 | 0 |
'''simple docstring'''
import math
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> bool:
return math.sqrt(UpperCAmelCase_ ) * math.sqrt(UpperCAmelCase_ ) == num
def UpperCAmelCase__ ( UpperCAmelCase_ : int ) -> bool:
__lowerCamelCase : Tuple = 0
__lowerCamelCase : str = n
while left <= right:
__lowerCamelCase : int = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
__lowerCamelCase : int = mid - 1
else:
__lowerCamelCase : int = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 |
import baseaa
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A__ ( SCREAMING_SNAKE_CASE_ : bytes ) -> str:
"""simple docstring"""
return baseaa.baadecode(SCREAMING_SNAKE_CASE_ ).decode('''utf-8''' )
if __name__ == "__main__":
UpperCAmelCase_ = "Hello World!"
UpperCAmelCase_ = baseaa_encode(test)
print(encoded)
UpperCAmelCase_ = baseaa_decode(encoded)
print(decoded) | 32 | 0 |
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
_a : str = abs(__a )
_a : Any = 0
while n > 0:
res += n % 10
n //= 10
return res
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
_a : Union[str, Any] = abs(__a )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
return sum(int(__a ) for c in str(abs(__a ) ) )
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__a : Callable ,__a : int ) -> None:
_a : str = F"""{func.__name__}({value})"""
_a : List[Any] = timeit(F"""__main__.{call}""" ,setup='''import __main__''' )
print(F"""{call:56} = {func(__a )} -- {timing:.4f} seconds""" )
for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__a ,__a )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 14 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
__A : int = ["""pixel_values"""]
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1 / 255 , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
super().__init__(**_UpperCamelCase )
_UpperCAmelCase = size if size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name='''crop_size''' )
_UpperCAmelCase = do_resize
_UpperCAmelCase = do_rescale
_UpperCAmelCase = do_normalize
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = size
_UpperCAmelCase = resample
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if "shortest_edge" in size:
_UpperCAmelCase = get_resize_output_image_size(_UpperCamelCase , size=size['''shortest_edge'''] , default_to_square=_UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = 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, width). Got {size.keys()}''' )
return center_crop(_UpperCamelCase , size=(size['''height'''], size['''width''']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( 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 , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = get_size_dict(_UpperCamelCase , param_name='''crop_size''' , default_to_square=_UpperCamelCase )
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if not is_batched(_UpperCamelCase ):
_UpperCAmelCase = [images]
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.''' )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
_UpperCAmelCase = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
_UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase ) | 32 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
A : List[Any] = open # noqa: we just need to have a builtin inside this module to test it properly
| 15 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] ) | 32 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
__A : List[str] = get_logger()
__A : Optional[dict] = None
class _SCREAMING_SNAKE_CASE ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : Tuple=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict ):
super().__init__(features=__lowerCamelCase )
import jax
from jaxlib.xla_client import Device
if isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError(
f"Expected {device} to be a `str` not {type(__lowerCamelCase )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE = device if isinstance(__lowerCamelCase , __lowerCamelCase ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"Device with string identifier {self.device} not listed among the available "
f"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
f"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE = jnp_array_kwargs
@staticmethod
def _snake_case ( ):
import jax
return {str(__lowerCamelCase ): device for device in jax.devices()}
def _snake_case ( self : Any , __lowerCamelCase : List[Any] ):
import jax
import jax.numpy as jnp
if isinstance(__lowerCamelCase , __lowerCamelCase ) and column:
if all(
isinstance(__lowerCamelCase , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__lowerCamelCase , axis=0 )
return column
def _snake_case ( self : int , __lowerCamelCase : Dict ):
import jax
import jax.numpy as jnp
if isinstance(__lowerCamelCase , (str, bytes, type(__lowerCamelCase )) ):
return value
elif isinstance(__lowerCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE = {}
if isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE = {"dtype": jnp.intaa}
elif isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__lowerCamelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE = np.asarray(__lowerCamelCase )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__lowerCamelCase , **{**default_dtype, **self.jnp_array_kwargs} )
def _snake_case ( self : List[Any] , __lowerCamelCase : int ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__lowerCamelCase , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__lowerCamelCase , "__array__" ) and not isinstance(__lowerCamelCase , jax.Array ):
SCREAMING_SNAKE_CASE = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__lowerCamelCase , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
elif isinstance(__lowerCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
return self._tensorize(__lowerCamelCase )
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : dict ):
return map_nested(self._recursive_tensorize , __lowerCamelCase , map_list=__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : pa.Table ):
SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_row(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_row(__lowerCamelCase )
return self.recursive_tensorize(__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : pa.Table ):
SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_column(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_column(__lowerCamelCase , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE = self.recursive_tensorize(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self._consolidate(__lowerCamelCase )
return column
def _snake_case ( self : int , __lowerCamelCase : pa.Table ):
SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_batch(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_batch(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.recursive_tensorize(__lowerCamelCase )
for column_name in batch:
SCREAMING_SNAKE_CASE = self._consolidate(batch[column_name] )
return batch | 16 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = [0 for i in range(n + 1 )]
_UpperCAmelCase = 1
_UpperCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = 1
_UpperCAmelCase = 0
for i in range(SCREAMING_SNAKE_CASE_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTextConfig''',
'''AltCLIPVisionConfig''',
],
'''processing_altclip''': ['''AltCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AltCLIPPreTrainedModel''',
'''AltCLIPModel''',
'''AltCLIPTextModel''',
'''AltCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
UpperCAmelCase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 17 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , _UpperCamelCase , )
super().__init__(*_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
import functools
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
@functools.cache
def min_distance(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : 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
_lowerCAmelCase = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , SCREAMING_SNAKE_CASE_ ) , 1 + min_distance(SCREAMING_SNAKE_CASE_ , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 18 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict = ["""image_processor""", """tokenizer"""]
__A : List[str] = """BridgeTowerImageProcessor"""
__A : str = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = 0 , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = True , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = self.tokenizer(
text=_UpperCamelCase , add_special_tokens=_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , max_length=_UpperCamelCase , stride=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_token_type_ids=_UpperCamelCase , return_attention_mask=_UpperCamelCase , return_overflowing_tokens=_UpperCamelCase , return_special_tokens_mask=_UpperCamelCase , return_offsets_mapping=_UpperCamelCase , return_length=_UpperCamelCase , verbose=_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase , )
# add pixel_values + pixel_mask
_UpperCAmelCase = self.image_processor(
_UpperCamelCase , return_tensors=_UpperCamelCase , do_normalize=_UpperCamelCase , do_center_crop=_UpperCamelCase , **_UpperCamelCase )
encoding.update(_UpperCamelCase )
return encoding
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
_UpperCAmelCase = self.tokenizer.model_input_names
_UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 32 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> bool:
"""simple docstring"""
if curr_ind == len(__snake_case ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0, len(__snake_case ) ):
if valid_connection(__snake_case, __snake_case, __snake_case, __snake_case ):
# Insert current vertex into path as next transition
_UpperCamelCase = next_ver
# Validate created path
if util_hamilton_cycle(__snake_case, __snake_case, curr_ind + 1 ):
return True
# Backtrack
_UpperCamelCase = -1
return False
def lowerCamelCase__ ( __snake_case, __snake_case = 0 ) -> list[int]:
"""simple docstring"""
_UpperCamelCase = [-1] * (len(__snake_case ) + 1)
# initialize start and end of path with starting index
_UpperCamelCase = _UpperCamelCase = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__snake_case, __snake_case, 1 ) else []
| 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 32 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase: str = logging.get_logger(__name__)
_lowerCAmelCase: Any = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class lowercase_ (lowercase__ ):
snake_case ='big_bird'
def __init__( self , lowercase_=50358 , lowercase_=768 , lowercase_=12 , lowercase_=12 , lowercase_=3072 , lowercase_="gelu_new" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=4096 , lowercase_=2 , lowercase_=0.02 , lowercase_=1e-12 , lowercase_=True , lowercase_=0 , lowercase_=1 , lowercase_=2 , lowercase_=66 , lowercase_="block_sparse" , lowercase_=True , lowercase_=False , lowercase_=64 , lowercase_=3 , lowercase_=None , **lowercase_ , ) -> Any:
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
a__ =vocab_size
a__ =max_position_embeddings
a__ =hidden_size
a__ =num_hidden_layers
a__ =num_attention_heads
a__ =intermediate_size
a__ =hidden_act
a__ =hidden_dropout_prob
a__ =attention_probs_dropout_prob
a__ =initializer_range
a__ =type_vocab_size
a__ =layer_norm_eps
a__ =use_cache
a__ =rescale_embeddings
a__ =attention_type
a__ =use_bias
a__ =block_size
a__ =num_random_blocks
a__ =classifier_dropout
class lowercase_ (lowercase__ ):
@property
def __UpperCamelCase ( self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
a__ ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__ ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 20 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __UpperCamelCase ( A__ ):
__A : Any = """biogpt"""
def __init__( self , _UpperCamelCase=42384 , _UpperCamelCase=1024 , _UpperCamelCase=24 , _UpperCamelCase=16 , _UpperCamelCase=4096 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=1024 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-12 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_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 = layer_norm_eps
_UpperCAmelCase = scale_embedding
_UpperCAmelCase = use_cache
_UpperCAmelCase = layerdrop
_UpperCAmelCase = activation_dropout
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
class __A :
def __init__( self :Any , __snake_case :str = "" , __snake_case :bool = False ):
'''simple docstring'''
__magic_name__ : dict[str, RadixNode] ={}
# A node will be a leaf if the tree contains its word
__magic_name__ : Tuple =is_leaf
__magic_name__ : Optional[Any] =prefix
def A__ ( self :Tuple , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Optional[Any] =0
for q, w in zip(self.prefix , __snake_case ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def A__ ( self :Optional[int] , __snake_case :list[str] ):
'''simple docstring'''
for word in words:
self.insert(__snake_case )
def A__ ( self :List[Any] , __snake_case :str ):
'''simple docstring'''
if self.prefix == word:
__magic_name__ : str =True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
__magic_name__ : Optional[int] =RadixNode(prefix=__snake_case , is_leaf=__snake_case )
else:
__magic_name__ : Union[str, Any] =self.nodes[word[0]]
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =incoming_node.match(
__snake_case )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(__snake_case )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
__magic_name__ : Tuple =remaining_prefix
__magic_name__ : Optional[Any] =self.nodes[matching_string[0]]
__magic_name__ : Optional[Any] =RadixNode(__snake_case , __snake_case )
__magic_name__ : int =aux_node
if remaining_word == "":
__magic_name__ : int =True
else:
self.nodes[matching_string[0]].insert(__snake_case )
def A__ ( self :Any , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.nodes.get(word[0] , __snake_case )
if not incoming_node:
return False
else:
__magic_name__ , __magic_name__ , __magic_name__ : Dict =incoming_node.match(
__snake_case )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(__snake_case )
def A__ ( self :Optional[Any] , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Tuple =self.nodes.get(word[0] , __snake_case )
if not incoming_node:
return False
else:
__magic_name__ , __magic_name__ , __magic_name__ : int =incoming_node.match(
__snake_case )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(__snake_case )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
__magic_name__ : List[Any] =list(self.nodes.values() )[0]
__magic_name__ : List[str] =merging_node.is_leaf
self.prefix += merging_node.prefix
__magic_name__ : List[str] =merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
__magic_name__ : str =False
# If there is 1 edge, we merge it with its child
else:
__magic_name__ : Optional[Any] =list(incoming_node.nodes.values() )[0]
__magic_name__ : List[Any] =merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
__magic_name__ : int =merging_node.nodes
return True
def A__ ( self :Tuple , __snake_case :int = 0 ):
'''simple docstring'''
if self.prefix != "":
print("""-""" * height , self.prefix , """ (leaf)""" if self.is_leaf else """""" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def lowerCAmelCase_ ( ):
__magic_name__ : List[Any] ="""banana bananas bandana band apple all beast""".split()
__magic_name__ : Dict =RadixNode()
root.insert_many(lowerCamelCase )
assert all(root.find(lowerCamelCase ) for word in words )
assert not root.find("""bandanas""" )
assert not root.find("""apps""" )
root.delete("""all""" )
assert not root.find("""all""" )
root.delete("""banana""" )
assert not root.find("""banana""" )
assert root.find("""bananas""" )
return True
def lowerCAmelCase_ ( ):
assert test_trie()
def lowerCAmelCase_ ( ):
__magic_name__ : Optional[int] =RadixNode()
__magic_name__ : List[str] ="""banana bananas bandanas bandana band apple all beast""".split()
root.insert_many(lowerCamelCase )
print("""Words:""" , lowerCamelCase )
print("""Tree:""" )
root.print_tree()
if __name__ == "__main__":
main()
| 21 |
from typing import List
from .keymap import KEYMAP, get_character
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : List[Any] ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
def A__ ( *SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : Any ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
class __UpperCamelCase ( A__ ):
def __new__( cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = super().__new__(cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if not hasattr(_UpperCamelCase , '''key_handler''' ):
setattr(_UpperCamelCase , '''key_handler''' , {} )
setattr(_UpperCamelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCAmelCase = getattr(_UpperCamelCase , '''handle_key''' , [] )
for key in handled_keys:
_UpperCAmelCase = value
return new_cls
@staticmethod
def UpperCamelCase( cls ):
_UpperCAmelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCAmelCase = ord(_UpperCamelCase )
_UpperCAmelCase = cls.key_handler.get(_UpperCamelCase )
if handler:
_UpperCAmelCase = char
return handler(cls )
else:
return None
def A__ ( cls : Union[str, Any] ) -> Any:
"""simple docstring"""
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() ) | 32 | 0 |
'''simple docstring'''
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 snake_case_ ():
'''simple docstring'''
raise RuntimeError('''CUDA out of memory.''' )
class A ( nn.Module ):
def __init__( self : List[Any] ) -> List[str]:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Dict ) -> List[str]:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase_ ) ) )
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_a = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(lowerCAmelCase_ : str ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase_ )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(lowerCAmelCase_ , [1_28, 64, 32, 16, 8] )
def __lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_a = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase_ )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
_a , _a = mock_training_loop_function('''hello''' )
self.assertListEqual(lowerCAmelCase_ , [1_28, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, '''hello'''] )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(lowerCAmelCase_ : Tuple ):
pass
with self.assertRaises(lowerCAmelCase_ ) as cm:
mock_training_loop_function()
self.assertIn('''No executable batch size found, reached zero.''' , cm.exception.args[0] )
def __lowerCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(lowerCAmelCase_ : Union[str, Any] ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(lowerCAmelCase_ ) as cm:
mock_training_loop_function()
self.assertIn('''No executable batch size found, reached zero.''' , cm.exception.args[0] )
def __lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(lowerCAmelCase_ : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(lowerCAmelCase_ ) as cm:
mock_training_loop_function(1_28 , '''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 __lowerCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(lowerCAmelCase_ : Any ):
raise ValueError('''Oops, we had an error!''' )
with self.assertRaises(lowerCAmelCase_ ) as cm:
mock_training_loop_function()
self.assertIn('''Oops, we had an error!''' , cm.exception.args[0] )
@require_cuda
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
_a = torch.cuda.memory_allocated()
_a = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , lowerCAmelCase_ )
_a = release_memory(lowerCAmelCase_ )
self.assertEqual(torch.cuda.memory_allocated() , lowerCAmelCase_ )
| 22 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=24 , _UpperCamelCase=2 , _UpperCamelCase=6 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=3 , _UpperCamelCase=None , _UpperCamelCase=1000 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def UpperCamelCase( self ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_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 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase( self ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
__A : Dict = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__A : Optional[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = False
__A : Optional[int] = False
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return True
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def UpperCamelCase( self ):
self.config_tester.run_common_tests()
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def UpperCamelCase( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
_UpperCAmelCase = torch.Size([1, 2, 768] )
_UpperCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) ) | 32 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
snake_case__ : str = """CompVis/stable-diffusion-v1-1"""
snake_case__ : Optional[Any] = """CompVis/stable-diffusion-v1-2"""
snake_case__ : List[Any] = """CompVis/stable-diffusion-v1-3"""
snake_case__ : int = """CompVis/stable-diffusion-v1-4"""
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = True , ) -> List[str]:
super()._init_()
UpperCamelCase_ = StableDiffusionPipeline.from_pretrained(_UpperCAmelCase )
UpperCamelCase_ = StableDiffusionPipeline.from_pretrained(_UpperCAmelCase )
UpperCamelCase_ = StableDiffusionPipeline.from_pretrained(_UpperCAmelCase )
UpperCamelCase_ = StableDiffusionPipeline(
vae=_UpperCAmelCase , text_encoder=_UpperCAmelCase , tokenizer=_UpperCAmelCase , unet=_UpperCAmelCase , scheduler=_UpperCAmelCase , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , requires_safety_checker=_UpperCAmelCase , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
return {k: getattr(self , _UpperCAmelCase ) for k in self.config.keys() if not k.startswith('_' )}
def _UpperCAmelCase ( self , _UpperCAmelCase = "auto" ) -> List[str]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> int:
self.enable_attention_slicing(_UpperCAmelCase )
@torch.no_grad()
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase = 512 , _UpperCAmelCase = 512 , _UpperCAmelCase = 50 , _UpperCAmelCase = 7.5 , _UpperCAmelCase = None , _UpperCAmelCase = 1 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "pil" , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 1 , **_UpperCAmelCase , ) -> Any:
return self.pipea(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
@torch.no_grad()
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase = 512 , _UpperCAmelCase = 512 , _UpperCAmelCase = 50 , _UpperCAmelCase = 7.5 , _UpperCAmelCase = None , _UpperCAmelCase = 1 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "pil" , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 1 , **_UpperCAmelCase , ) -> List[Any]:
return self.pipea(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
@torch.no_grad()
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase = 512 , _UpperCAmelCase = 512 , _UpperCAmelCase = 50 , _UpperCAmelCase = 7.5 , _UpperCAmelCase = None , _UpperCAmelCase = 1 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "pil" , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 1 , **_UpperCAmelCase , ) -> Optional[int]:
return self.pipea(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
@torch.no_grad()
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase = 512 , _UpperCAmelCase = 512 , _UpperCAmelCase = 50 , _UpperCAmelCase = 7.5 , _UpperCAmelCase = None , _UpperCAmelCase = 1 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "pil" , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 1 , **_UpperCAmelCase , ) -> Tuple:
return self.pipea(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
@torch.no_grad()
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase = 512 , _UpperCAmelCase = 512 , _UpperCAmelCase = 50 , _UpperCAmelCase = 7.5 , _UpperCAmelCase = None , _UpperCAmelCase = 1 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = "pil" , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 1 , **_UpperCAmelCase , ) -> List[Any]:
UpperCamelCase_ = 'cuda' if torch.cuda.is_available() else 'cpu'
self.to(_UpperCAmelCase )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}.""" )
# Get first result from Stable Diffusion Checkpoint v1.1
UpperCamelCase_ = self.textaimg_sda_a(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
# Get first result from Stable Diffusion Checkpoint v1.2
UpperCamelCase_ = self.textaimg_sda_a(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
# Get first result from Stable Diffusion Checkpoint v1.3
UpperCamelCase_ = self.textaimg_sda_a(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
# Get first result from Stable Diffusion Checkpoint v1.4
UpperCamelCase_ = self.textaimg_sda_a(
prompt=_UpperCAmelCase , height=_UpperCAmelCase , width=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , guidance_scale=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , latents=_UpperCAmelCase , output_type=_UpperCAmelCase , return_dict=_UpperCAmelCase , callback=_UpperCAmelCase , callback_steps=_UpperCAmelCase , **_UpperCAmelCase , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 23 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Tuple = """rwkv"""
__A : Any = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _UpperCamelCase=50277 , _UpperCamelCase=1024 , _UpperCamelCase=4096 , _UpperCamelCase=32 , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , _UpperCamelCase=0 , _UpperCamelCase=6 , _UpperCamelCase=False , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class lowerCAmelCase ( __lowerCAmelCase):
__lowercase : int = '''marian'''
__lowercase : Any = ['''past_key_values''']
__lowercase : List[Any] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , __SCREAMING_SNAKE_CASE=5_8101 , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=1024 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=4096 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=4096 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=1024 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=5_8100 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=5_8100 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=True , **__SCREAMING_SNAKE_CASE , ) -> str:
'''simple docstring'''
__snake_case = vocab_size
__snake_case = decoder_vocab_size or vocab_size
__snake_case = max_position_embeddings
__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 = encoder_layerdrop
__snake_case = decoder_layerdrop
__snake_case = use_cache
__snake_case = encoder_layers
__snake_case = scale_embedding # scale factor will be sqrt(d_model) if True
__snake_case = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , is_encoder_decoder=__SCREAMING_SNAKE_CASE , decoder_start_token_id=__SCREAMING_SNAKE_CASE , forced_eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
class lowerCAmelCase ( __lowerCAmelCase):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__snake_case = {0: '''batch'''}
__snake_case = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
__snake_case = {0: '''batch''', 1: '''decoder_sequence'''}
__snake_case = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
__snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__snake_case , __snake_case = self.num_layers
for i in range(__SCREAMING_SNAKE_CASE ):
__snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
__snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
__snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case = super().outputs
else:
__snake_case = super(__SCREAMING_SNAKE_CASE , self ).outputs
if self.use_past:
__snake_case , __snake_case = self.num_layers
for i in range(__SCREAMING_SNAKE_CASE ):
__snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
__snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
'''simple docstring'''
__snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Generate decoder inputs
__snake_case = seq_length if not self.use_past else 1
__snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
__snake_case = dict(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__snake_case , __snake_case = common_inputs['''input_ids'''].shape
__snake_case = common_inputs['''decoder_input_ids'''].shape[1]
__snake_case , __snake_case = self.num_attention_heads
__snake_case = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__snake_case = decoder_seq_length + 3
__snake_case = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__snake_case = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )] , dim=1 )
__snake_case = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__snake_case , __snake_case = self.num_layers
__snake_case = min(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = max(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) - min_num_layers
__snake_case = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__SCREAMING_SNAKE_CASE ):
common_inputs["past_key_values"].append(
(
torch.zeros(__SCREAMING_SNAKE_CASE ),
torch.zeros(__SCREAMING_SNAKE_CASE ),
torch.zeros(__SCREAMING_SNAKE_CASE ),
torch.zeros(__SCREAMING_SNAKE_CASE ),
) )
# TODO: test this.
__snake_case = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) )
return common_inputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
'''simple docstring'''
__snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__snake_case , __snake_case = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__snake_case = seqlen + 2
__snake_case , __snake_case = self.num_layers
__snake_case , __snake_case = self.num_attention_heads
__snake_case = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__snake_case = common_inputs['''attention_mask'''].dtype
__snake_case = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )] , dim=1 )
__snake_case = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(__SCREAMING_SNAKE_CASE )
]
return common_inputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
'''simple docstring'''
__snake_case = compute_effective_axis_dimension(
__SCREAMING_SNAKE_CASE , 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
__snake_case = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE )
__snake_case = compute_effective_axis_dimension(
__SCREAMING_SNAKE_CASE , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__SCREAMING_SNAKE_CASE )
# Generate dummy inputs according to compute batch and sequence
__snake_case = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
__snake_case = dict(tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE ) )
return common_inputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = -1 , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , is_pair=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
else:
__snake_case = self._generate_dummy_inputs_for_causal_lm(
__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , is_pair=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
return common_inputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case = super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
else:
__snake_case = super(__SCREAMING_SNAKE_CASE , self )._flatten_past_key_values_(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@property
def lowerCAmelCase ( self ) -> float:
'''simple docstring'''
return 1E-4
| 24 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase__ ( _a , _a , _a = None):
if version.parse(hfh.__version__).release < version.parse("0.11.0").release:
# old versions of hfh don't url-encode the file path
SCREAMING_SNAKE_CASE : Dict = quote(_a)
return hfh.hf_hub_url(_a , _a , repo_type="dataset" , revision=_a) | 25 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Dict = """falcon"""
__A : Any = ["""past_key_values"""]
def __init__( self , _UpperCamelCase=65024 , _UpperCamelCase=4544 , _UpperCamelCase=32 , _UpperCamelCase=71 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=11 , _UpperCamelCase=11 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop('''n_embed''' , _UpperCamelCase )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCAmelCase = alibi
_UpperCAmelCase = new_decoder_architecture
_UpperCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_UpperCAmelCase = parallel_attn
_UpperCAmelCase = bias
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
return self.hidden_size // self.num_attention_heads
@property
def UpperCamelCase( self ):
return not self.alibi | 32 | 0 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
__UpperCamelCase = datasets.logging.get_logger(__name__)
__UpperCamelCase = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n booktitle = {Proceedings of the Fifth Conference on Machine Translation},\n month = {November},\n year = {2020},\n address = {Online},\n publisher = {Association for Computational Linguistics},\n pages = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n author = \"Rei, Ricardo and\n Stewart, Craig and\n Farinha, Ana C and\n Lavie, Alon\",\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n pages = \"2685--2702\",\n}\n"
__UpperCamelCase = "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"
__UpperCamelCase = "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n `scores`: List of scores.\n\nExamples:\n\n >>> comet_metric = datasets.load_metric('comet')\n >>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use\n >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n >>> print([round(v, 2) for v in results[\"scores\"]])\n [0.19, 0.92]\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
def lowercase__ ( self : str ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://unbabel.github.io/COMET/html/index.html""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""sources""": datasets.Value("""string""" , id="""sequence""" ),
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/Unbabel/COMET"""] , reference_urls=[
"""https://github.com/Unbabel/COMET""",
"""https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
"""http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
] , )
def lowercase__ ( self : int , __magic_name__ : Any ) -> Any:
"""simple docstring"""
if self.config_name == "default":
__snake_case : Tuple = comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da""" ) )
else:
__snake_case : Any = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def lowercase__ ( self : Union[str, Any] , __magic_name__ : Dict , __magic_name__ : List[str] , __magic_name__ : Any , __magic_name__ : int=None , __magic_name__ : str=False ) -> List[str]:
"""simple docstring"""
if gpus is None:
__snake_case : List[str] = 1 if torch.cuda.is_available() else 0
__snake_case : int = {"""src""": sources, """mt""": predictions, """ref""": references}
__snake_case : Optional[Any] = [dict(zip(__magic_name__ , __magic_name__ ) ) for t in zip(*data.values() )]
__snake_case , __snake_case : List[Any] = self.scorer.predict(__magic_name__ , gpus=__magic_name__ , progress_bar=__magic_name__ )
return {"mean_score": mean_score, "scores": scores}
| 26 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_ ):
_A = data
_A = [0X6745_2301, 0XEFCD_AB89, 0X98BA_DCFE, 0X1032_5476, 0XC3D2_E1F0]
@staticmethod
def lowerCAmelCase__ ( snake_case_ , snake_case_ ):
return ((n << b) | (n >> (32 - b))) & 0XFFFF_FFFF
def lowerCAmelCase__ ( self ):
_A = B'\x80' + B'\x00' * (63 - (len(self.data ) + 8) % 64)
_A = self.data + padding + struct.pack('>Q' , 8 * len(self.data ) )
return padded_data
def lowerCAmelCase__ ( self ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def lowerCAmelCase__ ( self , snake_case_ ):
_A = list(struct.unpack('>16L' , snake_case_ ) ) + [0] * 64
for i in range(16 , 80 ):
_A = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def lowerCAmelCase__ ( self ):
_A = self.padding()
_A = self.split_blocks()
for block in self.blocks:
_A = self.expand_block(snake_case_ )
_A, _A, _A, _A, _A = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
_A = (b & c) | ((~b) & d)
_A = 0X5A82_7999
elif 20 <= i < 40:
_A = b ^ c ^ d
_A = 0X6ED9_EBA1
elif 40 <= i < 60:
_A = (b & c) | (b & d) | (c & d)
_A = 0X8F1B_BCDC
elif 60 <= i < 80:
_A = b ^ c ^ d
_A = 0XCA62_C1D6
_A, _A, _A, _A, _A = (
self.rotate(snake_case_ , 5 ) + f + e + k + expanded_block[i] & 0XFFFF_FFFF,
a,
self.rotate(snake_case_ , 30 ),
c,
d,
)
_A = (
self.h[0] + a & 0XFFFF_FFFF,
self.h[1] + b & 0XFFFF_FFFF,
self.h[2] + c & 0XFFFF_FFFF,
self.h[3] + d & 0XFFFF_FFFF,
self.h[4] + e & 0XFFFF_FFFF,
)
return ("{:08x}" * 5).format(*self.h )
def __lowerCAmelCase( ) -> Any:
"""simple docstring"""
_A = b'Test String'
assert SHAaHash(_SCREAMING_SNAKE_CASE ).final_hash() == hashlib.shaa(_SCREAMING_SNAKE_CASE ).hexdigest() # noqa: S324
def __lowerCAmelCase( ) -> int:
"""simple docstring"""
_A = argparse.ArgumentParser(description='Process some strings or files' )
parser.add_argument(
'--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument('--file' , dest='input_file' , help='Hash contents of a file' )
_A = parser.parse_args()
_A = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
_A = f.read()
else:
_A = bytes(_SCREAMING_SNAKE_CASE , 'utf-8' )
print(SHAaHash(_SCREAMING_SNAKE_CASE ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 27 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 0:
return False
_UpperCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [1]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = 0, 0, 0
SCREAMING_SNAKE_CASE : Dict = ugly_nums[ia] * 2
SCREAMING_SNAKE_CASE : Optional[int] = ugly_nums[ia] * 3
SCREAMING_SNAKE_CASE : Any = ugly_nums[ia] * 5
for _ in range(1 ,__UpperCamelCase ):
SCREAMING_SNAKE_CASE : int = min(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
ugly_nums.append(__UpperCamelCase )
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE : Tuple = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE : Dict = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(F"""{ugly_numbers(2_0_0) = }""")
| 28 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__A : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__A : ClassVar[Features] = Features({} )
__A : str = "text"
@property
def UpperCamelCase( self ):
return {self.text_column: "text"} | 32 | 0 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def lowercase ( lowerCAmelCase__ ):
# getting number of pixels in the image
lowerCamelCase_ , lowerCamelCase_ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(lowerCAmelCase__ ):
for j in range(lowerCAmelCase__ ):
lowerCamelCase_ = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
A_ = imread("""image_data/lena.jpg""", 1)
# convert to its negative
A_ = convert_to_negative(img)
# show result image
imshow("""negative of original image""", img)
waitKey(0)
destroyAllWindows()
| 29 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"vocab_file": "spiece.model"}
UpperCAmelCase_ = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
UpperCAmelCase_ = "▁"
class __UpperCamelCase ( A__ ):
__A : Any = VOCAB_FILES_NAMES
__A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , _UpperCamelCase , _UpperCamelCase="</s>" , _UpperCamelCase="<unk>" , _UpperCamelCase="<pad>" , _UpperCamelCase=100 , _UpperCamelCase=None , _UpperCamelCase = None , _UpperCamelCase=True , **_UpperCamelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase = len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) ) , _UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
_UpperCAmelCase = legacy
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , extra_ids=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = extra_ids
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@staticmethod
def UpperCamelCase( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_UpperCAmelCase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCamelCase , )
return max_model_length
@property
def UpperCamelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCamelCase( self ):
_UpperCAmelCase = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_UpperCamelCase )) + [1]
return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def UpperCamelCase( self ):
return list(
set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase( self ):
return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCamelCase( self , _UpperCamelCase ):
if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _UpperCamelCase ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_UpperCAmelCase = SPIECE_UNDERLINE + text.replace(_UpperCamelCase , ''' ''' )
return super().tokenize(_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
if not self.legacy:
_UpperCAmelCase = text.startswith(_UpperCamelCase )
if is_first:
_UpperCAmelCase = text[1:]
_UpperCAmelCase = self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
_UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCamelCase( self , _UpperCamelCase ):
if token.startswith('''<extra_id_''' ):
_UpperCAmelCase = re.match(R'''<extra_id_(\d+)>''' , _UpperCamelCase )
_UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase ):
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(_UpperCamelCase )
else:
_UpperCAmelCase = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(_UpperCamelCase )
_UpperCAmelCase = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,) | 32 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__a = 16
__a = 32
def lowerCamelCase__ ( _lowercase , _lowercase = 16 , _lowercase = "bert-base-cased" ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = AutoTokenizer.from_pretrained(_lowercase )
UpperCAmelCase_ : int = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_lowercase ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ : Any = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_lowercase , max_length=_lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ : Tuple = datasets.map(
_lowercase , batched=_lowercase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=_lowercase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ : Union[str, Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowercase , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(_lowercase , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCAmelCase_ : Union[str, Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
UpperCAmelCase_ : int = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
return train_dataloader, eval_dataloader
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
model.eval()
UpperCAmelCase_ : List[str] = 0
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_ : Any = model(**_lowercase )
UpperCAmelCase_ : Optional[Any] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_, UpperCAmelCase_ : int = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(_lowercase ) - 1:
UpperCAmelCase_ : Dict = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=_lowercase , references=_lowercase , )
UpperCAmelCase_ : Union[str, Any] = metric.compute()
return eval_metric["accuracy"]
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : int = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ : Optional[Any] = config['''lr''']
UpperCAmelCase_ : Union[str, Any] = int(config['''num_epochs'''] )
UpperCAmelCase_ : Optional[Any] = int(config['''seed'''] )
UpperCAmelCase_ : int = int(config['''batch_size'''] )
UpperCAmelCase_ : List[str] = args.model_name_or_path
set_seed(_lowercase )
UpperCAmelCase_, UpperCAmelCase_ : int = get_dataloaders(_lowercase , _lowercase , _lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowercase , return_dict=_lowercase )
# Instantiate optimizer
UpperCAmelCase_ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ : str = optimizer_cls(params=model.parameters() , lr=_lowercase )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ : Optional[int] = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
UpperCAmelCase_ : List[Any] = 1
UpperCAmelCase_ : Optional[Any] = (len(_lowercase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_ : List[str] = get_linear_schedule_with_warmup(
optimizer=_lowercase , num_warmup_steps=0 , num_training_steps=_lowercase , )
else:
UpperCAmelCase_ : List[str] = DummyScheduler(_lowercase , total_num_steps=_lowercase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Union[str, Any] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ : Tuple = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ : Optional[Any] = 0
UpperCAmelCase_ : str = evaluate.load('''glue''' , '''mrpc''' )
UpperCAmelCase_ : List[str] = num_epochs
if args.partial_train_epoch is not None:
UpperCAmelCase_ : Union[str, Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
UpperCAmelCase_ : Dict = args.resume_from_checkpoint.split('''epoch_''' )[1]
UpperCAmelCase_ : int = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
UpperCAmelCase_ : Union[str, Any] = int(_lowercase ) + 1
UpperCAmelCase_ : List[str] = evaluation_loop(_lowercase , _lowercase , _lowercase , _lowercase )
accelerator.print('''resumed checkpoint performance:''' , _lowercase )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' , lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' , optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir , f'''state_{starting_epoch-1}.json''' ) , '''r''' ) as f:
UpperCAmelCase_ : Any = json.load(_lowercase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
UpperCAmelCase_ : Union[str, Any] = {}
for epoch in range(_lowercase , _lowercase ):
model.train()
for step, batch in enumerate(_lowercase ):
UpperCAmelCase_ : List[str] = model(**_lowercase )
UpperCAmelCase_ : int = outputs.loss
UpperCAmelCase_ : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(_lowercase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
UpperCAmelCase_ : str = f'''epoch_{epoch}'''
UpperCAmelCase_ : Optional[Any] = os.path.join(args.output_dir , _lowercase )
accelerator.save_state(_lowercase )
UpperCAmelCase_ : Any = evaluation_loop(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase_ : Union[str, Any] = accuracy
UpperCAmelCase_ : List[str] = lr_scheduler.get_lr()[0]
UpperCAmelCase_ : List[Any] = optimizer.param_groups[0]['''lr''']
UpperCAmelCase_ : Any = epoch
UpperCAmelCase_ : Any = overall_step
accelerator.print(f'''epoch {epoch}:''' , _lowercase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'''state_{epoch}.json''' ) , '''w''' ) as f:
json.dump(_lowercase , _lowercase )
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=_lowercase , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=_lowercase , )
parser.add_argument(
'''--output_dir''' , type=_lowercase , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=_lowercase , default=_lowercase , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--partial_train_epoch''' , type=_lowercase , default=_lowercase , help='''If passed, the training will stop after this number of epochs.''' , )
parser.add_argument(
'''--num_epochs''' , type=_lowercase , default=2 , help='''Number of train epochs.''' , )
UpperCAmelCase_ : Union[str, Any] = parser.parse_args()
UpperCAmelCase_ : Dict = {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(_lowercase , _lowercase )
if __name__ == "__main__":
main() | 30 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
lowerCamelCase__ : int = tuple[float, float, float]
lowerCamelCase__ : Optional[Any] = tuple[float, float, float]
def UpperCAmelCase_ ( __UpperCAmelCase : Pointad , __UpperCAmelCase : Pointad ) -> Vectorad:
SCREAMING_SNAKE_CASE_ = end_pointa[0] - end_pointa[0]
SCREAMING_SNAKE_CASE_ = end_pointa[1] - end_pointa[1]
SCREAMING_SNAKE_CASE_ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def UpperCAmelCase_ ( __UpperCAmelCase : Vectorad , __UpperCAmelCase : Vectorad ) -> Vectorad:
SCREAMING_SNAKE_CASE_ = ab[1] * ac[2] - ab[2] * ac[1] # *i
SCREAMING_SNAKE_CASE_ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
SCREAMING_SNAKE_CASE_ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def UpperCAmelCase_ ( __UpperCAmelCase : Vectorad , __UpperCAmelCase : int ) -> bool:
return tuple(round(__UpperCAmelCase , __UpperCAmelCase ) for x in vector ) == (0, 0, 0)
def UpperCAmelCase_ ( __UpperCAmelCase : Pointad , __UpperCAmelCase : Pointad , __UpperCAmelCase : Pointad , __UpperCAmelCase : int = 10 ) -> bool:
SCREAMING_SNAKE_CASE_ = create_vector(__UpperCAmelCase , __UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = create_vector(__UpperCAmelCase , __UpperCAmelCase )
return is_zero_vector(get_ad_vectors_cross(__UpperCAmelCase , __UpperCAmelCase ) , __UpperCAmelCase ) | 31 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : str = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
"""tokenization_deberta""": ["""DebertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Any = ["""DebertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : List[str] = [
"""DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DebertaForMaskedLM""",
"""DebertaForQuestionAnswering""",
"""DebertaForSequenceClassification""",
"""DebertaForTokenClassification""",
"""DebertaModel""",
"""DebertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Tuple = [
"""TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDebertaForMaskedLM""",
"""TFDebertaForQuestionAnswering""",
"""TFDebertaForSequenceClassification""",
"""TFDebertaForTokenClassification""",
"""TFDebertaModel""",
"""TFDebertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
lowerCamelCase__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 33 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_=7 , lowerCamelCase_=3 , lowerCamelCase_=1_8 , lowerCamelCase_=3_0 , lowerCamelCase_=4_0_0 , lowerCamelCase_=True , lowerCamelCase_=3_2 , lowerCamelCase_=True , ) -> Union[str, Any]:
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size_divisor
UpperCamelCase = do_rescale
def UpperCAmelCase__ ( self) -> Any:
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class snake_case_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
A_ = GLPNImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self) -> List[Any]:
UpperCamelCase = GLPNImageProcessingTester(self)
@property
def UpperCAmelCase__ ( self) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self) -> Optional[int]:
UpperCamelCase = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCamelCase_ , '''do_resize'''))
self.assertTrue(hasattr(lowerCamelCase_ , '''size_divisor'''))
self.assertTrue(hasattr(lowerCamelCase_ , '''resample'''))
self.assertTrue(hasattr(lowerCamelCase_ , '''do_rescale'''))
def UpperCAmelCase__ ( self) -> str:
pass
def UpperCAmelCase__ ( self) -> Optional[Any]:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase_)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , Image.Image)
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def UpperCAmelCase__ ( self) -> Optional[Any]:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase_ , numpify=lowerCamelCase_)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , np.ndarray)
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def UpperCAmelCase__ ( self) -> Optional[int]:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase_ , torchify=lowerCamelCase_)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ , torch.Tensor)
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0) | 34 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( A__ , unittest.TestCase ):
__A : Any = DanceDiffusionPipeline
__A : Any = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__A : Tuple = PipelineTesterMixin.required_optional_params - {
"""callback""",
"""latents""",
"""callback_steps""",
"""output_type""",
"""num_images_per_prompt""",
}
__A : Tuple = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__A : List[str] = False
__A : str = False
def UpperCamelCase( self ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_UpperCamelCase , use_timestep_embedding=_UpperCamelCase , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
_UpperCAmelCase = IPNDMScheduler()
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=0 ):
if str(_UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(_UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def UpperCamelCase( self ):
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = DanceDiffusionPipeline(**_UpperCamelCase )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(_UpperCamelCase )
_UpperCAmelCase = pipe(**_UpperCamelCase )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_local()
@skip_mps
def UpperCamelCase( self ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase( self ):
return super().test_attention_slicing_forward_pass()
def UpperCamelCase( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2 | 32 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = input_paths_and_base_extractors[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(A__ )
assert base_extractor.is_extractable(A__ )
SCREAMING_SNAKE_CASE__ : Tuple = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(A__ , A__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ : List[str] = file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE__ : Tuple = output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE__ : List[str] = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
SCREAMING_SNAKE_CASE__ : int = input_paths[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ : int = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(A__ )
SCREAMING_SNAKE_CASE__ : Any = Extractor.infer_extractor_format(A__ )
assert extractor_format is not None
SCREAMING_SNAKE_CASE__ : Any = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(A__ , A__ , A__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ : List[Any] = file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE__ : List[Any] = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( A__ , A__ ) -> str:
'''simple docstring'''
import tarfile
SCREAMING_SNAKE_CASE__ : Optional[int] = tmp_path / '''data_dot_dot'''
directory.mkdir()
SCREAMING_SNAKE_CASE__ : str = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(A__ , '''w''' ) as f:
f.add(A__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( A__ ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
SCREAMING_SNAKE_CASE__ : Optional[int] = tmp_path / '''data_sym_link'''
directory.mkdir()
SCREAMING_SNAKE_CASE__ : Tuple = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=A__ )
with tarfile.TarFile(A__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
SCREAMING_SNAKE_CASE__ : Tuple = insecure_tar_files[insecure_tar_file]
SCREAMING_SNAKE_CASE__ : List[Any] = tmp_path / '''extracted'''
TarExtractor.extract(A__ , A__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( A__ ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
SCREAMING_SNAKE_CASE__ : Tuple = (
b'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
b'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
b'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
b'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(A__ )
assert zipfile.is_zipfile(str(A__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(A__ ) # but we're right
| 35 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , **__A )
snake_case : Tuple = AutoModelForSeqaSeqLM.from_config(__A )
model.save_pretrained(__A )
AutoTokenizer.from_pretrained(__A ).save_pretrained(__A )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 36 |
import baseaa
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A__ ( SCREAMING_SNAKE_CASE_ : bytes ) -> str:
"""simple docstring"""
return baseaa.baadecode(SCREAMING_SNAKE_CASE_ ).decode('''utf-8''' )
if __name__ == "__main__":
UpperCAmelCase_ = "Hello World!"
UpperCAmelCase_ = baseaa_encode(test)
print(encoded)
UpperCAmelCase_ = baseaa_decode(encoded)
print(decoded) | 32 | 0 |
import gc
import threading
import time
import psutil
import torch
class A__ :
"""simple docstring"""
def __init__( self : int ):
a__ : Optional[int] = psutil.Process()
a__ : Union[str, Any] = False
def _UpperCamelCase( self : Any ):
a__ : str = -1
while True:
a__ : Dict = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def _UpperCamelCase( self : Dict ):
a__ : int = True
a__ : Tuple = threading.Thread(target=self.peak_monitor )
a__ : Dict = True
self.thread.start()
def _UpperCamelCase( self : Any ):
a__ : Dict = False
self.thread.join()
return self.cpu_memory_peak
UpperCamelCase : List[str] = PeakCPUMemory()
def UpperCamelCase_ ( ) -> int:
# Time
a__ : List[Any] = {"time": time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
a__ : Union[str, Any] = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
a__ : Dict = torch.cuda.memory_allocated(__a )
torch.cuda.reset_peak_memory_stats()
return measures
def UpperCamelCase_ ( __a ) -> Any:
# Time
a__ : Union[str, Any] = {"time": time.time() - start_measures["time"]}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
a__ : Any = (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
a__ : Optional[int] = (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20
# GPU mem
for i in range(torch.cuda.device_count() ):
a__ : Optional[Any] = (torch.cuda.memory_allocated(__a ) - start_measures[str(__a )]) / 2**20
a__ : int = (torch.cuda.max_memory_allocated(__a ) - start_measures[str(__a )]) / 2**20
return measures
def UpperCamelCase_ ( __a , __a ) -> Optional[Any]:
print(f'''{description}:''' )
print(f'''- Time: {measures['time']:.2f}s''' )
for i in range(torch.cuda.device_count() ):
print(f'''- GPU {i} allocated: {measures[str(__a )]:.2f}MiB''' )
a__ : Dict = measures[f'''{i}-peak''']
print(f'''- GPU {i} peak: {peak:.2f}MiB''' )
print(f'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''' )
print(f'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''' )
| 37 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
__A : int = ["""pixel_values"""]
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1 / 255 , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
super().__init__(**_UpperCamelCase )
_UpperCAmelCase = size if size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name='''crop_size''' )
_UpperCAmelCase = do_resize
_UpperCAmelCase = do_rescale
_UpperCAmelCase = do_normalize
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = size
_UpperCAmelCase = resample
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if "shortest_edge" in size:
_UpperCAmelCase = get_resize_output_image_size(_UpperCamelCase , size=size['''shortest_edge'''] , default_to_square=_UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = 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, width). Got {size.keys()}''' )
return center_crop(_UpperCamelCase , size=(size['''height'''], size['''width''']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( 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 , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = get_size_dict(_UpperCamelCase , param_name='''crop_size''' , default_to_square=_UpperCamelCase )
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if not is_batched(_UpperCamelCase ):
_UpperCAmelCase = [images]
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.''' )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
_UpperCAmelCase = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
_UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ):
super().__init__(
features=__SCREAMING_SNAKE_CASE , cache_dir=__SCREAMING_SNAKE_CASE , keep_in_memory=__SCREAMING_SNAKE_CASE , streaming=__SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
snake_case__ : Tuple = Generator(
cache_dir=__SCREAMING_SNAKE_CASE , features=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , gen_kwargs=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
def __UpperCamelCase ( self ):
# Build iterable dataset
if self.streaming:
snake_case__ : Dict = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
snake_case__ : Optional[Any] = None
snake_case__ : List[Any] = None
snake_case__ : Dict = None
snake_case__ : int = None
self.builder.download_and_prepare(
download_config=__SCREAMING_SNAKE_CASE , download_mode=__SCREAMING_SNAKE_CASE , verification_mode=__SCREAMING_SNAKE_CASE , base_path=__SCREAMING_SNAKE_CASE , num_proc=self.num_proc , )
snake_case__ : Optional[int] = self.builder.as_dataset(
split="""train""" , verification_mode=__SCREAMING_SNAKE_CASE , in_memory=self.keep_in_memory )
return dataset
| 38 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] ) | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 39 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = [0 for i in range(n + 1 )]
_UpperCAmelCase = 1
_UpperCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = 1
_UpperCAmelCase = 0
for i in range(SCREAMING_SNAKE_CASE_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
def UpperCamelCase ( snake_case__ : list[list[int | float]] ) -> int:
UpperCamelCase : Tuple = len(snake_case__ )
UpperCamelCase : Tuple = len(matrix[0] )
UpperCamelCase : Tuple = min(snake_case__ , snake_case__ )
for row in range(snake_case__ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , snake_case__ ):
UpperCamelCase : Dict = matrix[col][row] / matrix[row][row]
for i in range(snake_case__ , snake_case__ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCamelCase : Optional[int] = True
for i in range(row + 1 , snake_case__ ):
if matrix[i][row] != 0:
UpperCamelCase , UpperCamelCase : Union[str, Any] = matrix[i], matrix[row]
UpperCamelCase : List[str] = False
break
if reduce:
rank -= 1
for i in range(snake_case__ ):
UpperCamelCase : Optional[Any] = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , _UpperCamelCase , )
super().__init__(*_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = list(range(len(A__ ) ) )
__lowercase = [v / w for v, w in zip(A__ , A__ )]
index.sort(key=lambda A__ : ratio[i] , reverse=A__ )
__lowercase = 0
__lowercase = [0] * len(A__ )
for i in index:
if weight[i] <= capacity:
__lowercase = 1
max_value += value[i]
capacity -= weight[i]
else:
__lowercase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 41 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict = ["""image_processor""", """tokenizer"""]
__A : List[str] = """BridgeTowerImageProcessor"""
__A : str = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = 0 , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = True , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = self.tokenizer(
text=_UpperCamelCase , add_special_tokens=_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , max_length=_UpperCamelCase , stride=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_token_type_ids=_UpperCamelCase , return_attention_mask=_UpperCamelCase , return_overflowing_tokens=_UpperCamelCase , return_special_tokens_mask=_UpperCamelCase , return_offsets_mapping=_UpperCamelCase , return_length=_UpperCamelCase , verbose=_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase , )
# add pixel_values + pixel_mask
_UpperCAmelCase = self.image_processor(
_UpperCamelCase , return_tensors=_UpperCamelCase , do_normalize=_UpperCamelCase , do_center_crop=_UpperCamelCase , **_UpperCamelCase )
encoding.update(_UpperCamelCase )
return encoding
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
_UpperCAmelCase = self.tokenizer.model_input_names
_UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 32 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> int:
assert column_title.isupper()
lowerCamelCase_ = 0
lowerCamelCase_ = len(__UpperCamelCase ) - 1
lowerCamelCase_ = 0
while index >= 0:
lowerCamelCase_ = (ord(column_title[index] ) - 64) * pow(26 ,__UpperCamelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 42 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 32 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _a ( UpperCamelCase__ ):
def __init__( self: int , UpperCamelCase_: pyspark.sql.DataFrame , UpperCamelCase_: Optional[NamedSplit] = None , UpperCamelCase_: Optional[Features] = None , UpperCamelCase_: bool = True , UpperCamelCase_: str = None , UpperCamelCase_: bool = False , UpperCamelCase_: str = None , UpperCamelCase_: bool = True , UpperCamelCase_: str = "arrow" , **UpperCamelCase_: List[Any] , ) -> Any:
"""simple docstring"""
super().__init__(
split=UpperCamelCase_ , features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase__ = load_from_cache_file
lowercase__ = file_format
lowercase__ = Spark(
df=UpperCamelCase_ , features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , working_dir=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCamelCase_ ( self: Tuple ) -> Dict:
"""simple docstring"""
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowercase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCamelCase_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 43 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __UpperCamelCase ( A__ ):
__A : Any = """biogpt"""
def __init__( self , _UpperCamelCase=42384 , _UpperCamelCase=1024 , _UpperCamelCase=24 , _UpperCamelCase=16 , _UpperCamelCase=4096 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=1024 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-12 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_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 = layer_norm_eps
_UpperCAmelCase = scale_embedding
_UpperCAmelCase = use_cache
_UpperCAmelCase = layerdrop
_UpperCAmelCase = activation_dropout
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'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 UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'bert'
def __init__( self : Dict,__A : str=3_0_5_2_2,__A : List[Any]=7_6_8,__A : Union[str, Any]=1_2,__A : Any=1_2,__A : str=3_0_7_2,__A : List[str]="gelu",__A : int=0.1,__A : Any=0.1,__A : int=5_1_2,__A : Union[str, Any]=2,__A : List[str]=0.02,__A : Tuple=1e-12,__A : Optional[Any]=0,__A : int="absolute",__A : str=True,__A : Any=None,**__A : str,):
super().__init__(pad_token_id=__A,**__A )
_lowerCamelCase : str = vocab_size
_lowerCamelCase : Optional[Any] = hidden_size
_lowerCamelCase : Optional[Any] = num_hidden_layers
_lowerCamelCase : List[str] = num_attention_heads
_lowerCamelCase : Any = hidden_act
_lowerCamelCase : Union[str, Any] = intermediate_size
_lowerCamelCase : int = hidden_dropout_prob
_lowerCamelCase : Optional[int] = attention_probs_dropout_prob
_lowerCamelCase : List[str] = max_position_embeddings
_lowerCamelCase : Optional[int] = type_vocab_size
_lowerCamelCase : Optional[Any] = initializer_range
_lowerCamelCase : int = layer_norm_eps
_lowerCamelCase : Tuple = position_embedding_type
_lowerCamelCase : List[Any] = use_cache
_lowerCamelCase : str = classifier_dropout
class UpperCAmelCase__ ( A ):
@property
def lowerCamelCase_ ( self : List[Any] ):
if self.task == "multiple-choice":
_lowerCamelCase : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
_lowerCamelCase : Optional[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 44 |
from typing import List
from .keymap import KEYMAP, get_character
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : List[Any] ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
def A__ ( *SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : Any ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
class __UpperCamelCase ( A__ ):
def __new__( cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = super().__new__(cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if not hasattr(_UpperCamelCase , '''key_handler''' ):
setattr(_UpperCamelCase , '''key_handler''' , {} )
setattr(_UpperCamelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCAmelCase = getattr(_UpperCamelCase , '''handle_key''' , [] )
for key in handled_keys:
_UpperCAmelCase = value
return new_cls
@staticmethod
def UpperCamelCase( cls ):
_UpperCAmelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCAmelCase = ord(_UpperCamelCase )
_UpperCAmelCase = cls.key_handler.get(_UpperCamelCase )
if handler:
_UpperCAmelCase = char
return handler(cls )
else:
return None
def A__ ( cls : Union[str, Any] ) -> Any:
"""simple docstring"""
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() ) | 32 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __a ( self :Optional[int] ):
UpperCamelCase__ :List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase__ , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(lowerCamelCase__ , """num_attention_heads""" ) )
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :Dict , lowerCamelCase__ :Optional[Any] , lowerCamelCase__ :str=13 , lowerCamelCase__ :Dict=64 , lowerCamelCase__ :Dict=3 , lowerCamelCase__ :Union[str, Any]=3 , lowerCamelCase__ :Dict=2 , lowerCamelCase__ :Tuple=1 , lowerCamelCase__ :Tuple=16 , lowerCamelCase__ :int=[1_28, 2_56, 3_84] , lowerCamelCase__ :int=[4, 6, 8] , lowerCamelCase__ :Optional[Any]=[2, 3, 4] , lowerCamelCase__ :int=[16, 16, 16] , lowerCamelCase__ :List[Any]=0 , lowerCamelCase__ :List[Any]=[2, 2, 2] , lowerCamelCase__ :List[Any]=[2, 2, 2] , lowerCamelCase__ :Dict=0.02 , lowerCamelCase__ :Tuple=True , lowerCamelCase__ :Any=True , lowerCamelCase__ :int=2 , ):
UpperCamelCase__ :Optional[Any] = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :Optional[int] = image_size
UpperCamelCase__ :Any = num_channels
UpperCamelCase__ :List[Any] = kernel_size
UpperCamelCase__ :List[Any] = stride
UpperCamelCase__ :Optional[Any] = padding
UpperCamelCase__ :Optional[int] = hidden_sizes
UpperCamelCase__ :Tuple = num_attention_heads
UpperCamelCase__ :Optional[int] = depths
UpperCamelCase__ :Dict = key_dim
UpperCamelCase__ :Tuple = drop_path_rate
UpperCamelCase__ :Optional[Any] = patch_size
UpperCamelCase__ :Optional[Any] = attention_ratio
UpperCamelCase__ :List[str] = mlp_ratio
UpperCamelCase__ :int = initializer_range
UpperCamelCase__ :List[Any] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
UpperCamelCase__ :int = is_training
UpperCamelCase__ :Union[str, Any] = use_labels
UpperCamelCase__ :Optional[int] = num_labels
UpperCamelCase__ :Any = initializer_range
def __a ( self :str ):
UpperCamelCase__ :Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ :int = None
if self.use_labels:
UpperCamelCase__ :List[Any] = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__ :Optional[Any] = self.get_config()
return config, pixel_values, labels
def __a ( self :Union[str, Any] ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def __a ( self :List[str] , lowerCamelCase__ :Any , lowerCamelCase__ :Tuple , lowerCamelCase__ :Tuple ):
UpperCamelCase__ :Any = LevitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
UpperCamelCase__ :List[str] = model(lowerCamelCase__ )
UpperCamelCase__ :Tuple = (self.image_size, self.image_size)
UpperCamelCase__ , UpperCamelCase__ :List[Any] = image_size[0], image_size[1]
for _ in range(4 ):
UpperCamelCase__ :List[str] = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
UpperCamelCase__ :str = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def __a ( self :Dict , lowerCamelCase__ :Optional[Any] , lowerCamelCase__ :Optional[Any] , lowerCamelCase__ :Optional[Any] ):
UpperCamelCase__ :Optional[Any] = self.num_labels
UpperCamelCase__ :Union[str, Any] = LevitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
UpperCamelCase__ :List[Any] = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __a ( self :Optional[Any] ):
UpperCamelCase__ :str = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Tuple = config_and_inputs
UpperCamelCase__ :Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_snake_case : int = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
_snake_case : Union[str, Any] = (
{
"""feature-extraction""": LevitModel,
"""image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_snake_case : List[str] = False
_snake_case : Dict = False
_snake_case : Dict = False
_snake_case : List[Any] = False
_snake_case : Dict = False
def __a ( self :Optional[int] ):
UpperCamelCase__ :Optional[Any] = LevitModelTester(self )
UpperCamelCase__ :int = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def __a ( self :Optional[int] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __a ( self :Union[str, Any] ):
return
@unittest.skip(reason="""Levit does not use inputs_embeds""" )
def __a ( self :Union[str, Any] ):
pass
@unittest.skip(reason="""Levit does not support input and output embeddings""" )
def __a ( self :Tuple ):
pass
@unittest.skip(reason="""Levit does not output attentions""" )
def __a ( self :int ):
pass
def __a ( self :Union[str, Any] ):
UpperCamelCase__ , UpperCamelCase__ :str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :List[str] = model_class(lowerCamelCase__ )
UpperCamelCase__ :Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ :Tuple = [*signature.parameters.keys()]
UpperCamelCase__ :Optional[int] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def __a ( self :int ):
def check_hidden_states_output(lowerCamelCase__ :Dict , lowerCamelCase__ :Dict , lowerCamelCase__ :str ):
UpperCamelCase__ :Dict = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
UpperCamelCase__ :Union[str, Any] = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
UpperCamelCase__ :Optional[int] = outputs.hidden_states
UpperCamelCase__ :Dict = len(self.model_tester.depths ) + 1
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
UpperCamelCase__ :int = (self.model_tester.image_size, self.model_tester.image_size)
UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = image_size[0], image_size[1]
for _ in range(4 ):
UpperCamelCase__ :Dict = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
UpperCamelCase__ :Optional[int] = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
UpperCamelCase__ , UpperCamelCase__ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :int = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ :Dict = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __a ( self :Dict ):
pass
def __a ( self :Dict , lowerCamelCase__ :Optional[int] , lowerCamelCase__ :List[str] , lowerCamelCase__ :List[str]=False ):
UpperCamelCase__ :int = super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __a ( self :Dict ):
UpperCamelCase__ :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def __a ( self :Tuple ):
UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
def __a ( self :List[Any] ):
if not self.model_tester.is_training:
return
UpperCamelCase__ , UpperCamelCase__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :str = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase__ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
UpperCamelCase__ :str = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
UpperCamelCase__ :str = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = model(**lowerCamelCase__ ).loss
loss.backward()
def __a ( self :Tuple ):
UpperCamelCase__ , UpperCamelCase__ :Any = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCamelCase__ :Union[str, Any] = False
UpperCamelCase__ :Optional[int] = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
UpperCamelCase__ :Dict = model_class(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase__ )
model.train()
UpperCamelCase__ :Union[str, Any] = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
UpperCamelCase__ :Optional[int] = model(**lowerCamelCase__ ).loss
loss.backward()
def __a ( self :Optional[int] ):
UpperCamelCase__ , UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :Dict = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCamelCase__ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
UpperCamelCase__ :str = problem_type["""title"""]
UpperCamelCase__ :Tuple = problem_type["""num_labels"""]
UpperCamelCase__ :Dict = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
UpperCamelCase__ :List[str] = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if problem_type["num_labels"] > 1:
UpperCamelCase__ :Tuple = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
UpperCamelCase__ :str = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCamelCase__ ) as warning_list:
UpperCamelCase__ :Dict = model(**lowerCamelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __a ( self :Any ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ :Any = LevitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def A ( ) -> str:
UpperCamelCase__ :int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __a ( self :Any ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def __a ( self :Tuple ):
UpperCamelCase__ :Any = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = self.default_image_processor
UpperCamelCase__ :Union[str, Any] = prepare_img()
UpperCamelCase__ :str = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
UpperCamelCase__ :List[str] = model(**lowerCamelCase__ )
# verify the logits
UpperCamelCase__ :str = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
UpperCamelCase__ :Tuple = torch.tensor([1.0448, -0.3745, -1.8317] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) ) | 45 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=24 , _UpperCamelCase=2 , _UpperCamelCase=6 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=3 , _UpperCamelCase=None , _UpperCamelCase=1000 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def UpperCamelCase( self ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_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 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase( self ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
__A : Dict = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__A : Optional[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = False
__A : Optional[int] = False
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return True
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def UpperCamelCase( self ):
self.config_tester.run_common_tests()
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def UpperCamelCase( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
_UpperCAmelCase = torch.Size([1, 2, 768] )
_UpperCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) ) | 32 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
# TODO Update this
_lowerCAmelCase : Optional[Any] = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ ( _a ):
lowerCAmelCase__ = 'esm'
def __init__( self: str ,__lowerCAmelCase: Tuple=None ,__lowerCAmelCase: Tuple=None ,__lowerCAmelCase: str=None ,__lowerCAmelCase: Optional[int]=768 ,__lowerCAmelCase: Any=12 ,__lowerCAmelCase: str=12 ,__lowerCAmelCase: List[Any]=3_072 ,__lowerCAmelCase: int=0.1 ,__lowerCAmelCase: int=0.1 ,__lowerCAmelCase: List[Any]=1_026 ,__lowerCAmelCase: Optional[Any]=0.02 ,__lowerCAmelCase: Dict=1e-12 ,__lowerCAmelCase: Dict="absolute" ,__lowerCAmelCase: List[Any]=True ,__lowerCAmelCase: Optional[Any]=None ,__lowerCAmelCase: Union[str, Any]=False ,__lowerCAmelCase: str=False ,__lowerCAmelCase: List[Any]=None ,__lowerCAmelCase: Union[str, Any]=None ,**__lowerCAmelCase: int ,):
'''simple docstring'''
super().__init__(pad_token_id=__lowerCAmelCase ,mask_token_id=__lowerCAmelCase ,**__lowerCAmelCase )
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Union[str, Any] = hidden_size
_lowerCamelCase : Optional[Any] = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : int = intermediate_size
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : Any = attention_probs_dropout_prob
_lowerCamelCase : int = max_position_embeddings
_lowerCamelCase : int = initializer_range
_lowerCamelCase : Union[str, Any] = layer_norm_eps
_lowerCamelCase : Optional[int] = position_embedding_type
_lowerCamelCase : str = use_cache
_lowerCamelCase : Union[str, Any] = emb_layer_norm_before
_lowerCamelCase : Tuple = token_dropout
_lowerCamelCase : Dict = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
_lowerCamelCase : Dict = EsmFoldConfig()
elif isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : List[Any] = EsmFoldConfig(**__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
_lowerCamelCase : List[str] = get_default_vocab_list()
else:
_lowerCamelCase : Optional[Any] = vocab_list
else:
_lowerCamelCase : List[str] = None
_lowerCamelCase : Dict = None
if self.esmfold_config is not None and getattr(self.esmfold_config ,"use_esm_attn_map" ,__lowerCAmelCase ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : List[Any] = super().to_dict()
if isinstance(self.esmfold_config ,__lowerCAmelCase ):
_lowerCamelCase : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
lowerCAmelCase__ = None
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = 0
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = 1_2_8
lowerCAmelCase__ = None
def _lowercase ( self: Dict ):
'''simple docstring'''
if self.trunk is None:
_lowerCamelCase : Optional[int] = TrunkConfig()
elif isinstance(self.trunk ,__lowerCAmelCase ):
_lowerCamelCase : Union[str, Any] = TrunkConfig(**self.trunk )
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = asdict(self )
_lowerCamelCase : str = self.trunk.to_dict()
return output
@dataclass
class A_ :
lowerCAmelCase__ = 4_8
lowerCAmelCase__ = 1_0_2_4
lowerCAmelCase__ = 1_2_8
lowerCAmelCase__ = 3_2
lowerCAmelCase__ = 3_2
lowerCAmelCase__ = 3_2
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = False
lowerCAmelCase__ = 4
lowerCAmelCase__ = 1_2_8
lowerCAmelCase__ = None
def _lowercase ( self: Any ):
'''simple docstring'''
if self.structure_module is None:
_lowerCamelCase : Tuple = StructureModuleConfig()
elif isinstance(self.structure_module ,__lowerCAmelCase ):
_lowerCamelCase : str = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
_lowerCamelCase : Optional[Any] = self.sequence_state_dim // self.sequence_head_width
_lowerCamelCase : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def _lowercase ( self: List[str] ):
'''simple docstring'''
_lowerCamelCase : Dict = asdict(self )
_lowerCamelCase : Optional[int] = self.structure_module.to_dict()
return output
@dataclass
class A_ :
lowerCAmelCase__ = 3_8_4
lowerCAmelCase__ = 1_2_8
lowerCAmelCase__ = 1_6
lowerCAmelCase__ = 1_2_8
lowerCAmelCase__ = 1_2
lowerCAmelCase__ = 4
lowerCAmelCase__ = 8
lowerCAmelCase__ = 0.1
lowerCAmelCase__ = 8
lowerCAmelCase__ = 1
lowerCAmelCase__ = 2
lowerCAmelCase__ = 7
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 1E-8
lowerCAmelCase__ = 1E5
def _lowercase ( self: Any ):
'''simple docstring'''
return asdict(self )
def lowerCamelCase_( ) -> int:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
) | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Tuple = """rwkv"""
__A : Any = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _UpperCamelCase=50277 , _UpperCamelCase=1024 , _UpperCamelCase=4096 , _UpperCamelCase=32 , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , _UpperCamelCase=0 , _UpperCamelCase=6 , _UpperCamelCase=False , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class _UpperCamelCase( __lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''owlvit_text_model'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple=4_9_4_0_8 , SCREAMING_SNAKE_CASE__ : Optional[int]=5_1_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=2_0_4_8 , SCREAMING_SNAKE_CASE__ : int=1_2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=8 , SCREAMING_SNAKE_CASE__ : int=1_6 , SCREAMING_SNAKE_CASE__ : Dict="quick_gelu" , SCREAMING_SNAKE_CASE__ : Any=1e-5 , SCREAMING_SNAKE_CASE__ : str=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=0.02 , SCREAMING_SNAKE_CASE__ : List[Any]=1.0 , SCREAMING_SNAKE_CASE__ : List[Any]=0 , SCREAMING_SNAKE_CASE__ : List[Any]=4_9_4_0_6 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=4_9_4_0_7 , **SCREAMING_SNAKE_CASE__ : Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__a : List[Any] = vocab_size
__a : Dict = hidden_size
__a : Optional[Any] = intermediate_size
__a : Any = num_hidden_layers
__a : List[Any] = num_attention_heads
__a : Optional[Any] = max_position_embeddings
__a : Dict = hidden_act
__a : str = layer_norm_eps
__a : List[str] = attention_dropout
__a : Optional[int] = initializer_range
__a : List[Any] = initializer_factor
@classmethod
def __lowerCAmelCase ( cls : int , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : Any ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )
__a , __a : Dict = cls.get_config_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
__a : Dict = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class _UpperCamelCase( __lowerCamelCase ):
__SCREAMING_SNAKE_CASE : str = '''owlvit_vision_model'''
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any]=7_6_8 , SCREAMING_SNAKE_CASE__ : Dict=3_0_7_2 , SCREAMING_SNAKE_CASE__ : Dict=1_2 , SCREAMING_SNAKE_CASE__ : List[str]=1_2 , SCREAMING_SNAKE_CASE__ : Dict=3 , SCREAMING_SNAKE_CASE__ : List[Any]=7_6_8 , SCREAMING_SNAKE_CASE__ : int=3_2 , SCREAMING_SNAKE_CASE__ : Tuple="quick_gelu" , SCREAMING_SNAKE_CASE__ : List[Any]=1e-5 , SCREAMING_SNAKE_CASE__ : List[str]=0.0 , SCREAMING_SNAKE_CASE__ : List[str]=0.02 , SCREAMING_SNAKE_CASE__ : Tuple=1.0 , **SCREAMING_SNAKE_CASE__ : Tuple , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
__a : str = hidden_size
__a : int = intermediate_size
__a : Dict = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = num_channels
__a : Tuple = image_size
__a : str = patch_size
__a : List[str] = hidden_act
__a : Dict = layer_norm_eps
__a : Union[str, Any] = attention_dropout
__a : int = initializer_range
__a : List[str] = initializer_factor
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : Dict ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )
__a , __a : int = cls.get_config_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
__a : List[str] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class _UpperCamelCase( __lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Any = '''owlvit'''
__SCREAMING_SNAKE_CASE : str = True
def __init__( self : str , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Any=5_1_2 , SCREAMING_SNAKE_CASE__ : Optional[int]=2.6_592 , SCREAMING_SNAKE_CASE__ : Dict=True , **SCREAMING_SNAKE_CASE__ : str , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
if text_config is None:
__a : int = {}
logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.' )
if vision_config is None:
__a : Optional[int] = {}
logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.' )
__a : int = OwlViTTextConfig(**SCREAMING_SNAKE_CASE__ )
__a : str = OwlViTVisionConfig(**SCREAMING_SNAKE_CASE__ )
__a : str = projection_dim
__a : Union[str, Any] = logit_scale_init_value
__a : Union[str, Any] = return_dict
__a : int = 1.0
@classmethod
def __lowerCAmelCase ( cls : Any , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : Tuple ):
'''simple docstring'''
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )
__a , __a : int = cls.get_config_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@classmethod
def __lowerCAmelCase ( cls : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , **SCREAMING_SNAKE_CASE__ : Optional[int] ):
'''simple docstring'''
__a : Union[str, Any] = {}
__a : List[str] = text_config
__a : Tuple = vision_config
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
__a : List[Any] = copy.deepcopy(self.__dict__ )
__a : Optional[Any] = self.text_config.to_dict()
__a : Dict = self.vision_config.to_dict()
__a : Dict = self.__class__.model_type
return output
class _UpperCamelCase( __lowerCamelCase ):
@property
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
] )
@property
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return OrderedDict(
[
('logits_per_image', {0: 'batch'}),
('logits_per_text', {0: 'batch'}),
('text_embeds', {0: 'batch'}),
('image_embeds', {0: 'batch'}),
] )
@property
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
return 1e-4
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : "ProcessorMixin" , SCREAMING_SNAKE_CASE__ : int = -1 , SCREAMING_SNAKE_CASE__ : int = -1 , SCREAMING_SNAKE_CASE__ : Optional["TensorType"] = None , ):
'''simple docstring'''
__a : Tuple = super().generate_dummy_inputs(
processor.tokenizer , batch_size=SCREAMING_SNAKE_CASE__ , seq_length=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ )
__a : Dict = super().generate_dummy_inputs(
processor.image_processor , batch_size=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ )
return {**text_input_dict, **image_input_dict}
@property
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return 1_4
| 47 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
] , )
def A ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json" , "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
lowerCAmelCase__ = DatasetInfosDict.from_directory(UpperCamelCase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"dataset_info" , [
DatasetInfo(),
DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=42 , ),
] , )
def A ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : DatasetInfo ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = str(UpperCamelCase_ )
dataset_info.write_to_directory(UpperCamelCase_ )
lowerCAmelCase__ = DatasetInfo.from_directory(UpperCamelCase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCamelCase_ , "dataset_info.json" ) )
def A ( ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = DatasetInfo(
description="foo" , citation="bar" , homepage="https://foo.bar" , license="CC0" , features=Features({"a": Value("int32" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train", "num_examples": 42}] , download_checksums={} , download_size=13_37 , post_processing_size=4_42 , dataset_size=12_34 , size_in_bytes=13_37 + 4_42 + 12_34 , )
lowerCAmelCase__ = dataset_info._to_yaml_dict()
assert sorted(UpperCamelCase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
lowerCAmelCase__ = yaml.safe_dump(UpperCamelCase_ )
lowerCAmelCase__ = yaml.safe_load(UpperCamelCase_ )
assert dataset_info_yaml_dict == reloaded
def A ( ) -> str:
'''simple docstring'''
lowerCAmelCase__ = DatasetInfo()
lowerCAmelCase__ = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict" , [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=13_37 ),
} ),
] , )
def A ( UpperCamelCase_ : Dict , UpperCamelCase_ : DatasetInfosDict ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = str(UpperCamelCase_ )
dataset_infos_dict.write_to_directory(UpperCamelCase_ )
lowerCAmelCase__ = DatasetInfosDict.from_directory(UpperCamelCase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
lowerCAmelCase__ = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
lowerCAmelCase__ = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCamelCase_ , "README.md" ) )
| 48 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Dict = """falcon"""
__A : Any = ["""past_key_values"""]
def __init__( self , _UpperCamelCase=65024 , _UpperCamelCase=4544 , _UpperCamelCase=32 , _UpperCamelCase=71 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=11 , _UpperCamelCase=11 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop('''n_embed''' , _UpperCamelCase )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCAmelCase = alibi
_UpperCAmelCase = new_decoder_architecture
_UpperCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_UpperCAmelCase = parallel_attn
_UpperCAmelCase = bias
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
return self.hidden_size // self.num_attention_heads
@property
def UpperCamelCase( self ):
return not self.alibi | 32 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase__ ( snake_case_ :Dict ):
__UpperCAmelCase = {}
__UpperCAmelCase = job['''started_at''']
__UpperCAmelCase = job['''completed_at''']
__UpperCAmelCase = date_parser.parse(snake_case_ )
__UpperCAmelCase = date_parser.parse(snake_case_ )
__UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__UpperCAmelCase = start
__UpperCAmelCase = end
__UpperCAmelCase = duration_in_min
return job_info
def lowercase__ ( snake_case_ :Tuple , snake_case_ :List[Any]=None ):
__UpperCAmelCase = None
if token is not None:
__UpperCAmelCase = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}
__UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__UpperCAmelCase = requests.get(snake_case_ , headers=snake_case_ ).json()
__UpperCAmelCase = {}
try:
job_time.update({job['''name''']: extract_time_from_single_job(snake_case_ ) for job in result['''jobs''']} )
__UpperCAmelCase = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(snake_case_ ):
__UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=snake_case_ ).json()
job_time.update({job['''name''']: extract_time_from_single_job(snake_case_ ) for job in result['''jobs''']} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
_lowercase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
_lowercase : Tuple = parser.parse_args()
_lowercase : List[str] = get_job_time(args.workflow_run_id)
_lowercase : List[Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f"""{k}: {v["duration"]}""")
| 49 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def A__ ( __lowerCAmelCase : Any ):
if isinstance(__lowerCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class UpperCamelCase__ :
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
pass
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase ,_lowerCAmelCase ,F'''Difference between torch and flax is {diff} (>= {tol}).''' )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], config.projection_dim) )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
lowerCamelCase__ = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
lowerCamelCase__ = after_output[0]
lowerCamelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase ,1E-3 )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(
input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,output_attentions=_lowerCAmelCase )
lowerCamelCase__ = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) ,vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase__ = to_atuple(vision_model.config.image_size )
lowerCamelCase__ = to_atuple(vision_model.config.patch_size )
lowerCamelCase__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCamelCase__ = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
lowerCamelCase__ = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
pt_model.to(_lowerCAmelCase )
pt_model.eval()
# prepare inputs
lowerCamelCase__ = inputs_dict
lowerCamelCase__ = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowerCamelCase__ = pt_model(**_lowerCAmelCase ).to_tuple()
lowerCamelCase__ = fx_model(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] ,pt_outputs[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_pt=_lowerCAmelCase )
lowerCamelCase__ = fx_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] ,pt_outputs[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_flax=_lowerCAmelCase )
pt_model_loaded.to(_lowerCAmelCase )
pt_model_loaded.eval()
with torch.no_grad():
lowerCamelCase__ = pt_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] ,pt_outputs_loaded[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output_loaded.numpy() ,4E-2 )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,_lowerCAmelCase )
lowerCamelCase__ = fx_state
self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = load_flax_weights_in_pytorch_model(_lowerCAmelCase ,fx_model.params )
self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@is_pt_flax_cross_test
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
lowerCamelCase__ = config_inputs_dict.pop("""vision_config""" )
lowerCamelCase__ = config_inputs_dict.pop("""text_config""" )
lowerCamelCase__ = config_inputs_dict
self.check_equivalence_pt_to_flax(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
self.check_equivalence_flax_to_pt(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.get_pretrained_model_and_inputs()
lowerCamelCase__ = model_a(**_lowerCAmelCase )
lowerCamelCase__ = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model_a(**_lowerCAmelCase )
lowerCamelCase__ = after_outputs[0]
lowerCamelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase ,1E-5 )
@require_flax
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
lowerCamelCase__ = 13
lowerCamelCase__ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ = ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size )
lowerCamelCase__ = random_attention_mask([batch_size, 4] )
lowerCamelCase__ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxViTModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxBertModel(_lowerCAmelCase )
return vision_model, text_model
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxViTModelTester(self )
lowerCamelCase__ = FlaxBertModelTester(self )
lowerCamelCase__ = vit_model_tester.prepare_config_and_inputs()
lowerCamelCase__ = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
lowerCamelCase__ = 13
lowerCamelCase__ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ = ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size )
lowerCamelCase__ = random_attention_mask([batch_size, 4] )
lowerCamelCase__ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxCLIPVisionModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxBertModel(_lowerCAmelCase )
return vision_model, text_model
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxCLIPVisionModelTester(self )
lowerCamelCase__ = FlaxBertModelTester(self )
lowerCamelCase__ = clip_model_tester.prepare_config_and_inputs()
lowerCamelCase__ = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" ,logit_scale_init_value=1.0 )
lowerCamelCase__ = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] ,images=_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,)
lowerCamelCase__ = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image ,_lowerCAmelCase ,atol=1E-3 ) )
| 50 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 0:
return False
_UpperCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> dict:
"""simple docstring"""
UpperCAmelCase = f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(SCREAMING_SNAKE_CASE_ ).json()
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 10 ) -> list[dict]:
"""simple docstring"""
UpperCAmelCase = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
UpperCAmelCase = requests.get(SCREAMING_SNAKE_CASE_ ).json()[:max_stories]
return [get_hackernews_story(SCREAMING_SNAKE_CASE_ ) for story_id in story_ids]
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 10 ) -> str:
"""simple docstring"""
UpperCAmelCase = hackernews_top_stories(SCREAMING_SNAKE_CASE_ )
return "\n".join('''* [{title}]({url})'''.format(**SCREAMING_SNAKE_CASE_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 51 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__A : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__A : ClassVar[Features] = Features({} )
__A : str = "text"
@property
def UpperCamelCase( self ):
return {self.text_column: "text"} | 32 | 0 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
A = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
A = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
'''simple docstring'''
def _lowerCamelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 , _UpperCAmelCase = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_UpperCAmelCase , hypotheses=_UpperCAmelCase , min_len=_UpperCAmelCase , max_len=_UpperCAmelCase )
} | 52 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"vocab_file": "spiece.model"}
UpperCAmelCase_ = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
UpperCAmelCase_ = "▁"
class __UpperCamelCase ( A__ ):
__A : Any = VOCAB_FILES_NAMES
__A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , _UpperCamelCase , _UpperCamelCase="</s>" , _UpperCamelCase="<unk>" , _UpperCamelCase="<pad>" , _UpperCamelCase=100 , _UpperCamelCase=None , _UpperCamelCase = None , _UpperCamelCase=True , **_UpperCamelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase = len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) ) , _UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
_UpperCAmelCase = legacy
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , extra_ids=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = extra_ids
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@staticmethod
def UpperCamelCase( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_UpperCAmelCase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCamelCase , )
return max_model_length
@property
def UpperCamelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCamelCase( self ):
_UpperCAmelCase = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_UpperCamelCase )) + [1]
return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def UpperCamelCase( self ):
return list(
set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase( self ):
return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCamelCase( self , _UpperCamelCase ):
if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _UpperCamelCase ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_UpperCAmelCase = SPIECE_UNDERLINE + text.replace(_UpperCamelCase , ''' ''' )
return super().tokenize(_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
if not self.legacy:
_UpperCAmelCase = text.startswith(_UpperCamelCase )
if is_first:
_UpperCAmelCase = text[1:]
_UpperCAmelCase = self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
_UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCamelCase( self , _UpperCamelCase ):
if token.startswith('''<extra_id_''' ):
_UpperCAmelCase = re.match(R'''<extra_id_(\d+)>''' , _UpperCamelCase )
_UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase ):
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(_UpperCamelCase )
else:
_UpperCAmelCase = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(_UpperCamelCase )
_UpperCAmelCase = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,) | 32 | 0 |
from __future__ import annotations
import requests
_snake_case : Any = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : int = 1, lowerCAmelCase_ : str = "new", lowerCAmelCase_ : list | None = None ):
__lowerCAmelCase = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(lowerCAmelCase_ ) - valid_terms ) ):
__lowerCAmelCase = F"""Invalid search term: {invalid_search_terms}"""
raise ValueError(lowerCAmelCase_ )
__lowerCAmelCase = requests.get(
F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""", headers={'User-agent': 'A random string'}, )
if response.status_code == 429:
raise requests.HTTPError
__lowerCAmelCase = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(lowerCAmelCase_ )}
__lowerCAmelCase = {}
for id_ in range(lowerCAmelCase_ ):
__lowerCAmelCase = {
item: data['data']['children'][id_]['data'][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))
| 53 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
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 A ( __lowercase , unittest.TestCase ):
_snake_case =KandinskyVaaImgaImgPipeline
_snake_case =['''image_embeds''', '''negative_image_embeds''', '''image''']
_snake_case =[
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_snake_case =[
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_snake_case =False
@property
def lowerCAmelCase__ ( self: List[Any] ) -> Dict:
'''simple docstring'''
return 32
@property
def lowerCAmelCase__ ( self: Any ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCAmelCase__ ( self: Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCAmelCase__ ( self: List[str] ) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCAmelCase__ ( self: int ) -> str:
'''simple docstring'''
return 100
@property
def lowerCAmelCase__ ( self: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase_ ={
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
UpperCAmelCase_ =UNetaDConditionModel(**_lowerCAmelCase )
return model
@property
def lowerCAmelCase__ ( self: Any ) -> Tuple:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase_ =VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase__ ( self: Dict ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ =self.dummy_unet
UpperCAmelCase_ =self.dummy_movq
UpperCAmelCase_ ={
"num_train_timesteps": 1000,
"beta_schedule": "linear",
"beta_start": 0.0_00_85,
"beta_end": 0.0_12,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
UpperCAmelCase_ =DDIMScheduler(**_lowerCAmelCase )
UpperCAmelCase_ ={
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def lowerCAmelCase__ ( self: int , _lowerCAmelCase: Any , _lowerCAmelCase: Optional[Any]=0 ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
UpperCAmelCase_ =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowerCAmelCase )
# create init_image
UpperCAmelCase_ =floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
UpperCAmelCase_ =image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_ =Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert("RGB" ).resize((256, 256) )
if str(_lowerCAmelCase ).startswith("mps" ):
UpperCAmelCase_ =torch.manual_seed(_lowerCAmelCase )
else:
UpperCAmelCase_ =torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
UpperCAmelCase_ ={
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def lowerCAmelCase__ ( self: int ) -> int:
'''simple docstring'''
UpperCAmelCase_ ="cpu"
UpperCAmelCase_ =self.get_dummy_components()
UpperCAmelCase_ =self.pipeline_class(**_lowerCAmelCase )
UpperCAmelCase_ =pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
UpperCAmelCase_ =pipe(**self.get_dummy_inputs(_lowerCAmelCase ) )
UpperCAmelCase_ =output.images
UpperCAmelCase_ =pipe(
**self.get_dummy_inputs(_lowerCAmelCase ) , return_dict=_lowerCAmelCase , )[0]
UpperCAmelCase_ =image[0, -3:, -3:, -1]
UpperCAmelCase_ =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_ =np.array(
[0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63] )
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 A ( unittest.TestCase ):
def lowerCAmelCase__ ( self: List[Any] ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self: int ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ =load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
UpperCAmelCase_ =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
UpperCAmelCase_ ="A red cartoon frog, 4k"
UpperCAmelCase_ =KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_lowerCAmelCase )
UpperCAmelCase_ =KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
UpperCAmelCase_ =pipeline.to(_lowerCAmelCase )
pipeline.set_progress_bar_config(disable=_lowerCAmelCase )
UpperCAmelCase_ =torch.Generator(device="cpu" ).manual_seed(0 )
UpperCAmelCase_ , UpperCAmelCase_ =pipe_prior(
_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
UpperCAmelCase_ =pipeline(
image=_lowerCAmelCase , image_embeds=_lowerCAmelCase , negative_image_embeds=_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="np" , )
UpperCAmelCase_ =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCAmelCase , _lowerCAmelCase )
| 54 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_torch
def UpperCamelCase_ ( self : Optional[int] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__A = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
__A = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
__A = "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
__A = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task="fill-mask" ,model=A )
# baseline - just load from_pretrained with normal network
__A = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
__A = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__A = "1"
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Union[str, Any] ):
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__A = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
__A = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
__A = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
__A = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task="fill-mask" ,model=A )
# baseline - just load from_pretrained with normal network
__A = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
__A = self.get_env()
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Optional[int] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__A = "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n "
__A = "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n "
__A = "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
# baseline - just load from_pretrained with normal network
__A = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
__A = self.get_env()
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
# next emulate no network
__A = [sys.executable, "-c", "\n".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__A = "1"
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : List[str] ):
__A = "\nfrom transformers import pipeline\n "
__A = "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n "
__A = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
__A = self.get_env()
__A = "1"
__A = [sys.executable, "-c", "\n".join([load, mock, run] )]
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,1 ,result.stderr )
self.assertIn(
"You cannot infer task automatically within `pipeline` when using offline mode" ,result.stderr.decode().replace("\n" ,"" ) ,)
@require_torch
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = "\nfrom transformers import AutoModel\n "
__A = "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n "
# baseline - just load from_pretrained with normal network
__A = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
__A = self.get_env()
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__A = "1"
__A = subprocess.run(A ,env=A ,check=A ,capture_output=A )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
| 55 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _lowercase :
pass
| 56 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( A__ , unittest.TestCase ):
__A : Any = DanceDiffusionPipeline
__A : Any = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__A : Tuple = PipelineTesterMixin.required_optional_params - {
"""callback""",
"""latents""",
"""callback_steps""",
"""output_type""",
"""num_images_per_prompt""",
}
__A : Tuple = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__A : List[str] = False
__A : str = False
def UpperCamelCase( self ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_UpperCamelCase , use_timestep_embedding=_UpperCamelCase , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
_UpperCAmelCase = IPNDMScheduler()
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=0 ):
if str(_UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(_UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def UpperCamelCase( self ):
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = DanceDiffusionPipeline(**_UpperCamelCase )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(_UpperCamelCase )
_UpperCAmelCase = pipe(**_UpperCamelCase )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_local()
@skip_mps
def UpperCamelCase( self ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase( self ):
return super().test_attention_slicing_forward_pass()
def UpperCamelCase( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2 | 32 | 0 |
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Any = name
UpperCamelCase_: Any = value
UpperCamelCase_: Optional[Any] = weight
def __repr__( self ):
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ):
return self.value
def _a ( self ):
return self.name
def _a ( self ):
return self.weight
def _a ( self ):
return self.value / self.weight
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[int]:
UpperCamelCase_: Any = []
for i in range(len(UpperCAmelCase__ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
UpperCamelCase_: Optional[int] = sorted(UpperCAmelCase__ , key=UpperCAmelCase__ , reverse=UpperCAmelCase__ )
UpperCamelCase_: Dict = []
UpperCamelCase_ ,UpperCamelCase_: Any = 0.0, 0.0
for i in range(len(UpperCAmelCase__ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def snake_case () -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod() | 57 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
'''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
'''junnyu/roformer_chinese_char_small''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
),
'''junnyu/roformer_chinese_char_base''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
),
'''junnyu/roformer_small_discriminator''': (
'''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
),
'''junnyu/roformer_small_generator''': (
'''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''roformer'''
def __init__( self , _lowercase=5_0_0_0_0 , _lowercase=None , _lowercase=7_6_8 , _lowercase=1_2 , _lowercase=1_2 , _lowercase=3_0_7_2 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1_5_3_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=1E-12 , _lowercase=0 , _lowercase=False , _lowercase=True , **_lowercase , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=_lowercase , **_lowercase )
snake_case_ : str = vocab_size
snake_case_ : Any = hidden_size if embedding_size is None else embedding_size
snake_case_ : List[str] = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : str = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = type_vocab_size
snake_case_ : Tuple = initializer_range
snake_case_ : str = layer_norm_eps
snake_case_ : List[str] = rotary_value
snake_case_ : str = use_cache
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case_ : int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case_ : Any = {0: """batch""", 1: """sequence"""}
snake_case_ : Optional[int] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 58 |
import baseaa
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A__ ( SCREAMING_SNAKE_CASE_ : bytes ) -> str:
"""simple docstring"""
return baseaa.baadecode(SCREAMING_SNAKE_CASE_ ).decode('''utf-8''' )
if __name__ == "__main__":
UpperCAmelCase_ = "Hello World!"
UpperCAmelCase_ = baseaa_encode(test)
print(encoded)
UpperCAmelCase_ = baseaa_decode(encoded)
print(decoded) | 32 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "convbert"
def __init__(self : List[str] , UpperCAmelCase_ : Any=30_522 , UpperCAmelCase_ : Optional[int]=768 , UpperCAmelCase_ : Optional[int]=12 , UpperCAmelCase_ : Optional[Any]=12 , UpperCAmelCase_ : List[str]=3_072 , UpperCAmelCase_ : Dict="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : List[str]=512 , UpperCAmelCase_ : List[str]=2 , UpperCAmelCase_ : Tuple=0.02 , UpperCAmelCase_ : List[str]=1E-1_2 , UpperCAmelCase_ : Optional[int]=1 , UpperCAmelCase_ : Union[str, Any]=0 , UpperCAmelCase_ : Tuple=2 , UpperCAmelCase_ : Optional[int]=768 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : Optional[int]=9 , UpperCAmelCase_ : List[str]=1 , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[int] , ) ->List[Any]:
'''simple docstring'''
super().__init__(
pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCamelCase__: Any =vocab_size
lowerCamelCase__: Optional[Any] =hidden_size
lowerCamelCase__: List[Any] =num_hidden_layers
lowerCamelCase__: List[str] =num_attention_heads
lowerCamelCase__: Dict =intermediate_size
lowerCamelCase__: List[Any] =hidden_act
lowerCamelCase__: List[str] =hidden_dropout_prob
lowerCamelCase__: List[Any] =attention_probs_dropout_prob
lowerCamelCase__: Union[str, Any] =max_position_embeddings
lowerCamelCase__: str =type_vocab_size
lowerCamelCase__: str =initializer_range
lowerCamelCase__: Optional[Any] =layer_norm_eps
lowerCamelCase__: Optional[int] =embedding_size
lowerCamelCase__: List[str] =head_ratio
lowerCamelCase__: Optional[Any] =conv_kernel_size
lowerCamelCase__: int =num_groups
lowerCamelCase__: List[str] =classifier_dropout
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCamelCase__: Any ={0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase__: str ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
])
| 59 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
__A : int = ["""pixel_values"""]
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1 / 255 , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
super().__init__(**_UpperCamelCase )
_UpperCAmelCase = size if size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name='''crop_size''' )
_UpperCAmelCase = do_resize
_UpperCAmelCase = do_rescale
_UpperCAmelCase = do_normalize
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = size
_UpperCAmelCase = resample
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if "shortest_edge" in size:
_UpperCAmelCase = get_resize_output_image_size(_UpperCamelCase , size=size['''shortest_edge'''] , default_to_square=_UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = 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, width). Got {size.keys()}''' )
return center_crop(_UpperCamelCase , size=(size['''height'''], size['''width''']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( 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 , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = get_size_dict(_UpperCamelCase , param_name='''crop_size''' , default_to_square=_UpperCamelCase )
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if not is_batched(_UpperCamelCase ):
_UpperCAmelCase = [images]
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.''' )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
_UpperCAmelCase = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
_UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase ) | 32 | 0 |
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : Optional[int] = [0 for i in range(len(_UpperCamelCase ) )]
# initialize interval's left pointer and right pointer
snake_case_ , snake_case_ : Tuple = 0, 0
for i in range(1 , len(_UpperCamelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
snake_case_ : Any = min(right_pointer - i + 1 , z_result[i - left_pointer] )
snake_case_ : Tuple = min_edge
while go_next(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
snake_case_ , snake_case_ : int = i, i + z_result[i] - 1
return z_result
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(_UpperCamelCase ) and s[z_result[i]] == s[i + z_result[i]]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : Tuple = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
snake_case_ : Union[str, Any] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_UpperCamelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] ) | 32 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict=7 , SCREAMING_SNAKE_CASE__ : Tuple=3 , SCREAMING_SNAKE_CASE__ : Optional[int]=18 , SCREAMING_SNAKE_CASE__ : List[str]=30 , SCREAMING_SNAKE_CASE__ : Dict=400 , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Tuple=None , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : str=None , SCREAMING_SNAKE_CASE__ : str=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , ) -> int:
lowerCAmelCase__ = size if size is not None else {"height": 224, "width": 224}
lowerCAmelCase__ = crop_size if crop_size is not None else {"height": 18, "width": 18}
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = do_center_crop
lowerCAmelCase__ = crop_size
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean
lowerCAmelCase__ = image_std
lowerCAmelCase__ = do_convert_rgb
def a ( self : Union[str, Any] ) -> Union[str, Any]:
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_convert_rgb": self.do_convert_rgb,
}
def a ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=False , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : Optional[Any]=False ) -> Optional[Any]:
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
lowerCAmelCase__ = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
lowerCAmelCase__ = []
for i in range(self.batch_size ):
lowerCAmelCase__ , lowerCAmelCase__ = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
lowerCAmelCase__ = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
lowerCAmelCase__ = [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __lowerCamelCase ( UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case__ = ChineseCLIPImageProcessor if is_vision_available() else None
def a ( self : Optional[Any] ) -> List[str]:
lowerCAmelCase__ = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE__ )
@property
def a ( self : int ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def a ( self : Tuple ) -> int:
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_resize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "size" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_normalize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_mean" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_std" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_convert_rgb" ) )
def a ( self : Dict ) -> int:
lowerCAmelCase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 224, "width": 224} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
lowerCAmelCase__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def a ( self : str ) -> Optional[int]:
pass
def a ( self : Tuple ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
lowerCAmelCase__ = 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
lowerCAmelCase__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def a ( self : Union[str, Any] ) -> Dict:
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
# Test not batched input
lowerCAmelCase__ = 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
lowerCAmelCase__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def a ( self : Optional[Any] ) -> Any:
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
lowerCAmelCase__ = 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
lowerCAmelCase__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
@require_torch
@require_vision
class __lowerCamelCase ( UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case__ = ChineseCLIPImageProcessor if is_vision_available() else None
def a ( self : Optional[int] ) -> Union[str, Any]:
lowerCAmelCase__ = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = 3
@property
def a ( self : int ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def a ( self : List[str] ) -> Dict:
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_resize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "size" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_normalize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_mean" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_std" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_convert_rgb" ) )
def a ( self : Any ) -> Optional[int]:
pass
def a ( self : str ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 61 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = [0 for i in range(n + 1 )]
_UpperCAmelCase = 1
_UpperCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = 1
_UpperCAmelCase = 0
for i in range(SCREAMING_SNAKE_CASE_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
print("The following activities are selected:" )
# The first activity is always selected
SCREAMING_SNAKE_CASE : Optional[int] = 0
print(lowercase , end="," )
# Consider rest of the activities
for j in range(lowercase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(lowercase , end="," )
SCREAMING_SNAKE_CASE : List[str] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case = [1, 3, 0, 5, 8, 5]
snake_case = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 62 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , _UpperCamelCase , )
super().__init__(*_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict ):
__UpperCAmelCase : int = 0
while b > 0:
if b & 1:
__UpperCAmelCase : Optional[int] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 63 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict = ["""image_processor""", """tokenizer"""]
__A : List[str] = """BridgeTowerImageProcessor"""
__A : str = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = 0 , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = True , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = self.tokenizer(
text=_UpperCamelCase , add_special_tokens=_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , max_length=_UpperCamelCase , stride=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_token_type_ids=_UpperCamelCase , return_attention_mask=_UpperCamelCase , return_overflowing_tokens=_UpperCamelCase , return_special_tokens_mask=_UpperCamelCase , return_offsets_mapping=_UpperCamelCase , return_length=_UpperCamelCase , verbose=_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase , )
# add pixel_values + pixel_mask
_UpperCAmelCase = self.image_processor(
_UpperCamelCase , return_tensors=_UpperCamelCase , do_normalize=_UpperCamelCase , do_center_crop=_UpperCamelCase , **_UpperCamelCase )
encoding.update(_UpperCamelCase )
return encoding
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
_UpperCAmelCase = self.tokenizer.model_input_names
_UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ : Optional[int] = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Any = ['GLPNFeatureExtractor']
lowercase_ : str = ['GLPNImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ : Union[str, Any] = [
'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
'GLPNForDepthEstimation',
'GLPNLayer',
'GLPNModel',
'GLPNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
lowercase_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 64 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 32 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = 'docs/source/en/_toctree.yml'
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = defaultdict(__UpperCamelCase )
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Tuple = []
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(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = new_doc_list
UpperCAmelCase__ : Tuple = [key for key, value in counts.items() if value > 1]
UpperCAmelCase__ : str = []
for duplicate_key in duplicates:
UpperCAmelCase__ : Tuple = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 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] )
UpperCAmelCase__ : List[str] = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowerCAmelCase ( __UpperCamelCase=False ):
'''simple docstring'''
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCAmelCase__ : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
UpperCAmelCase__ : Tuple = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCAmelCase__ : Dict = content[api_idx]["""sections"""]
# Then to the model doc
UpperCAmelCase__ : Any = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCAmelCase__ : Union[str, Any] = api_doc[scheduler_idx]["""sections"""]
UpperCAmelCase__ : Dict = clean_doc_toc(__UpperCamelCase )
UpperCAmelCase__ : Dict = False
if new_scheduler_doc != scheduler_doc:
UpperCAmelCase__ : Optional[Any] = True
if overwrite:
UpperCAmelCase__ : int = new_scheduler_doc
if diff:
if overwrite:
UpperCAmelCase__ : Any = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowerCAmelCase ( __UpperCamelCase=False ):
'''simple docstring'''
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCAmelCase__ : List[str] = yaml.safe_load(f.read() )
# Get to the API doc
UpperCAmelCase__ : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCAmelCase__ : Tuple = content[api_idx]["""sections"""]
# Then to the model doc
UpperCAmelCase__ : List[str] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCAmelCase__ : str = False
UpperCAmelCase__ : List[Any] = api_doc[pipeline_idx]["""sections"""]
UpperCAmelCase__ : str = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCAmelCase__ : Optional[Any] = pipeline_doc["""section"""]
UpperCAmelCase__ : Union[str, Any] = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCAmelCase__ : str = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCAmelCase__ : List[str] = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCAmelCase__ : Any = True
if overwrite:
UpperCAmelCase__ : List[str] = new_pipeline_docs
if diff:
if overwrite:
UpperCAmelCase__ : Optional[int] = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
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__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 65 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __UpperCamelCase ( A__ ):
__A : Any = """biogpt"""
def __init__( self , _UpperCamelCase=42384 , _UpperCamelCase=1024 , _UpperCamelCase=24 , _UpperCamelCase=16 , _UpperCamelCase=4096 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=1024 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-12 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_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 = layer_norm_eps
_UpperCAmelCase = scale_embedding
_UpperCAmelCase = use_cache
_UpperCAmelCase = layerdrop
_UpperCAmelCase = activation_dropout
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
import sys
UpperCamelCase = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
_lowercase : List[Any] = 1
for digit in s:
product *= int(SCREAMING_SNAKE_CASE )
return product
def __magic_name__ ( SCREAMING_SNAKE_CASE = N ) -> int:
_lowercase : Dict = -sys.maxsize - 1
_lowercase : Tuple = n[:13]
_lowercase : List[Any] = 13
while cur_index < len(SCREAMING_SNAKE_CASE ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
_lowercase : List[str] = substr[1:] + n[cur_index]
cur_index += 1
else:
_lowercase : str = max(SCREAMING_SNAKE_CASE , str_eval(SCREAMING_SNAKE_CASE ) )
_lowercase : Dict = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 66 |
from typing import List
from .keymap import KEYMAP, get_character
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : List[Any] ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
def A__ ( *SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : Any ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
class __UpperCamelCase ( A__ ):
def __new__( cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = super().__new__(cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if not hasattr(_UpperCamelCase , '''key_handler''' ):
setattr(_UpperCamelCase , '''key_handler''' , {} )
setattr(_UpperCamelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCAmelCase = getattr(_UpperCamelCase , '''handle_key''' , [] )
for key in handled_keys:
_UpperCAmelCase = value
return new_cls
@staticmethod
def UpperCamelCase( cls ):
_UpperCAmelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCAmelCase = ord(_UpperCamelCase )
_UpperCAmelCase = cls.key_handler.get(_UpperCamelCase )
if handler:
_UpperCAmelCase = char
return handler(cls )
else:
return None
def A__ ( cls : Union[str, Any] ) -> Any:
"""simple docstring"""
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() ) | 32 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 67 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=24 , _UpperCamelCase=2 , _UpperCamelCase=6 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=3 , _UpperCamelCase=None , _UpperCamelCase=1000 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def UpperCamelCase( self ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_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 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase( self ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
__A : Dict = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__A : Optional[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = False
__A : Optional[int] = False
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return True
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def UpperCamelCase( self ):
self.config_tester.run_common_tests()
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def UpperCamelCase( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
_UpperCAmelCase = torch.Size([1, 2, 768] )
_UpperCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) ) | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Tuple = """rwkv"""
__A : Any = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _UpperCamelCase=50277 , _UpperCamelCase=1024 , _UpperCamelCase=4096 , _UpperCamelCase=32 , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , _UpperCamelCase=0 , _UpperCamelCase=6 , _UpperCamelCase=False , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__snake_case = list(poly_a or [0] )[:]
__snake_case = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
__snake_case = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
__snake_case = len(self.polyB )
# Add 0 to make lengths equal a power of 2
__snake_case = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
__snake_case = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
__snake_case = self.__multiply()
def A ( self : Any , a_ : Optional[Any] ):
"""simple docstring"""
__snake_case = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
# Corner case
if len(a_ ) <= 1:
return dft[0]
#
__snake_case = self.c_max_length // 2
while next_ncol > 0:
__snake_case = [[] for i in range(a_ )]
__snake_case = self.root**next_ncol
# First half of next step
__snake_case = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(a_ ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
__snake_case = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(a_ ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
__snake_case = new_dft
__snake_case = next_ncol // 2
return dft[0]
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.__dft("A" )
__snake_case = self.__dft("B" )
__snake_case = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
__snake_case = 2
while next_ncol <= self.c_max_length:
__snake_case = [[] for i in range(a_ )]
__snake_case = self.root ** (next_ncol // 2)
__snake_case = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
__snake_case = new_inverse_c
next_ncol *= 2
# Unpack
__snake_case = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1j for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self : Optional[int] ):
"""simple docstring"""
__snake_case = "A = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
__snake_case = "B = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
__snake_case = "A*B = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCamelCase : Optional[Any] = numpy.array([0, 0])
lowerCamelCase : Optional[int] = numpy.array([0.5, 0.866_0254])
lowerCamelCase : Optional[Any] = numpy.array([1, 0])
lowerCamelCase : Dict = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def _SCREAMING_SNAKE_CASE ( lowercase : list[numpy.ndarray] , lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = initial_vectors
for _ in range(lowercase ):
lowerCamelCase_ = iteration_step(lowercase )
return vectors
def _SCREAMING_SNAKE_CASE ( lowercase : list[numpy.ndarray] ):
'''simple docstring'''
lowerCamelCase_ = []
for i, start_vector in enumerate(vectors[:-1] ):
lowerCamelCase_ = vectors[i + 1]
new_vectors.append(lowercase )
lowerCamelCase_ = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def _SCREAMING_SNAKE_CASE ( lowercase : numpy.ndarray , lowercase : float ):
'''simple docstring'''
lowerCamelCase_ = numpy.radians(lowercase )
lowerCamelCase_ , lowerCamelCase_ = numpy.cos(lowercase ), numpy.sin(lowercase )
lowerCamelCase_ = numpy.array(((c, -s), (s, c)) )
return numpy.dot(lowercase , lowercase )
def _SCREAMING_SNAKE_CASE ( lowercase : list[numpy.ndarray] ):
'''simple docstring'''
lowerCamelCase_ = plt.gca()
axes.set_aspect('equal' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
lowerCamelCase_ , lowerCamelCase_ = zip(*lowercase )
plt.plot(lowercase , lowercase )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase : Union[str, Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 70 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Dict = """falcon"""
__A : Any = ["""past_key_values"""]
def __init__( self , _UpperCamelCase=65024 , _UpperCamelCase=4544 , _UpperCamelCase=32 , _UpperCamelCase=71 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=11 , _UpperCamelCase=11 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop('''n_embed''' , _UpperCamelCase )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCAmelCase = alibi
_UpperCAmelCase = new_decoder_architecture
_UpperCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_UpperCAmelCase = parallel_attn
_UpperCAmelCase = bias
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
return self.hidden_size // self.num_attention_heads
@property
def UpperCamelCase( self ):
return not self.alibi | 32 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def a__ ( _SCREAMING_SNAKE_CASE : Callable[[int | float], int | float] , _SCREAMING_SNAKE_CASE : int | float , _SCREAMING_SNAKE_CASE : int | float , _SCREAMING_SNAKE_CASE : int = 1_00 , ) -> float:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = x_start
UpperCAmelCase_ : Optional[Any] = fnc(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = 0.0
for _ in range(_SCREAMING_SNAKE_CASE ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
UpperCAmelCase_ : int = (x_end - x_start) / steps + xa
UpperCAmelCase_ : str = fnc(_SCREAMING_SNAKE_CASE )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
UpperCAmelCase_ : Any = xa
UpperCAmelCase_ : List[Any] = fxa
return area
if __name__ == "__main__":
def a__ ( _SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
_lowerCamelCase = 10
while i <= 10_0000:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 10
| 71 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 0:
return False
_UpperCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : Optional[int] = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class _snake_case ( A__ ):
_lowercase : Union[str, Any] = '''wav2vec2'''
def __init__( self , a=32 , a=768 , a=12 , a=12 , a=3072 , a="gelu" , a=0.1 , a=0.1 , a=0.1 , a=0.0 , a=0.0 , a=0.1 , a=0.1 , a=0.02 , a=1E-5 , a="group" , a="gelu" , a=(512, 512, 512, 512, 512, 512, 512) , a=(5, 2, 2, 2, 2, 2, 2) , a=(10, 3, 3, 3, 3, 2, 2) , a=False , a=128 , a=16 , a=False , a=True , a=0.05 , a=10 , a=2 , a=0.0 , a=10 , a=0 , a=320 , a=2 , a=0.1 , a=100 , a=256 , a=256 , a=0.1 , a="sum" , a=False , a=False , a=256 , a=(512, 512, 512, 512, 1500) , a=(5, 3, 3, 1, 1) , a=(1, 2, 3, 1, 1) , a=512 , a=0 , a=1 , a=2 , a=False , a=3 , a=2 , a=3 , a=None , a=None , **a , ) -> Tuple:
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a)
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = feat_extract_norm
SCREAMING_SNAKE_CASE = feat_extract_activation
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = conv_bias
SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE = len(self.conv_dim)
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = feat_proj_dropout
SCREAMING_SNAKE_CASE = final_dropout
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = do_stable_layer_norm
SCREAMING_SNAKE_CASE = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE = apply_spec_augment
SCREAMING_SNAKE_CASE = mask_time_prob
SCREAMING_SNAKE_CASE = mask_time_length
SCREAMING_SNAKE_CASE = mask_time_min_masks
SCREAMING_SNAKE_CASE = mask_feature_prob
SCREAMING_SNAKE_CASE = mask_feature_length
SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE = num_codevectors_per_group
SCREAMING_SNAKE_CASE = num_codevector_groups
SCREAMING_SNAKE_CASE = contrastive_logits_temperature
SCREAMING_SNAKE_CASE = feat_quantizer_dropout
SCREAMING_SNAKE_CASE = num_negatives
SCREAMING_SNAKE_CASE = codevector_dim
SCREAMING_SNAKE_CASE = proj_codevector_dim
SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE = add_adapter
SCREAMING_SNAKE_CASE = adapter_kernel_size
SCREAMING_SNAKE_CASE = adapter_stride
SCREAMING_SNAKE_CASE = num_adapter_layers
SCREAMING_SNAKE_CASE = output_hidden_size or hidden_size
SCREAMING_SNAKE_CASE = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 73 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__A : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__A : ClassVar[Features] = Features({} )
__A : str = "text"
@property
def UpperCamelCase( self ):
return {self.text_column: "text"} | 32 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase_ = logging.get_logger(__name__)
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = set()
__SCREAMING_SNAKE_CASE : str = []
def parse_line(snake_case ):
for line in fp:
if isinstance(snake_case , snake_case ):
__SCREAMING_SNAKE_CASE : List[Any] = line.decode('''UTF-8''' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(''' ''' ):
# process a single warning and move it to `selected_warnings`.
if len(snake_case ) > 0:
__SCREAMING_SNAKE_CASE : List[Any] = '''\n'''.join(snake_case )
# Only keep the warnings specified in `targets`
if any(F''': {x}: ''' in warning for x in targets ):
selected_warnings.add(snake_case )
buffer.clear()
continue
else:
__SCREAMING_SNAKE_CASE : int = line.strip()
buffer.append(snake_case )
if from_gh:
for filename in os.listdir(snake_case ):
__SCREAMING_SNAKE_CASE : Any = os.path.join(snake_case , snake_case )
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with open(snake_case ) as fp:
parse_line(snake_case )
else:
try:
with zipfile.ZipFile(snake_case ) as z:
for filename in z.namelist():
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with z.open(snake_case ) as fp:
parse_line(snake_case )
except Exception:
logger.warning(
F'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )
return selected_warnings
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = set()
__SCREAMING_SNAKE_CASE : List[Any] = [os.path.join(snake_case , snake_case ) for p in os.listdir(snake_case ) if (p.endswith('''.zip''' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(snake_case , snake_case ) )
return selected_warnings
if __name__ == "__main__":
def a__ ( snake_case ):
"""simple docstring"""
return values.split(''',''' )
lowercase_ = 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.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
lowercase_ = parser.parse_args()
lowercase_ = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
lowercase_ = 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)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
lowercase_ = extract_warnings(args.output_dir, args.targets)
lowercase_ = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 74 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"vocab_file": "spiece.model"}
UpperCAmelCase_ = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
UpperCAmelCase_ = "▁"
class __UpperCamelCase ( A__ ):
__A : Any = VOCAB_FILES_NAMES
__A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , _UpperCamelCase , _UpperCamelCase="</s>" , _UpperCamelCase="<unk>" , _UpperCamelCase="<pad>" , _UpperCamelCase=100 , _UpperCamelCase=None , _UpperCamelCase = None , _UpperCamelCase=True , **_UpperCamelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase = len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) ) , _UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
_UpperCAmelCase = legacy
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , extra_ids=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = extra_ids
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@staticmethod
def UpperCamelCase( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_UpperCAmelCase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCamelCase , )
return max_model_length
@property
def UpperCamelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCamelCase( self ):
_UpperCAmelCase = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_UpperCamelCase )) + [1]
return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def UpperCamelCase( self ):
return list(
set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase( self ):
return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCamelCase( self , _UpperCamelCase ):
if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _UpperCamelCase ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_UpperCAmelCase = SPIECE_UNDERLINE + text.replace(_UpperCamelCase , ''' ''' )
return super().tokenize(_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
if not self.legacy:
_UpperCAmelCase = text.startswith(_UpperCamelCase )
if is_first:
_UpperCAmelCase = text[1:]
_UpperCAmelCase = self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
_UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCamelCase( self , _UpperCamelCase ):
if token.startswith('''<extra_id_''' ):
_UpperCAmelCase = re.match(R'''<extra_id_(\d+)>''' , _UpperCamelCase )
_UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase ):
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(_UpperCamelCase )
else:
_UpperCAmelCase = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(_UpperCamelCase )
_UpperCAmelCase = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,) | 32 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase_ ( __a ):
def __init__( self : int , _A : CLIPSegForImageSegmentation , _A : CLIPSegProcessor , _A : AutoencoderKL , _A : CLIPTextModel , _A : CLIPTokenizer , _A : UNetaDConditionModel , _A : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , _A : StableDiffusionSafetyChecker , _A : CLIPImageProcessor , ):
'''simple docstring'''
super().__init__()
if hasattr(scheduler.config , '''steps_offset''' ) and scheduler.config.steps_offset != 1:
UpperCAmelCase__ : Union[str, Any] = (
f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
'''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
''' file'''
)
deprecate('''steps_offset!=1''' , '''1.0.0''' , _A , standard_warn=_A )
UpperCAmelCase__ : Optional[int] = dict(scheduler.config )
UpperCAmelCase__ : List[str] = 1
UpperCAmelCase__ : Optional[Any] = FrozenDict(_A )
if hasattr(scheduler.config , '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase__ : List[str] = (
f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
''' Hub, it would be very nice if you could open a Pull request for the'''
''' `scheduler/scheduler_config.json` file'''
)
deprecate('''skip_prk_steps not set''' , '''1.0.0''' , _A , standard_warn=_A )
UpperCAmelCase__ : str = dict(scheduler.config )
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : Union[str, Any] = FrozenDict(_A )
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(
segmentation_model=_A , segmentation_processor=_A , vae=_A , text_encoder=_A , tokenizer=_A , unet=_A , scheduler=_A , safety_checker=_A , feature_extractor=_A , )
def lowercase_ ( self : List[str] , _A : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ : List[str] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_A )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
self.enable_attention_slicing(_A )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
UpperCAmelCase__ : List[Any] = torch.device('''cuda''' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(_A , _A )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
if self.device != torch.device('''meta''' ) or not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_A , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self : Optional[Any] , _A : Union[str, List[str]] , _A : Union[torch.FloatTensor, PIL.Image.Image] , _A : str , _A : int = 512 , _A : int = 512 , _A : int = 50 , _A : float = 7.5 , _A : Optional[Union[str, List[str]]] = None , _A : Optional[int] = 1 , _A : float = 0.0 , _A : Optional[torch.Generator] = None , _A : Optional[torch.FloatTensor] = None , _A : Optional[str] = "pil" , _A : bool = True , _A : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , _A : int = 1 , **_A : List[Any] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.segmentation_processor(
text=[text] , images=[image] , padding='''max_length''' , return_tensors='''pt''' ).to(self.device )
UpperCAmelCase__ : str = self.segmentation_model(**_A )
UpperCAmelCase__ : Any = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase__ : Any = self.numpy_to_pil(_A )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase__ : List[Any] = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=_A , image=_A , mask_image=_A , height=_A , width=_A , num_inference_steps=_A , guidance_scale=_A , negative_prompt=_A , num_images_per_prompt=_A , eta=_A , generator=_A , latents=_A , output_type=_A , return_dict=_A , callback=_A , callback_steps=_A , )
| 75 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCAmelCase_ ( snake_case ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> Any:
super().__init__()
__lowercase : Optional[Any] = value_function
__lowercase : Tuple = unet
__lowercase : Any = scheduler
__lowercase : List[str] = env
__lowercase : Dict = env.get_dataset()
__lowercase : Dict = {}
for key in self.data.keys():
try:
__lowercase : List[str] = self.data[key].mean()
except: # noqa: E722
pass
__lowercase : List[str] = {}
for key in self.data.keys():
try:
__lowercase : str = self.data[key].std()
except: # noqa: E722
pass
__lowercase : Any = env.observation_space.shape[0]
__lowercase : List[Any] = env.action_space.shape[0]
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> Any:
return (x_in - self.means[key]) / self.stds[key]
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
return x_in * self.stds[key] + self.means[key]
def _lowerCamelCase ( self , UpperCamelCase_ ) -> List[Any]:
if type(UpperCamelCase_ ) is dict:
return {k: self.to_torch(UpperCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(UpperCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(UpperCamelCase_ , device=self.unet.device )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
for key, val in cond.items():
__lowercase : Optional[int] = val.clone()
return x_in
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> str:
__lowercase : List[str] = x.shape[0]
__lowercase : int = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
__lowercase : Tuple = torch.full((batch_size,) , UpperCamelCase_ , device=self.unet.device , dtype=torch.long )
for _ in range(UpperCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
__lowercase : Optional[int] = self.value_function(x.permute(0 , 2 , 1 ) , UpperCamelCase_ ).sample
__lowercase : List[Any] = torch.autograd.grad([y.sum()] , [x] )[0]
__lowercase : Dict = self.scheduler._get_variance(UpperCamelCase_ )
__lowercase : str = torch.exp(0.5 * posterior_variance )
__lowercase : Tuple = model_std * grad
__lowercase : Dict = 0
__lowercase : Optional[int] = x.detach()
__lowercase : Optional[Any] = x + scale * grad
__lowercase : List[str] = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
__lowercase : List[Any] = self.unet(x.permute(0 , 2 , 1 ) , UpperCamelCase_ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
__lowercase : Tuple = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , predict_epsilon=UpperCamelCase_ )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
__lowercase : Any = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
__lowercase : Dict = self.to_torch(UpperCamelCase_ )
return x, y
def __call__( self , UpperCamelCase_ , UpperCamelCase_=64 , UpperCamelCase_=32 , UpperCamelCase_=2 , UpperCamelCase_=0.1 ) -> List[Any]:
# normalize the observations and create batch dimension
__lowercase : Optional[Any] = self.normalize(UpperCamelCase_ , '''observations''' )
__lowercase : Any = obs[None].repeat(UpperCamelCase_ , axis=0 )
__lowercase : Optional[int] = {0: self.to_torch(UpperCamelCase_ )}
__lowercase : Optional[int] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
__lowercase : int = randn_tensor(UpperCamelCase_ , device=self.unet.device )
__lowercase : int = self.reset_xa(UpperCamelCase_ , UpperCamelCase_ , self.action_dim )
__lowercase : List[Any] = self.to_torch(UpperCamelCase_ )
# run the diffusion process
__lowercase ,__lowercase : str = self.run_diffusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# sort output trajectories by value
__lowercase : Optional[Any] = y.argsort(0 , descending=UpperCamelCase_ ).squeeze()
__lowercase : List[Any] = x[sorted_idx]
__lowercase : Union[str, Any] = sorted_values[:, :, : self.action_dim]
__lowercase : List[str] = actions.detach().cpu().numpy()
__lowercase : Optional[Any] = self.de_normalize(UpperCamelCase_ , key='''actions''' )
# select the action with the highest value
if y is not None:
__lowercase : str = 0
else:
# if we didn't run value guiding, select a random action
__lowercase : Optional[int] = np.random.randint(0 , UpperCamelCase_ )
__lowercase : Optional[Any] = denorm_actions[selected_index, 0]
return denorm_actions
| 76 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
"""simple docstring"""
A = """Input must be a string of 8 numbers plus letter"""
A = """TRWAGMYFPDXBNJZSQVHLCKE"""
def _UpperCamelCase ( UpperCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = f"Expected string as input, found {type(UpperCamelCase ).__name__}"
raise TypeError(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = spanish_id.replace("-" , "" ).upper()
if len(UpperCamelCase ) != 9:
raise ValueError(UpperCamelCase )
try:
__UpperCAmelCase : int = int(spanish_id_clean[0:8] )
__UpperCAmelCase : Optional[Any] = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
'''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
SCREAMING_SNAKE_CASE_: Optional[Any] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[Any] ={
'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 __A ( UpperCamelCase__ ):
a__ : List[Any] = """perceiver"""
def __init__(self : Optional[int] , __a : Tuple=256 , __a : Optional[Any]=1280 , __a : Optional[int]=768 , __a : Any=1 , __a : List[str]=26 , __a : Dict=8 , __a : List[Any]=8 , __a : Tuple=None , __a : List[str]=None , __a : Optional[int]="kv" , __a : Union[str, Any]=1 , __a : List[str]=1 , __a : List[Any]="gelu" , __a : List[str]=0.1 , __a : str=0.02 , __a : List[str]=1E-12 , __a : Optional[int]=True , __a : Tuple=262 , __a : Dict=2048 , __a : int=56 , __a : Optional[int]=[368, 496] , __a : Any=16 , __a : Optional[Any]=1920 , __a : Any=16 , __a : str=[1, 16, 224, 224] , **__a : Any , ):
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 __A ( UpperCamelCase__ ):
@property
def _lowercase (self : Dict ):
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 _lowercase (self : Optional[Any] ):
return 1E-4
def _lowercase (self : Union[str, Any] , __a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __a : int = -1 , __a : int = -1 , __a : int = -1 , __a : bool = False , __a : Optional[TensorType] = None , __a : int = 3 , __a : int = 40 , __a : int = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
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." )
| 78 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( A__ , unittest.TestCase ):
__A : Any = DanceDiffusionPipeline
__A : Any = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__A : Tuple = PipelineTesterMixin.required_optional_params - {
"""callback""",
"""latents""",
"""callback_steps""",
"""output_type""",
"""num_images_per_prompt""",
}
__A : Tuple = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__A : List[str] = False
__A : str = False
def UpperCamelCase( self ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_UpperCamelCase , use_timestep_embedding=_UpperCamelCase , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
_UpperCAmelCase = IPNDMScheduler()
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=0 ):
if str(_UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(_UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def UpperCamelCase( self ):
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = DanceDiffusionPipeline(**_UpperCamelCase )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(_UpperCamelCase )
_UpperCAmelCase = pipe(**_UpperCamelCase )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_local()
@skip_mps
def UpperCamelCase( self ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase( self ):
return super().test_attention_slicing_forward_pass()
def UpperCamelCase( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2 | 32 | 0 |
SCREAMING_SNAKE_CASE__ : Dict = 6_55_21
def _lowerCamelCase ( __lowerCamelCase ) -> int:
'''simple docstring'''
UpperCAmelCase__ : str = 1
UpperCAmelCase__ : Tuple = 0
for plain_chr in plain_text:
UpperCAmelCase__ : int = (a + ord(__lowerCamelCase )) % MOD_ADLER
UpperCAmelCase__ : List[str] = (b + a) % MOD_ADLER
return (b << 16) | a
| 79 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 | 0 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase ) + 1
__lowercase = len(lowerCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__lowercase = [[0 for i in range(lowerCamelCase )] for j in range(lowerCamelCase )]
# since string of zero length match pattern of zero length
__lowercase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowerCamelCase ):
__lowercase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowerCamelCase ):
__lowercase = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowerCamelCase ):
for j in range(1 , lowerCamelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__lowercase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__lowercase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__lowercase = dp[i - 1][j]
else:
__lowercase = 0
else:
__lowercase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
__UpperCamelCase : Any = """aab"""
__UpperCamelCase : Optional[Any] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 80 |
import baseaa
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A__ ( SCREAMING_SNAKE_CASE_ : bytes ) -> str:
"""simple docstring"""
return baseaa.baadecode(SCREAMING_SNAKE_CASE_ ).decode('''utf-8''' )
if __name__ == "__main__":
UpperCAmelCase_ = "Hello World!"
UpperCAmelCase_ = baseaa_encode(test)
print(encoded)
UpperCAmelCase_ = baseaa_decode(encoded)
print(decoded) | 32 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : List[str] = (DDIMParallelScheduler,)
__UpperCAmelCase : Tuple = (("eta", 0.0), ("num_inference_steps", 50))
def __snake_case ( self : int , **lowerCamelCase : Optional[Any] ) -> Optional[Any]:
__snake_case : Any = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**lowerCamelCase )
return config
def __snake_case ( self : Optional[Any] , **lowerCamelCase : List[str] ) -> Any:
__snake_case : Optional[int] = self.scheduler_classes[0]
__snake_case : Optional[int] = self.get_scheduler_config(**lowerCamelCase )
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
__snake_case , __snake_case : List[Any] = 10, 0.0
__snake_case : Union[str, Any] = self.dummy_model()
__snake_case : Union[str, Any] = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase )
for t in scheduler.timesteps:
__snake_case : List[Any] = model(lowerCamelCase , lowerCamelCase )
__snake_case : List[str] = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ).prev_sample
return sample
def __snake_case ( self : Tuple ) -> Dict:
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase )
def __snake_case ( self : Dict ) -> int:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCamelCase )
__snake_case : Optional[int] = self.scheduler_classes[0]
__snake_case : Optional[Any] = self.get_scheduler_config(steps_offset=1 )
__snake_case : List[str] = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def __snake_case ( self : List[Any] ) -> int:
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=lowerCamelCase , beta_end=lowerCamelCase )
def __snake_case ( self : List[Any] ) -> str:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCamelCase )
def __snake_case ( self : Tuple ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase )
def __snake_case ( self : Optional[Any] ) -> List[Any]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCamelCase )
def __snake_case ( self : Tuple ) -> List[Any]:
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=lowerCamelCase )
def __snake_case ( self : Tuple ) -> Any:
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase )
def __snake_case ( self : Dict ) -> List[str]:
self.check_over_configs(thresholding=lowerCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=lowerCamelCase , prediction_type=lowerCamelCase , sample_max_value=lowerCamelCase , )
def __snake_case ( self : int ) -> Union[str, Any]:
for t in [1, 10, 49]:
self.check_over_forward(time_step=lowerCamelCase )
def __snake_case ( self : int ) -> List[Any]:
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=lowerCamelCase , num_inference_steps=lowerCamelCase )
def __snake_case ( self : Dict ) -> str:
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=lowerCamelCase , eta=lowerCamelCase )
def __snake_case ( self : Any ) -> Any:
__snake_case : Any = self.scheduler_classes[0]
__snake_case : Dict = self.get_scheduler_config()
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_47_71 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_24_60 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5
def __snake_case ( self : Dict ) -> List[Any]:
__snake_case : Any = self.scheduler_classes[0]
__snake_case : Dict = self.get_scheduler_config()
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
__snake_case , __snake_case : str = 10, 0.0
scheduler.set_timesteps(lowerCamelCase )
__snake_case : Dict = self.dummy_model()
__snake_case : List[Any] = self.dummy_sample_deter
__snake_case : int = self.dummy_sample_deter + 0.1
__snake_case : str = self.dummy_sample_deter - 0.1
__snake_case : Dict = samplea.shape[0]
__snake_case : str = torch.stack([samplea, samplea, samplea] , dim=0 )
__snake_case : int = torch.arange(lowerCamelCase )[0:3, None].repeat(1 , lowerCamelCase )
__snake_case : Union[str, Any] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
__snake_case : Any = scheduler.batch_step_no_noise(lowerCamelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , lowerCamelCase )
__snake_case : Optional[Any] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : List[Any] = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
assert abs(result_mean.item() - 0.49_82 ) < 1E-3
def __snake_case ( self : str ) -> Optional[int]:
__snake_case : Union[str, Any] = self.full_loop()
__snake_case : Dict = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3
def __snake_case ( self : str ) -> Dict:
__snake_case : Any = self.full_loop(prediction_type="v_prediction" )
__snake_case : Optional[int] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : int = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 52.53_02 ) < 1E-2
assert abs(result_mean.item() - 0.06_84 ) < 1E-3
def __snake_case ( self : Dict ) -> Tuple:
# We specify different beta, so that the first alpha is 0.99
__snake_case : str = self.full_loop(set_alpha_to_one=lowerCamelCase , beta_start=0.01 )
__snake_case : Dict = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
assert abs(result_mean.item() - 0.19_51 ) < 1E-3
def __snake_case ( self : Optional[Any] ) -> List[Any]:
# We specify different beta, so that the first alpha is 0.99
__snake_case : Union[str, Any] = self.full_loop(set_alpha_to_one=lowerCamelCase , beta_start=0.01 )
__snake_case : Optional[Any] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
assert abs(result_mean.item() - 0.19_41 ) < 1E-3
| 81 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
__A : int = ["""pixel_values"""]
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1 / 255 , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
super().__init__(**_UpperCamelCase )
_UpperCAmelCase = size if size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_UpperCAmelCase = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name='''crop_size''' )
_UpperCAmelCase = do_resize
_UpperCAmelCase = do_rescale
_UpperCAmelCase = do_normalize
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = size
_UpperCAmelCase = resample
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if "shortest_edge" in size:
_UpperCAmelCase = get_resize_output_image_size(_UpperCamelCase , size=size['''shortest_edge'''] , default_to_square=_UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = 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, width). Got {size.keys()}''' )
return center_crop(_UpperCamelCase , size=(size['''height'''], size['''width''']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( 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 , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = get_size_dict(_UpperCamelCase , param_name='''crop_size''' , default_to_square=_UpperCamelCase )
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(_UpperCamelCase )
if not is_batched(_UpperCamelCase ):
_UpperCAmelCase = [images]
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.''' )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
_UpperCAmelCase = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
_UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase ) | 32 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowerCamelCase = None
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = """▁"""
lowerCamelCase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
lowerCamelCase = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
lowerCamelCase = {
"""google/pegasus-xsum""": 512,
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = PegasusTokenizer
UpperCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[Any] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Any="<pad>" , _UpperCAmelCase : List[str]="</s>" , _UpperCAmelCase : Tuple="<unk>" , _UpperCAmelCase : Optional[Any]="<mask_2>" , _UpperCAmelCase : int="<mask_1>" , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Optional[Any]=103 , **_UpperCAmelCase : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError(
F"""additional_special_tokens should be of type {type(_UpperCAmelCase )}, but is"""
F""" {type(_UpperCAmelCase )}""" )
UpperCAmelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(_UpperCAmelCase ) , self.offset - 1 )
]
if len(set(_UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
UpperCAmelCase_ = additional_special_tokens_extended
else:
UpperCAmelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , pad_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , mask_token_sent=_UpperCAmelCase , offset=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , **_UpperCAmelCase , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = False if not self.vocab_file else True
def lowercase__ ( self : Optional[int] , _UpperCAmelCase : str ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase__ ( self : List[Any] , _UpperCAmelCase : List , _UpperCAmelCase : Optional[List] = None , _UpperCAmelCase : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return self._special_token_mask(_UpperCAmelCase )
elif token_ids_a is None:
return self._special_token_mask(_UpperCAmelCase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase__ ( self : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str]=None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase__ ( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
_UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 82 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] ) | 32 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger()
def snake_case_ ( A_ : int, A_ : str, A_ : LevitConfig, A_ : Path, A_ : bool = True ):
'''simple docstring'''
print(F'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 1_28:
if name[-1] == "S":
_lowerCamelCase : int = timm.create_model('''levit_128s''', pretrained=A_ )
else:
_lowerCamelCase : Tuple = timm.create_model('''levit_128''', pretrained=A_ )
if hidden_sizes == 1_92:
_lowerCamelCase : List[str] = timm.create_model('''levit_192''', pretrained=A_ )
if hidden_sizes == 2_56:
_lowerCamelCase : Union[str, Any] = timm.create_model('''levit_256''', pretrained=A_ )
if hidden_sizes == 3_84:
_lowerCamelCase : Union[str, Any] = timm.create_model('''levit_384''', pretrained=A_ )
from_model.eval()
_lowerCamelCase : Any = LevitForImageClassificationWithTeacher(A_ ).eval()
_lowerCamelCase : int = OrderedDict()
_lowerCamelCase : Any = from_model.state_dict()
_lowerCamelCase : List[str] = list(from_model.state_dict().keys() )
_lowerCamelCase : List[str] = list(our_model.state_dict().keys() )
print(len(A_ ), len(A_ ) )
for i in range(len(A_ ) ):
_lowerCamelCase : Union[str, Any] = weights[og_keys[i]]
our_model.load_state_dict(A_ )
_lowerCamelCase : Optional[int] = torch.randn((2, 3, 2_24, 2_24) )
_lowerCamelCase : Union[str, Any] = from_model(A_ )
_lowerCamelCase : Optional[Any] = our_model(A_ ).logits
assert torch.allclose(A_, A_ ), "The model logits don't match the original one."
_lowerCamelCase : int = name
print(A_ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
_lowerCamelCase : int = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F'''Pushed {checkpoint_name}''' )
def snake_case_ ( A_ : Path, A_ : str = None, A_ : bool = True ):
'''simple docstring'''
_lowerCamelCase : Dict = '''imagenet-1k-id2label.json'''
_lowerCamelCase : Dict = 10_00
_lowerCamelCase : Union[str, Any] = (1, num_labels)
_lowerCamelCase : Tuple = '''huggingface/label-files'''
_lowerCamelCase : Any = num_labels
_lowerCamelCase : List[Any] = json.load(open(hf_hub_download(A_, A_, repo_type='''dataset''' ), '''r''' ) )
_lowerCamelCase : List[str] = {int(A_ ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : Tuple = {v: k for k, v in idalabel.items()}
_lowerCamelCase : int = partial(A_, num_labels=A_, idalabel=A_, labelaid=A_ )
_lowerCamelCase : Optional[int] = {
'''levit-128S''': 1_28,
'''levit-128''': 1_28,
'''levit-192''': 1_92,
'''levit-256''': 2_56,
'''levit-384''': 3_84,
}
_lowerCamelCase : Any = {
'''levit-128S''': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84], num_attention_heads=[4, 6, 8], depths=[2, 3, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'''levit-128''': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84], num_attention_heads=[4, 8, 12], depths=[4, 4, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'''levit-192''': ImageNetPreTrainedConfig(
hidden_sizes=[1_92, 2_88, 3_84], num_attention_heads=[3, 5, 6], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'''levit-256''': ImageNetPreTrainedConfig(
hidden_sizes=[2_56, 3_84, 5_12], num_attention_heads=[4, 6, 8], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'''levit-384''': ImageNetPreTrainedConfig(
hidden_sizes=[3_84, 5_12, 7_68], num_attention_heads=[6, 9, 12], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0.1, ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name], A_, names_to_config[model_name], A_, A_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name], A_, A_, A_, A_ )
return config, expected_shape
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 83 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = [0 for i in range(n + 1 )]
_UpperCAmelCase = 1
_UpperCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = 1
_UpperCAmelCase = 0
for i in range(SCREAMING_SNAKE_CASE_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case="</s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case=125 , snake_case=None , **snake_case , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase = [F'''<extra_id_{i}>''' for i in range(snake_case )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase = len(set(filter(lambda snake_case : bool('extra_id' in str(snake_case ) ) , snake_case ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
' extra_ids tokens' )
lowercase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else pad_token
lowercase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else eos_token
lowercase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else unk_token
super().__init__(
eos_token=snake_case , unk_token=snake_case , pad_token=snake_case , extra_ids=snake_case , additional_special_tokens=snake_case , **snake_case , )
lowercase = extra_ids
lowercase = 2**8 # utf is 8 bits
# define special tokens dict
lowercase = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
lowercase = len(self.special_tokens_encoder )
lowercase = len(snake_case )
for i, token in enumerate(snake_case ):
lowercase = self.vocab_size + i - n
lowercase = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case , token_ids_a=snake_case , already_has_special_tokens=snake_case )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(snake_case )) + [1]
return ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if len(snake_case ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = self._add_eos_if_not_present(snake_case )
if token_ids_a is None:
return token_ids_a
else:
lowercase = self._add_eos_if_not_present(snake_case )
return token_ids_a + token_ids_a
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = [chr(snake_case ) for i in text.encode('utf-8' )]
return tokens
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if token in self.special_tokens_encoder:
lowercase = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
lowercase = self.added_tokens_encoder[token]
elif len(snake_case ) != 1:
lowercase = self.unk_token_id
else:
lowercase = ord(snake_case ) + self._num_special_tokens
return token_id
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if index in self.special_tokens_decoder:
lowercase = self.special_tokens_decoder[index]
else:
lowercase = chr(index - self._num_special_tokens )
return token
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = B''
for token in tokens:
if token in self.special_tokens_decoder:
lowercase = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.added_tokens_decoder:
lowercase = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.special_tokens_encoder:
lowercase = token.encode('utf-8' )
elif token in self.added_tokens_encoder:
lowercase = token.encode('utf-8' )
else:
lowercase = bytes([ord(snake_case )] )
bstring += tok_string
lowercase = bstring.decode('utf-8' , errors='ignore' )
return string
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
return ()
| 84 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , _UpperCamelCase , )
super().__init__(*_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def _a ( lowercase__ : Any , lowercase__ : Any="shi-labs/oneformer_demo" ):
'''simple docstring'''
with open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) as f:
SCREAMING_SNAKE_CASE__ : List[str] = json.load(lowercase__ )
SCREAMING_SNAKE_CASE__ : int = {}
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = []
for key, info in class_info.items():
SCREAMING_SNAKE_CASE__ : str = info['name']
class_names.append(info['name'] )
if info["isthing"]:
thing_ids.append(int(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : Dict = thing_ids
SCREAMING_SNAKE_CASE__ : int = class_names
return metadata
class snake_case ( unittest.TestCase ):
def __init__( self : int , a_ : Optional[int] , a_ : Any=7 , a_ : Optional[Any]=3 , a_ : Optional[int]=30 , a_ : List[str]=400 , a_ : Optional[Any]=None , a_ : List[str]=True , a_ : Union[str, Any]=True , a_ : Tuple=[0.5, 0.5, 0.5] , a_ : str=[0.5, 0.5, 0.5] , a_ : List[Any]=10 , a_ : Any=False , a_ : str=255 , a_ : List[str]="shi-labs/oneformer_demo" , a_ : Optional[int]="ade20k_panoptic.json" , a_ : Dict=10 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = parent
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : str = min_resolution
SCREAMING_SNAKE_CASE__ : int = max_resolution
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_resize
SCREAMING_SNAKE_CASE__ : Tuple = {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
SCREAMING_SNAKE_CASE__ : Any = do_normalize
SCREAMING_SNAKE_CASE__ : List[Any] = image_mean
SCREAMING_SNAKE_CASE__ : List[Any] = image_std
SCREAMING_SNAKE_CASE__ : int = class_info_file
SCREAMING_SNAKE_CASE__ : Any = prepare_metadata(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Tuple = num_text
SCREAMING_SNAKE_CASE__ : str = repo_path
# for the post_process_functions
SCREAMING_SNAKE_CASE__ : List[Any] = 2
SCREAMING_SNAKE_CASE__ : Tuple = 10
SCREAMING_SNAKE_CASE__ : Optional[int] = 10
SCREAMING_SNAKE_CASE__ : Optional[Any] = 3
SCREAMING_SNAKE_CASE__ : Tuple = 4
SCREAMING_SNAKE_CASE__ : str = num_labels
SCREAMING_SNAKE_CASE__ : List[Any] = do_reduce_labels
SCREAMING_SNAKE_CASE__ : Tuple = ignore_index
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def __lowercase( self : Optional[int] , a_ : Optional[int] , a_ : Dict=False )-> str:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ : List[str] = image_inputs[0]
if isinstance(a_ , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE__ : Tuple = int(self.size['shortest_edge'] * h / w )
SCREAMING_SNAKE_CASE__ : str = self.size['shortest_edge']
elif w > h:
SCREAMING_SNAKE_CASE__ : Any = self.size['shortest_edge']
SCREAMING_SNAKE_CASE__ : Dict = int(self.size['shortest_edge'] * w / h )
else:
SCREAMING_SNAKE_CASE__ : str = self.size['shortest_edge']
SCREAMING_SNAKE_CASE__ : str = self.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE__ : int = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max(a_ , key=lambda a_ : item[0] )[0]
SCREAMING_SNAKE_CASE__ : Tuple = max(a_ , key=lambda a_ : item[1] )[1]
return expected_height, expected_width
def __lowercase( self : Union[str, Any] )-> int:
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowercase_ = image_processing_class
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = OneFormerImageProcessorTester(self )
@property
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'image_mean' ) )
self.assertTrue(hasattr(a_ , 'image_std' ) )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_resize' ) )
self.assertTrue(hasattr(a_ , 'size' ) )
self.assertTrue(hasattr(a_ , 'ignore_index' ) )
self.assertTrue(hasattr(a_ , 'class_info_file' ) )
self.assertTrue(hasattr(a_ , 'num_text' ) )
self.assertTrue(hasattr(a_ , 'repo_path' ) )
self.assertTrue(hasattr(a_ , 'metadata' ) )
self.assertTrue(hasattr(a_ , 'do_reduce_labels' ) )
def __lowercase( self : Dict )-> List[Any]:
"""simple docstring"""
pass
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : str = prepare_image_inputs(self.image_processing_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.image_processing_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.image_processing_tester.get_expected_values(a_ , batched=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processor(
a_ , ['semantic'] * len(a_ ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase( self : int )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[str] = prepare_image_inputs(self.image_processing_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.image_processing_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processing_tester.get_expected_values(a_ , batched=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
a_ , ['semantic'] * len(a_ ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : str = prepare_image_inputs(self.image_processing_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.image_processing_tester.get_expected_values(a_ , batched=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
a_ , ['semantic'] * len(a_ ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase( self : str , a_ : Optional[int]=False , a_ : Optional[Any]=False , a_ : str="np" )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processing_tester.num_labels
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_image_inputs(self.image_processing_tester , equal_resolution=a_ )
if with_segmentation_maps:
SCREAMING_SNAKE_CASE__ : Optional[int] = num_labels
if is_instance_map:
SCREAMING_SNAKE_CASE__ : Dict = list(range(a_ ) ) * 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(enumerate(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
SCREAMING_SNAKE_CASE__ : Optional[Any] = [Image.fromarray(a_ ) for annotation in annotations]
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
a_ , ['semantic'] * len(a_ ) , a_ , return_tensors='pt' , instance_id_to_semantic_id=a_ , pad_and_return_pixel_mask=a_ , )
return inputs
def __lowercase( self : Any )-> List[str]:
"""simple docstring"""
pass
def __lowercase( self : Optional[int] )-> Optional[int]:
"""simple docstring"""
def common(a_ : Optional[Any]=False , a_ : Tuple=None ):
SCREAMING_SNAKE_CASE__ : int = self.comm_get_image_processor_inputs(
with_segmentation_maps=a_ , is_instance_map=a_ , segmentation_type=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs['mask_labels']
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['class_labels']
SCREAMING_SNAKE_CASE__ : Optional[int] = inputs['pixel_values']
SCREAMING_SNAKE_CASE__ : List[str] = inputs['text_inputs']
# check the batch_size
for mask_label, class_label, text_input in zip(a_ , a_ , a_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(a_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=a_ )
common(is_instance_map=a_ , segmentation_type='pil' )
common(is_instance_map=a_ , segmentation_type='pil' )
def __lowercase( self : Union[str, Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = np.zeros((20, 50) )
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : int = binary_mask_to_rle(a_ )
self.assertEqual(len(a_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ : int = fature_extractor.post_process_semantic_segmentation(a_ )
self.assertEqual(len(a_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
SCREAMING_SNAKE_CASE__ : List[str] = fature_extractor.post_process_semantic_segmentation(a_ , target_sizes=a_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def __lowercase( self : Optional[int] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor.post_process_instance_segmentation(a_ , threshold=0 )
self.assertTrue(len(a_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , a_ )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processor.post_process_panoptic_segmentation(a_ , threshold=0 )
self.assertTrue(len(a_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , a_ )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 85 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict = ["""image_processor""", """tokenizer"""]
__A : List[str] = """BridgeTowerImageProcessor"""
__A : str = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = 0 , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = True , _UpperCamelCase = None , **_UpperCamelCase , ):
_UpperCAmelCase = self.tokenizer(
text=_UpperCamelCase , add_special_tokens=_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , max_length=_UpperCamelCase , stride=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_token_type_ids=_UpperCamelCase , return_attention_mask=_UpperCamelCase , return_overflowing_tokens=_UpperCamelCase , return_special_tokens_mask=_UpperCamelCase , return_offsets_mapping=_UpperCamelCase , return_length=_UpperCamelCase , verbose=_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase , )
# add pixel_values + pixel_mask
_UpperCAmelCase = self.image_processor(
_UpperCamelCase , return_tensors=_UpperCamelCase , do_normalize=_UpperCamelCase , do_center_crop=_UpperCamelCase , **_UpperCamelCase )
encoding.update(_UpperCamelCase )
return encoding
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , *_UpperCamelCase , **_UpperCamelCase ):
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
_UpperCAmelCase = self.tokenizer.model_input_names
_UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 32 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
return [ord(__UpperCamelCase ) - 96 for elem in plain]
def __snake_case ( __UpperCamelCase : list[int] ):
"""simple docstring"""
return "".join(chr(elem + 96 ) for elem in encoded )
def __snake_case ( ):
"""simple docstring"""
A_ = encode(input("-> " ).strip().lower() )
print("Encoded: " ,__UpperCamelCase )
print("Decoded:" ,decode(__UpperCamelCase ) )
if __name__ == "__main__":
main() | 86 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 32 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE ( ) -> Iterator[int]:
"""simple docstring"""
A__ = 2
while True:
if is_prime(lowercase_ ):
yield num
num += 1
def SCREAMING_SNAKE_CASE ( lowercase_ = 2_000_000 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda lowercase_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 87 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __UpperCamelCase ( A__ ):
__A : Any = """biogpt"""
def __init__( self , _UpperCamelCase=42384 , _UpperCamelCase=1024 , _UpperCamelCase=24 , _UpperCamelCase=16 , _UpperCamelCase=4096 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=1024 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-12 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_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 = layer_norm_eps
_UpperCAmelCase = scale_embedding
_UpperCAmelCase = use_cache
_UpperCAmelCase = layerdrop
_UpperCAmelCase = activation_dropout
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase__ :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=99 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=37 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=None , ) -> List[Any]:
_lowerCamelCase : Union[str, Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = seq_length
_lowerCamelCase : Any = is_training
_lowerCamelCase : int = use_input_mask
_lowerCamelCase : Tuple = use_token_type_ids
_lowerCamelCase : List[str] = use_labels
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = hidden_size
_lowerCamelCase : Any = num_hidden_layers
_lowerCamelCase : Dict = num_attention_heads
_lowerCamelCase : List[Any] = intermediate_size
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : List[str] = attention_probs_dropout_prob
_lowerCamelCase : Dict = max_position_embeddings
_lowerCamelCase : str = type_vocab_size
_lowerCamelCase : Any = type_sequence_label_size
_lowerCamelCase : List[str] = initializer_range
_lowerCamelCase : Optional[Any] = num_labels
_lowerCamelCase : List[str] = num_choices
_lowerCamelCase : int = scope
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowerCamelCase : Union[str, Any] = None
if self.use_input_mask:
_lowerCamelCase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length])
_lowerCamelCase : int = None
if self.use_token_type_ids:
_lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_lowerCamelCase : Dict = None
_lowerCamelCase : Any = None
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_lowerCamelCase : Dict = ids_tensor([self.batch_size] , self.num_choices)
_lowerCamelCase : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase_ ( self) -> Dict:
return NystromformerConfig(
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=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> List[str]:
_lowerCamelCase : str = NystromformerModel(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> List[Any]:
_lowerCamelCase : Union[str, Any] = NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> Any:
_lowerCamelCase : List[str] = NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Dict = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , start_positions=SCREAMING_SNAKE_CASE , end_positions=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> Optional[Any]:
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : int = NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : str = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> int:
_lowerCamelCase : Tuple = self.num_labels
_lowerCamelCase : int = NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> List[str]:
_lowerCamelCase : int = self.num_choices
_lowerCamelCase : Any = NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Optional[Any] = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_lowerCamelCase : List[str] = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_lowerCamelCase : Dict = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_lowerCamelCase : str = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def UpperCamelCase_ ( self) -> List[Any]:
_lowerCamelCase : Tuple = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Union[str, Any] = config_and_inputs
_lowerCamelCase : Optional[int] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowercase__ ( A_ ,A_ ,unittest.TestCase ):
__UpperCAmelCase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
def UpperCamelCase_ ( self) -> List[str]:
_lowerCamelCase : Union[str, Any] = NystromformerModelTester(self)
_lowerCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , hidden_size=37)
def UpperCamelCase_ ( self) -> str:
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self) -> Any:
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Tuple:
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCamelCase : int = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Union[str, Any]:
_lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Optional[int]:
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> List[str]:
_lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> str:
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE)
@slow
def UpperCamelCase_ ( self) -> Optional[int]:
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE)
self.assertIsNotNone(SCREAMING_SNAKE_CASE)
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Union[str, Any] = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""")
_lowerCamelCase : Union[str, Any] = torch.tensor([[0, 1, 2, 3, 4, 5]])
with torch.no_grad():
_lowerCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE)[0]
_lowerCamelCase : Any = torch.Size((1, 6, 768))
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = torch.tensor(
[[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4))
@slow
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Optional[Any] = """the [MASK] of Belgium is Brussels"""
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""")
_lowerCamelCase : List[Any] = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""")
_lowerCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE , return_tensors="""pt""")
with torch.no_grad():
_lowerCamelCase : Tuple = model(encoding.input_ids).logits
_lowerCamelCase : str = token_logits[:, 2, :].argmax(-1)[0]
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE) , """capital""")
| 88 |
from typing import List
from .keymap import KEYMAP, get_character
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : List[Any] ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
def A__ ( *SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
"""simple docstring"""
def decorator(SCREAMING_SNAKE_CASE_ : Any ):
_UpperCAmelCase = getattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , [] )
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , '''handle_key''' , SCREAMING_SNAKE_CASE_ )
return func
return decorator
class __UpperCamelCase ( A__ ):
def __new__( cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = super().__new__(cls , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if not hasattr(_UpperCamelCase , '''key_handler''' ):
setattr(_UpperCamelCase , '''key_handler''' , {} )
setattr(_UpperCamelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCAmelCase = getattr(_UpperCamelCase , '''handle_key''' , [] )
for key in handled_keys:
_UpperCAmelCase = value
return new_cls
@staticmethod
def UpperCamelCase( cls ):
_UpperCAmelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCAmelCase = ord(_UpperCamelCase )
_UpperCAmelCase = cls.key_handler.get(_UpperCamelCase )
if handler:
_UpperCAmelCase = char
return handler(cls )
else:
return None
def A__ ( cls : Union[str, Any] ) -> Any:
"""simple docstring"""
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() ) | 32 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCamelCase( unittest.TestCase ):
def __init__( self, lowerCamelCase, lowerCamelCase=7, lowerCamelCase=3, lowerCamelCase=18, lowerCamelCase=30, lowerCamelCase=4_00, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=True, ) -> Optional[Any]:
"""simple docstring"""
_lowercase : Tuple = size if size is not None else {'height': 18, 'width': 18}
_lowercase : Optional[int] = parent
_lowercase : Optional[Any] = batch_size
_lowercase : Any = num_channels
_lowercase : Optional[int] = image_size
_lowercase : List[Any] = min_resolution
_lowercase : Optional[int] = max_resolution
_lowercase : Optional[int] = do_resize
_lowercase : str = size
_lowercase : Optional[int] = apply_ocr
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : Optional[Any] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = LayoutLMvaImageProcessingTester(self)
@property
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCamelCase, 'do_resize'))
self.assertTrue(hasattr(lowerCamelCase, 'size'))
self.assertTrue(hasattr(lowerCamelCase, 'apply_ocr'))
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {'height': 18, 'width': 18})
_lowercase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {'height': 42, 'width': 42})
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
pass
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_lowercase : int = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, Image.Image)
# Test not batched input
_lowercase : Dict = image_processing(image_inputs[0], return_tensors='pt')
self.assertEqual(
encoding.pixel_values.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
self.assertIsInstance(encoding.words, lowerCamelCase)
self.assertIsInstance(encoding.boxes, lowerCamelCase)
# Test batched
_lowercase : Optional[Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_lowercase : Dict = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, numpify=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, np.ndarray)
# Test not batched input
_lowercase : Union[str, Any] = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
# Test batched
_lowercase : Tuple = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_lowercase : Any = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, torchify=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, torch.Tensor)
# Test not batched input
_lowercase : Optional[Any] = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
# Test batched
_lowercase : Union[str, Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : int = LayoutLMvaImageProcessor()
from datasets import load_dataset
_lowercase : List[str] = load_dataset('hf-internal-testing/fixtures_docvqa', split='test')
_lowercase : Tuple = Image.open(ds[0]['file']).convert('RGB')
_lowercase : Dict = image_processing(lowerCamelCase, return_tensors='pt')
self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24))
self.assertEqual(len(encoding.words), len(encoding.boxes))
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
_lowercase : Union[str, Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
_lowercase : Optional[int] = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words, lowerCamelCase)
self.assertListEqual(encoding.boxes, lowerCamelCase)
# with apply_OCR = False
_lowercase : int = LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase)
_lowercase : int = image_processing(lowerCamelCase, return_tensors='pt')
self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24))
| 89 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=7 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=99 , _UpperCamelCase=24 , _UpperCamelCase=2 , _UpperCamelCase=6 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=16 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=3 , _UpperCamelCase=None , _UpperCamelCase=1000 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def UpperCamelCase( self ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
_UpperCAmelCase = model(_UpperCamelCase , bbox=_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 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase( self ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
__A : Dict = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__A : Optional[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = False
__A : Optional[int] = False
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return True
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def UpperCamelCase( self ):
self.config_tester.run_common_tests()
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def UpperCamelCase( self ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def UpperCamelCase( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''' ).to(_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=_UpperCamelCase )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
_UpperCAmelCase = torch.Size([1, 2, 768] )
_UpperCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) ) | 32 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['''XLNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['''XLNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLNetForMultipleChoice''',
'''XLNetForQuestionAnswering''',
'''XLNetForQuestionAnsweringSimple''',
'''XLNetForSequenceClassification''',
'''XLNetForTokenClassification''',
'''XLNetLMHeadModel''',
'''XLNetModel''',
'''XLNetPreTrainedModel''',
'''load_tf_weights_in_xlnet''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLNetForMultipleChoice''',
'''TFXLNetForQuestionAnsweringSimple''',
'''TFXLNetForSequenceClassification''',
'''TFXLNetForTokenClassification''',
'''TFXLNetLMHeadModel''',
'''TFXLNetMainLayer''',
'''TFXLNetModel''',
'''TFXLNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 90 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Tuple = """rwkv"""
__A : Any = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _UpperCamelCase=50277 , _UpperCamelCase=1024 , _UpperCamelCase=4096 , _UpperCamelCase=32 , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=1e-5 , _UpperCamelCase=0 , _UpperCamelCase=0 , _UpperCamelCase=6 , _UpperCamelCase=False , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
A = XLMRobertaModel.from_pretrained('xlm-roberta-base' )
A = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
A = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
A = torch.tensor(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A = model(A_ )['last_hidden_state'].detach()
self.assertEqual(output.shape ,A_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,A_ ,atol=1e-3 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
A = XLMRobertaModel.from_pretrained('xlm-roberta-large' )
A = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
A = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
A = torch.tensor(
[[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A = model(A_ )['last_hidden_state'].detach()
self.assertEqual(output.shape ,A_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] ,A_ ,atol=1e-3 ) ) | 91 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCamelCase_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCamelCase_ = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f'''{len(upper_files)} files contain uppercase characters:''')
print("""\n""".join(upper_files) + """\n""")
UpperCamelCase_ = [file for file in filepaths if """ """ in file]
if space_files:
print(f'''{len(space_files)} files contain space characters:''')
print("""\n""".join(space_files) + """\n""")
UpperCamelCase_ = [file for file in filepaths if """-""" in file]
if hyphen_files:
print(f'''{len(hyphen_files)} files contain hyphen characters:''')
print("""\n""".join(hyphen_files) + """\n""")
UpperCamelCase_ = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f'''{len(nodir_files)} files are not in a directory:''')
print("""\n""".join(nodir_files) + """\n""")
UpperCamelCase_ = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 92 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : Dict = """falcon"""
__A : Any = ["""past_key_values"""]
def __init__( self , _UpperCamelCase=65024 , _UpperCamelCase=4544 , _UpperCamelCase=32 , _UpperCamelCase=71 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=None , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=11 , _UpperCamelCase=11 , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop('''n_embed''' , _UpperCamelCase )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCAmelCase = alibi
_UpperCAmelCase = new_decoder_architecture
_UpperCAmelCase = multi_query # Ignored when new_decoder_architecture is True
_UpperCAmelCase = parallel_attn
_UpperCAmelCase = bias
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
@property
def UpperCamelCase( self ):
return self.hidden_size // self.num_attention_heads
@property
def UpperCamelCase( self ):
return not self.alibi | 32 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__A = """ def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
"""
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , 'models/bert/' ) )
lowerCAmelCase__ :List[Any] = self.transformer_dir
shutil.copy(
os.path.join(__UpperCAmelCase , 'src/transformers/models/bert/modeling_bert.py' ) , os.path.join(self.transformer_dir , 'models/bert/modeling_bert.py' ) , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = 'src/transformers'
shutil.rmtree(self.transformer_dir )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
lowerCAmelCase__ :List[Any] = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
lowerCAmelCase__ :Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
lowerCAmelCase__ :int = black.format_str(__UpperCAmelCase , mode=__UpperCAmelCase )
lowerCAmelCase__ :Any = os.path.join(self.transformer_dir , 'new_code.py' )
with open(__UpperCAmelCase , 'w' , newline='\n' ) as f:
f.write(__UpperCAmelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__UpperCAmelCase )
with open(__UpperCAmelCase , 'r' ) as f:
self.assertTrue(f.read() , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :str = check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , __UpperCAmelCase , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , re.sub('Bert' , 'TestModel' , __UpperCAmelCase ) , )
# Copy consistency with a really long name
lowerCAmelCase__ :Optional[int] = 'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}" , F"{long_class_name}LMPredictionHead" , re.sub('Bert' , __UpperCAmelCase , __UpperCAmelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , __UpperCAmelCase , overwrite_result=re.sub('Bert' , 'TestModel' , __UpperCAmelCase ) , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = check_copies.LOCALIZED_READMES['README_zh-hans.md']
lowerCAmelCase__ :Optional[int] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
' Luong, Quoc V. Le, Christopher D. Manning.'
)
lowerCAmelCase__ :Optional[int] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ :Any = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
' Christopher D. Manning 发布。\n'
)
lowerCAmelCase__ , lowerCAmelCase__ :Union[str, Any] = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
self.assertFalse(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(__UpperCAmelCase )
lowerCAmelCase__ :List[str] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
)
lowerCAmelCase__ :Dict = (
'1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ :Optional[Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ , lowerCAmelCase__ :int = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
# Check if the model link is synchronized.
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
| 93 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
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_table.py
SCREAMING_SNAKE_CASE = 'src/transformers'
SCREAMING_SNAKE_CASE = 'docs/source/en'
SCREAMING_SNAKE_CASE = '.'
def lowercase_ ( __A : List[Any] , __A : Dict , __A : int ) -> List[str]:
"""simple docstring"""
with open(__A , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase : Optional[Any] =f.readlines()
# Find the start prompt.
lowercase : Optional[int] =0
while not lines[start_index].startswith(__A ):
start_index += 1
start_index += 1
lowercase : str =start_index
while not lines[end_index].startswith(__A ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE = 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.
SCREAMING_SNAKE_CASE = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE = direct_transformers_import(TRANSFORMERS_PATH)
def lowercase_ ( __A : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Optional[int] =re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , __A )
return [m.group(0 ) for m in matches]
def lowercase_ ( __A : Tuple , __A : List[Any] ) -> int:
"""simple docstring"""
lowercase : int =2 if text == '''✅''' or text == '''❌''' else len(__A )
lowercase : int =(width - text_length) // 2
lowercase : Dict =width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowercase_ ( ) -> int:
"""simple docstring"""
lowercase : List[str] =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase : Union[str, Any] ={
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase : Dict ={name: config.replace('''Config''' , '''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
lowercase : Optional[int] =collections.defaultdict(__A )
lowercase : List[Any] =collections.defaultdict(__A )
lowercase : List[Any] =collections.defaultdict(__A )
lowercase : str =collections.defaultdict(__A )
lowercase : int =collections.defaultdict(__A )
# Let's lookup through all transformers object (once).
for attr_name in dir(__A ):
lowercase : int =None
if attr_name.endswith('''Tokenizer''' ):
lowercase : Optional[Any] =slow_tokenizers
lowercase : Dict =attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowercase : Optional[int] =fast_tokenizers
lowercase : Optional[Any] =attr_name[:-1_3]
elif _re_tf_models.match(__A ) is not None:
lowercase : Optional[Any] =tf_models
lowercase : Any =_re_tf_models.match(__A ).groups()[0]
elif _re_flax_models.match(__A ) is not None:
lowercase : Any =flax_models
lowercase : Optional[int] =_re_flax_models.match(__A ).groups()[0]
elif _re_pt_models.match(__A ) is not None:
lowercase : List[str] =pt_models
lowercase : Dict =_re_pt_models.match(__A ).groups()[0]
if lookup_dict is not None:
while len(__A ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase : Optional[int] =True
break
# Try again after removing the last word in the name
lowercase : List[str] =''''''.join(camel_case_split(__A )[:-1] )
# Let's build that table!
lowercase : List[Any] =list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase : Tuple =['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
lowercase : Optional[Any] =[len(__A ) + 2 for c in columns]
lowercase : int =max([len(__A ) for name in model_names] ) + 2
# Build the table per se
lowercase : Any ='''|''' + '''|'''.join([_center_text(__A , __A ) for c, w in zip(__A , __A )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowercase : Dict ={True: '''✅''', False: '''❌'''}
for name in model_names:
lowercase : Dict =model_name_to_prefix[name]
lowercase : Dict =[
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__A , __A ) for l, w in zip(__A , __A )] ) + "|\n"
return table
def lowercase_ ( __A : Any=False ) -> Optional[Any]:
"""simple docstring"""
lowercase , lowercase , lowercase , lowercase : Optional[Any] =_find_text_in_file(
filename=os.path.join(__A , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowercase : List[Any] =get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__A , '''index.md''' ) , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 94 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 0:
return False
_UpperCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ (__A ):
__magic_name__ = ''''''
__magic_name__ = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
__magic_name__ = None # compression type in fsspec. ex: "gzip"
__magic_name__ = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : List[Any] , lowerCAmelCase_ : str = "" , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[dict] = None , **lowerCAmelCase_ : List[str] ) -> List[Any]:
super().__init__(self , **lowerCAmelCase_ )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
UpperCAmelCase_ : Dict = 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 {}) , )
UpperCAmelCase_ : Optional[int] = os.path.basename(self.file.path.split("::" )[0] )
UpperCAmelCase_ : Any = (
self.compressed_name[: self.compressed_name.rindex("." )]
if "." in self.compressed_name
else self.compressed_name
)
UpperCAmelCase_ : Any = None
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[Any] , lowerCAmelCase_ : List[str] ) -> Any:
# compressed file paths are always relative to the archive root
return super()._strip_protocol(lowerCAmelCase_ ).lstrip("/" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[str]:
if self.dir_cache is None:
UpperCAmelCase_ : Optional[Any] = {**self.file.fs.info(self.file.path ), "name": self.uncompressed_name}
UpperCAmelCase_ : str = {f["name"]: f}
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : str ) -> Any:
return self.file.open().read()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : str = "rb" , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : int=None , **lowerCAmelCase_ : Tuple , ) -> Tuple:
UpperCAmelCase_ : List[str] = 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 UpperCamelCase_ (__A ):
__magic_name__ = '''bz2'''
__magic_name__ = '''bz2'''
__magic_name__ = '''.bz2'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''gzip'''
__magic_name__ = '''gzip'''
__magic_name__ = '''.gz'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''lz4'''
__magic_name__ = '''lz4'''
__magic_name__ = '''.lz4'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''xz'''
__magic_name__ = '''xz'''
__magic_name__ = '''.xz'''
class UpperCamelCase_ (__A ):
__magic_name__ = '''zstd'''
__magic_name__ = '''zstd'''
__magic_name__ = '''.zst'''
def __init__( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : str = "rb" , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[dict] = None , lowerCAmelCase_ : int = DEFAULT_BLOCK_SIZE , **lowerCAmelCase_ : List[Any] , ) -> Dict:
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
UpperCAmelCase_ : Optional[Any] = self.file.__enter__
class UpperCamelCase_ :
def __init__( self : Tuple , lowerCAmelCase_ : List[Any] ) -> List[Any]:
UpperCAmelCase_ : Optional[int] = file_
def __enter__( self : Tuple ) -> List[Any]:
self._file.__enter__()
return self
def __exit__( self : Union[str, Any] , *lowerCAmelCase_ : int , **lowerCAmelCase_ : int ) -> Optional[int]:
self._file.__exit__(*lowerCAmelCase_ , **lowerCAmelCase_ )
def __iter__( self : Optional[int] ) -> int:
return iter(self._file )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return next(self._file )
def __getattr__( self : Optional[int] , lowerCAmelCase_ : Optional[Any] ) -> List[Any]:
return getattr(self._file , lowerCAmelCase_ )
def fixed_enter(*lowerCAmelCase_ : int , **lowerCAmelCase_ : Optional[Any] ):
return WrappedFile(_enter(*lowerCAmelCase_ , **lowerCAmelCase_ ) )
UpperCAmelCase_ : List[Any] = fixed_enter
| 95 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__A : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__A : ClassVar[Features] = Features({} )
__A : str = "text"
@property
def UpperCamelCase( self ):
return {self.text_column: "text"} | 32 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowerCamelCase = logging.get_logger(__name__)
def a ( __UpperCAmelCase : List[str] ) -> List[List[ImageInput]]:
if isinstance(__UpperCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__UpperCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__UpperCAmelCase ):
return [[videos]]
raise ValueError(f'Could not make batched video from {videos}' )
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = ["pixel_values"]
def __init__( self : str , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_5_5 , __snake_case : bool = True , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , **__snake_case : List[str] , ) -> None:
super().__init__(**__snake_case )
__magic_name__: Tuple = size if size is not None else {"""shortest_edge""": 2_2_4}
__magic_name__: str = get_size_dict(__snake_case , default_to_square=__snake_case )
__magic_name__: Optional[int] = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
__magic_name__: str = get_size_dict(__snake_case , param_name="""crop_size""" )
__magic_name__: List[str] = do_resize
__magic_name__: Optional[int] = size
__magic_name__: Dict = do_center_crop
__magic_name__: Any = crop_size
__magic_name__: List[str] = resample
__magic_name__: str = do_rescale
__magic_name__: int = rescale_factor
__magic_name__: List[str] = do_normalize
__magic_name__: Any = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__magic_name__: List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self : List[Any] , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> np.ndarray:
__magic_name__: Optional[Any] = get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" in size:
__magic_name__: Tuple = get_resize_output_image_size(__snake_case , size["""shortest_edge"""] , default_to_square=__snake_case )
elif "height" in size and "width" in size:
__magic_name__: Optional[Any] = (size["""height"""], size["""width"""])
else:
raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase__ ( self : List[Any] , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[int] , ) -> np.ndarray:
__magic_name__: str = get_size_dict(__snake_case )
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(__snake_case , size=(size["""height"""], size["""width"""]) , data_format=__snake_case , **__snake_case )
def lowerCamelCase__ ( self : List[Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> Union[str, Any]:
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase__ ( self : str , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Any , ) -> np.ndarray:
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase__ ( self : Union[str, Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_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.
__magic_name__: int = to_numpy_array(__snake_case )
if do_resize:
__magic_name__: Union[str, Any] = self.resize(image=__snake_case , size=__snake_case , resample=__snake_case )
if do_center_crop:
__magic_name__: str = self.center_crop(__snake_case , size=__snake_case )
if do_rescale:
__magic_name__: str = self.rescale(image=__snake_case , scale=__snake_case )
if do_normalize:
__magic_name__: List[Any] = self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case )
__magic_name__: str = to_channel_dimension_format(__snake_case , __snake_case )
return image
def lowerCamelCase__ ( self : str , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[str, TensorType]] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : int , ) -> PIL.Image.Image:
__magic_name__: List[Any] = do_resize if do_resize is not None else self.do_resize
__magic_name__: List[str] = resample if resample is not None else self.resample
__magic_name__: Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
__magic_name__: Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
__magic_name__: Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
__magic_name__: Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
__magic_name__: Dict = image_mean if image_mean is not None else self.image_mean
__magic_name__: List[str] = image_std if image_std is not None else self.image_std
__magic_name__: List[str] = size if size is not None else self.size
__magic_name__: Tuple = get_size_dict(__snake_case , default_to_square=__snake_case )
__magic_name__: Optional[Any] = crop_size if crop_size is not None else self.crop_size
__magic_name__: List[str] = get_size_dict(__snake_case , param_name="""crop_size""" )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
__magic_name__: Tuple = make_batched(__snake_case )
__magic_name__: int = [
[
self._preprocess_image(
image=__snake_case , do_resize=__snake_case , size=__snake_case , resample=__snake_case , do_center_crop=__snake_case , crop_size=__snake_case , do_rescale=__snake_case , rescale_factor=__snake_case , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , data_format=__snake_case , )
for img in video
]
for video in videos
]
__magic_name__: Tuple = {"""pixel_values""": videos}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 96 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"vocab_file": "spiece.model"}
UpperCAmelCase_ = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
}
}
# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ = {
"t5-small": 5_12,
"t5-base": 5_12,
"t5-large": 5_12,
"t5-3b": 5_12,
"t5-11b": 5_12,
}
UpperCAmelCase_ = "▁"
class __UpperCamelCase ( A__ ):
__A : Any = VOCAB_FILES_NAMES
__A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , _UpperCamelCase , _UpperCamelCase="</s>" , _UpperCamelCase="<unk>" , _UpperCamelCase="<pad>" , _UpperCamelCase=100 , _UpperCamelCase=None , _UpperCamelCase = None , _UpperCamelCase=True , **_UpperCamelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase = len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) ) , _UpperCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
_UpperCAmelCase = legacy
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , extra_ids=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_UpperCamelCase , **_UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = extra_ids
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCamelCase )
@staticmethod
def UpperCamelCase( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
_UpperCAmelCase = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCamelCase , )
return max_model_length
@property
def UpperCamelCase( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCamelCase( self ):
_UpperCAmelCase = {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_UpperCamelCase )) + [1]
return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]
def UpperCamelCase( self ):
return list(
set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCamelCase( self ):
return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCamelCase( self , _UpperCamelCase ):
if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase = self._add_eos_if_not_present(_UpperCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _UpperCamelCase ):
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
_UpperCAmelCase = SPIECE_UNDERLINE + text.replace(_UpperCamelCase , ''' ''' )
return super().tokenize(_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase , **_UpperCamelCase ):
if not self.legacy:
_UpperCAmelCase = text.startswith(_UpperCamelCase )
if is_first:
_UpperCAmelCase = text[1:]
_UpperCAmelCase = self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
_UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCamelCase( self , _UpperCamelCase ):
if token.startswith('''<extra_id_''' ):
_UpperCAmelCase = re.match(R'''<extra_id_(\d+)>''' , _UpperCamelCase )
_UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase ):
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(_UpperCamelCase )
else:
_UpperCAmelCase = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCamelCase ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(_UpperCamelCase )
_UpperCAmelCase = False
out_string += self.sp_model.decode(_UpperCamelCase )
return out_string.strip()
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase = None ):
if not os.path.isdir(_UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase )
return (out_vocab_file,) | 32 | 0 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
lowercase_ = 0
lowercase_ = str(snake_case__ )
while len(snake_case__ ) != 1:
lowercase_ = [int(snake_case__ ) for i in num_string]
lowercase_ = 1
for i in range(0 , len(snake_case__ ) ):
total *= numbers[i]
lowercase_ = str(snake_case__ )
steps += 1
return steps
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
lowercase_ = 0
lowercase_ = str(snake_case__ )
while len(snake_case__ ) != 1:
lowercase_ = [int(snake_case__ ) for i in num_string]
lowercase_ = 0
for i in range(0 , len(snake_case__ ) ):
total += numbers[i]
lowercase_ = str(snake_case__ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 97 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
_snake_case : Dict = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : int=0 ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = np.random.RandomState(lowerCAmelCase__ )
_UpperCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def snake_case__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_UpperCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_UpperCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_UpperCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : Dict ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_UpperCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_UpperCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = pipe(**lowerCAmelCase__ ).images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
_UpperCamelCase = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = 3 * [inputs['''prompt''']]
# forward
_UpperCamelCase = pipe(**lowerCAmelCase__ )
_UpperCamelCase = output.images[0, -3:, -3:, -1]
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = 3 * [inputs.pop('''prompt''' )]
_UpperCamelCase = pipe.tokenizer(
lowerCAmelCase__ , padding='''max_length''' , max_length=pipe.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors='''np''' , )
_UpperCamelCase = text_inputs['''input_ids''']
_UpperCamelCase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
_UpperCamelCase = prompt_embeds
# forward
_UpperCamelCase = pipe(**lowerCAmelCase__ )
_UpperCamelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def snake_case__ ( self : Tuple ) -> int:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = 3 * ['''this is a negative prompt''']
_UpperCamelCase = negative_prompt
_UpperCamelCase = 3 * [inputs['''prompt''']]
# forward
_UpperCamelCase = pipe(**lowerCAmelCase__ )
_UpperCamelCase = output.images[0, -3:, -3:, -1]
_UpperCamelCase = self.get_dummy_inputs()
_UpperCamelCase = 3 * [inputs.pop('''prompt''' )]
_UpperCamelCase = []
for p in [prompt, negative_prompt]:
_UpperCamelCase = pipe.tokenizer(
lowerCAmelCase__ , padding='''max_length''' , max_length=pipe.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors='''np''' , )
_UpperCamelCase = text_inputs['''input_ids''']
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
_UpperCamelCase , _UpperCamelCase = embeds
# forward
_UpperCamelCase = pipe(**lowerCAmelCase__ )
_UpperCamelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def snake_case__ ( self : Any ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case__ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = ort.SessionOptions()
_UpperCamelCase = False
return options
def snake_case__ ( self : Any ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = '''A painting of a squirrel eating a burger'''
np.random.seed(0 )
_UpperCamelCase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type='''np''' )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : str ) -> Dict:
'''simple docstring'''
_UpperCamelCase = DDIMScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = '''open neural network exchange'''
_UpperCamelCase = np.random.RandomState(0 )
_UpperCamelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase__ , output_type='''np''' )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = '''open neural network exchange'''
_UpperCamelCase = np.random.RandomState(0 )
_UpperCamelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase__ , output_type='''np''' )
_UpperCamelCase = output.images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self : List[Any] ) -> Any:
'''simple docstring'''
_UpperCamelCase = 0
def test_callback_fn(lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : np.ndarray ) -> None:
_UpperCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
_UpperCamelCase = latents[0, -3:, -3:, -1]
_UpperCamelCase = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
_UpperCamelCase = latents[0, -3:, -3:, -1]
_UpperCamelCase = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
_UpperCamelCase = False
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase = '''Andromeda galaxy in a bottle'''
_UpperCamelCase = np.random.RandomState(0 )
pipe(
prompt=lowerCAmelCase__ , num_inference_steps=5 , guidance_scale=7.5 , generator=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def snake_case__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ )
assert pipe.safety_checker is None
_UpperCamelCase = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowerCAmelCase__ )
_UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCamelCase = pipe('''example prompt''' , num_inference_steps=2 ).images[0]
assert image is not None
| 98 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 32 | 0 |
def a (lowerCAmelCase__ ):
if not numbers:
return 0
if not isinstance(lowerCAmelCase__ , (list, tuple) ) or not all(
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
__a = __a = __a = numbers[0]
for i in range(1 , len(lowerCAmelCase__ ) ):
# update the maximum and minimum subarray products
__a = numbers[i]
if number < 0:
__a , __a = min_till_now, max_till_now
__a = max(lowerCAmelCase__ , max_till_now * number )
__a = min(lowerCAmelCase__ , min_till_now * number )
# update the maximum product found till now
__a = max(lowerCAmelCase__ , lowerCAmelCase__ )
return max_prod
| 99 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
def __snake_case ( lowerCAmelCase_ ) -> int:
SCREAMING_SNAKE_CASE__ = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __snake_case ( lowerCAmelCase_ = 1_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
for i in range(2 , max_n + 1 ):
SCREAMING_SNAKE_CASE__ = pre_numerator
SCREAMING_SNAKE_CASE__ = 2 * i // 3 if i % 3 == 0 else 1
SCREAMING_SNAKE_CASE__ = cur_numerator
SCREAMING_SNAKE_CASE__ = e_cont * pre_numerator + temp
return sum_digits(lowerCAmelCase_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 100 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( A__ , unittest.TestCase ):
__A : Any = DanceDiffusionPipeline
__A : Any = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__A : Tuple = PipelineTesterMixin.required_optional_params - {
"""callback""",
"""latents""",
"""callback_steps""",
"""output_type""",
"""num_images_per_prompt""",
}
__A : Tuple = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__A : List[str] = False
__A : str = False
def UpperCamelCase( self ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_UpperCamelCase , use_timestep_embedding=_UpperCamelCase , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
_UpperCAmelCase = IPNDMScheduler()
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=0 ):
if str(_UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(_UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
_UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def UpperCamelCase( self ):
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = DanceDiffusionPipeline(**_UpperCamelCase )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(_UpperCamelCase )
_UpperCAmelCase = pipe(**_UpperCamelCase )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_local()
@skip_mps
def UpperCamelCase( self ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase( self ):
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase( self ):
return super().test_attention_slicing_forward_pass()
def UpperCamelCase( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase( self ):
_UpperCAmelCase = torch_device
_UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(generator=_UpperCamelCase , num_inference_steps=100 , audio_length_in_s=4.096 )
_UpperCAmelCase = output.audios
_UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2 | 32 | 0 |
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